In this episode, Camilla shares a founder journey shaped long before robotics.

She grew up as a competitive alpine skier, leaving home early to train at elite level. Discipline, pressure, and repetition defined her daily life. Losing was normal. Learning how to reset and keep going became second nature.

After an injury ended her professional sports career, that same intensity moved into engineering. Camilla talks about discovering robotics through building, not theory. Working on factory floors, programming robots across vendors, and seeing firsthand how slow and fragmented industrial automation really is.

We talk about how Forgis came to life at ETH Zurich. Why hardware is not the bottleneck in factories. Why software fragmentation is. And how Forgis sits on top of existing systems as a hardware-agnostic, edge-based AI layer that upgrades how factories operate instead of tearing them apart.

A great conversation about discipline, switching paths, and why Europe’s manufacturing future depends on intelligence, not replacement.

In this episode, Stephen shares his path from growing up in Wales to spending a decade at Imperial, a postdoc at Berkeley, and eventually founding Neuracore. Not because he wanted to be a startup founder, but because he kept running into the same problem again and again: every robotics team rebuilding the same infrastructure from scratch.

We talk about what actually slows robotics teams down, why data pipelines matter more than clever algorithms, and how Neuracore aims to become the infrastructure layer that lets teams focus on deployment instead of plumbing.

Stephen also reflects on imposter syndrome, work ethic, moving between academia and industry, and why Europe can and should build its own robotics infrastructure instead of copying Silicon Valley playbooks.

A very honest conversation about building foundations, not hype, and why scaling robotics is mostly about removing friction.

In this episode, Steve shares a rare founder journey that starts far from robotics. From studying physics at Peking University, struggling to stand out, and rebuilding confidence through sheer consistency, to a PhD at Columbia and an early failed startup built out of love for his dog. A detour that taught him the cost of building without a business model.

We talk about his path through Cruise, NVIDIA, and NIO, where he led large-scale simulation efforts for autonomous driving. Steve explains how those years shaped his conviction that simulation, data quality, and evaluation are the real bottlenecks in physical AI.

He then breaks down how Lightwheel AI came to life. Why sim-ready assets matter more than solvers. How synthetic data actually closes the sim-to-real gap. And why robotics teams hit a ceiling without proper evaluation and scaling infrastructure.

A deep conversation about resilience, delayed gratification, and why the hardest part of building is often unlearning what made you successful before.

We talk about growing up in India, building his first autonomous vehicle on a $280 budget after being rejected from a CMU program, and how that failure pushed him toward Berkeley, Stanford, and eventually NVIDIA Research. Animesh shares why he avoided computer science early on, what drew him to mechatronics, and how curiosity rather than planning shaped his entire career.

ORBIT and Isaac Lab, why simulation is now the backbone of robot learning, and how world models, reinforcement learning, and foundation models are lowering the barrier for people outside robotics to build real systems.

Animesh explains why he believes the most important robotics breakthroughs will come from people who are still in high school today.

A deeply personal conversation about grit, risk, redefining success, and why chasing interesting problems beats chasing money.

Marco shares his journey from a middle-class upbringing with no academic role models to becoming a leading figure in applied AI for manufacturing. We talk about discovering computer science through a single physics teacher, why he almost went to vocational school, and how a mix of personal drive and mentors shaped his path.

We spend some time on his years between academia and industry, what he learned working in high-pressure startups, and why real innovation happens when theory meets factory floors. Marco explains how Germany still leads in fundamental AI research but is at risk of losing the race when it comes to turning research into scalable industrial products.

A conversation about explainable AI, robotics in production, and why Europe has only a small window left to turn Physical AI into a competitive advantage.

In this episode, Stefan shares how his path into robotics did not start with founding ambitions, but with curiosity for how machines work. From studying mechanical engineering at TUM to hands-on autonomous driving research at BMW and years at Fraunhofer IPA, his motivation was always the same: building systems that work in the real world, not just on paper.

We talk about the transition from research to entrepreneurship, why understanding the market matters more than perfect technology, and how NODE Robotics emerged as a Fraunhofer spin-off with real customers from day one. Stefan explains why many mobile robot projects fail at scale, how software complexity is the real bottleneck, and why modular, hardware-agnostic autonomy is critical for industrial adoption.

The conversation also looks into decision-making under uncertainty, the risks of staying too technology-driven, and why taking action beats waiting for perfect information. An honest reflection at building deep tech companies in Europe, navigating industrial customers, and turning applied research into a global product.

A grounded conversation about ambition, uncertainty, and what it really takes to scale robotics beyond pilots.

Q: What problem are we solving and why is now the right time?

• We solve a specific pain point (e.g. labor shortage, high manual costs, safety issue) in [industry], backed by data (e.g. X% productivity loss).

• Our timing is urgent: market drivers like labor shortages and improving tech (cheaper sensors, powerful AI) have created unprecedented demand for automation.

• Early validation: pilot customers report [quantified benefit] (e.g. 30% fewer errors, 2× throughput) proving real ROI.

In our pitch, we clearly define the user pain and quantify it. For example, with manufacturing labor shortages rising, our robot automates [task] to deliver cost savings and throughput gains. Robotics is hitting a tipping point: sensors are now inexpensive and AI is mature, meaning solutions that were impractical years ago are now feasiblequbit.capital. We tie the solution to a measurable impact (e.g. $ saved per shift), and cite early pilot feedback that customers see real value. This shows investors the problem is acute, the need is validated, and the timing (technology and market) is perfect.

Q: What is our solution and how does it uniquely address the problem?

• Our product is a [robot/AI system] that [brief description of how it works]. It uses proprietary [algorithms, hardware design, sensors] tailored to [problem].

• We highlight differentiators: e.g. modular design for easy scale, unique AI model trained on real data, or specialized end-effector.

• We emphasize benefits: faster ROI (e.g. payback in 6 months), higher accuracy, reduced downtime, etc., compared to existing options.

We pitch the solution as both technically novel and customer-ready. For instance, our robot integrates a custom AI vision system that reduces error rates by X%, and is designed to plug into standard factory workflows. We stress our proprietary edge – for example, a new control algorithm or unique data-collection that competitors lack – which translates to higher performance and defensibility. Investors expect not only a good idea but evidence it works in practice: we demonstrate a working prototype (even if just at TRL6/7) and explain why our approach is not just incremental. By comparing to current “work-arounds” (e.g. manual labor or legacy automation), we show clear ROI and explain why our tech is a big leap forward, giving confidence in our solution’s viability.

Market

Q: Who are our target customers and how big is the market?

• We define a large, growing market: e.g. global robotics market projected ~$72B in 2025 to ~$151B by 2030 (16% CAGR). Our initial segment (e.g. “industrial picking robots” or “healthcare service bots”) is a multi-billion-dollar slice of that.

• Ideal customers: [specific profile, e.g. mid-to-large manufacturers/warehouses/hospitals] who share the pain point. We list personas or job titles (e.g. Plant Manager, CTO of X industry) to be concrete.

• We cite credible data: industry reports, analyst projections, or analogous market growth figures.

We show investors the total addressable opportunity is venture-scale. For example, if we target quality inspection robots, we might say that segment is worth $X billion (citing industry sources) out of a $Y billion global automation market. We emphasize that even capturing a small percentage (SAM/SOM) leads to a high-return business. By tying our TAM/SAM figures to reputable research (e.g. Reuters, IDC, or NASDAQ reports) and discussing adjacent trends (like labor shortages driving adoptionqubit.capital), we prove the vision. We also explain how we will incrementally expand: starting with one vertical and plan to move into others (e.g. adding [related use case] next) to show a roadmap for market growth.

Q: How have we validated market demand?

• We’ve engaged early customers via pilots/POCs and collected feedback/LOIs. E.g. “We ran a 3-month pilot with [Company], which resulted in [outcome] and a signed letter of intent.”

• We show any metrics from trials: performance gains, user satisfaction, or contingent commitments (non-binding LOIs, MOU) from target clients.

• We highlight tractions like number of demos given, pilot partners, and any small early sales or pending contracts.

Investors want evidence that real customers want this. We share concrete validation: e.g. a major XYZ manufacturer tested our system and agreed to a follow-up pilot (validating demand)qubit.capital. Even if revenues are small, pilots (especially paid ones) count as traction. We quantify the benefits seen in pilots (e.g. “50% error reduction”), tying back to ROI, which makes the market case stronger. We also mention industry awards or press if any, but focus on customer interest. By documenting customer engagement and willingness to pay or pilot, we demonstrate that the market we described is real and eager for our solution.

Technology & IP

Q: What is unique about our technology and what gives us defensibility?

• We describe the core tech (e.g. proprietary AI model, custom robotics platform). We highlight why it’s novel: e.g. new algorithm, advanced sensor fusion, modular architecture.

• Emphasize scale and reliability: our design is modular for volume manufacturing and cloud-connected for continual learning. We note that only a few teams have our combination of skills and access.

• Mention performance advantage: cite metrics or test results (e.g. 95% detection accuracy vs 70% for nearest alternative). This shows not just a concept but a better-performing solution.

The goal is to convince investors this isn’t a commodity solution. For example, we might say “Our computer vision AI was trained on 1M+ annotated images from live factory floors (a data asset no competitor has)”, or “Our robot’s mechanical design is 5× more durable, cutting maintenance costs.” We explain why these differences are hard to copy: perhaps it’s years of R&D, specialized know-how, or exclusive datasets. We may have filed patents or can show significant trade secrets (even pending patents) on key componentsqubit.capitalqubit.capital. By showing our engineering solves a “hard problem” (like autonomous grasping or real-time learning) and that we’ve already validated it in a realistic setting, investors see our defensibility. We also note any modular design – as experts recommend modular architectures for scalability – which means we can improve components without redesigning the whole system.

Q: What intellectual property or data moats do we have?

• We list any patents (filed/granted) or patents pending. If not yet filed, we outline a plan or unique element that we’ll protect.

• We point out proprietary data: e.g. unique annotated datasets, customer usage data for ML improvements, or specialized libraries. This can be a moat: “No one else has our dataset of [scenario]”.

• If relevant, mention trade secrets or exclusive partnerships (e.g. a supplier agreement that others can’t replicate).

Investors expect a clear moat. We’ll say, for instance, “We have 2 provisional patents on our control algorithm” or “We’ve built a unique library of 10,000 annotated medical images that we use to train our model, data that competitors lack”. From the Qubit report, we note that patent portfolios protect core innovation. If we’re still early, we commit to an IP strategy. Similarly, if our tech relies on hard-to-get data (like sensitive hospital data), that’s a moat once under NDA. The answer must reassure investors that copying us won’t be easy.

Business Model

Q: How do we make money (business model and revenue streams)?

• Primary model: e.g. direct sales of hardware (robots) + recurring revenue from software licenses or maintenance contracts. Outline pricing (e.g. a robot sells for $X, plus a Y% annual support fee).

• If applicable, mention service or SaaS aspects (for robotics often add-ons like analytics dashboard, spare parts, training services).

• We justify margins: robotics can be hardware-heavy, but we project [~Z%] gross margin by optimizing costs over time.

We must explain clearly how money flows in. For example, we might say: “We sell the robot hardware to customers with a 1–2 year payback period. Additionally, we offer a cloud analytics subscription (SaaS) for remote monitoring, which adds a recurring revenue stream and high margins.” If we use a recurring model (e.g. leasing or RaaS model), we explain the benefit of predictable revenue. We use unit-economics thinking: detail cost of goods (bill of materials, manufacturing) versus pricing, as advised. VCs look for a path to 40–60% gross margins in robotics, so we highlight how design choices and scale will get there (e.g. by moving from custom parts to mass production, reducing component costs, and adding software licensing). Overall, we present a credible revenue model with both one-time and recurring components, and show the business can scale profitably.

Q: What are our unit economics and margins?

• We show unit cost breakdown: e.g. “Each robot’s bill of materials is $A, plus $B for assembly. We sell at $C, giving an initial gross margin of D%, expected to improve as volume increases.”

• If relevant, include service costs: warranty, customer support, software hosting. Factor those into lifetime value (LTV).

• We project LTV vs CAC (if applicable): “We expect a 3:1 LTV:CAC by year 2.” If no formal CAC yet, describe expected sales cycle and cost.

For hardware startups, investors know margins may start low but improve with scale. We use data: for instance, from Qubit we note that Series A robotics should aim for >40% gross margins. So we explain our path: “Initially margins may be ~25% (custom builds), but by year 3 we expect >50% due to scaling production. Consumables and software upsells raise margins on each unit.” We also address unit economics explicitly as advised – showing we’ve calculated component costs, labor, overhead, warranty (even if estimates). This demonstrates financial rigor and that the model is grounded in reality. If it’s B2B, we mention payment terms (e.g. 30-60 days vs inventory holding) to show cash flow awareness.

Go-to-Market

Q: What is our go-to-market strategy and sales channels?

• We identify channels: direct sales, enterprise sales team, distribution partners, system integrators, etc. For example, “We will sell directly to large manufacturers via a small in-house team, and partner with regional integrators for mid-market.”

• Customer acquisition: targeted outreach at industry trade shows, pilot programs, inbound demos (leveraging industry publications). Possibly a digital marketing strategy for recurring software sales.

• Strategic partnerships: We may leverage alliances (e.g. collaborate with existing automation providers or platform partnerships). For example, distribution deals with [established distributor] to tap their network.

We present a clear plan for reaching customers. If we have letters of intent or pilot agreements, we mention that as part of GTM validation. For instance: “We’ve already signed an LOI with [Distributor/Integrator] who will resell our robot in [region]” or “We are in discussions with [Major Corp] to co-market the solution.” We highlight that robotics often needs field support, so we might have a hybrid model (e.g. our engineers do initial installs). The Qubit report suggests strategic partners help with market access and validation, so we mention any such relationships and how they will accelerate sales. In bullets, we succinctly list channels and any key partnerships. In the narrative, we explain how these channels match customer buying habits (e.g. “Industrial buyers trust existing vendor networks, so we plug into [OEM’s] channel”) and how that will drive early orders.

Q: Who are our initial customers or partners, and what traction have they provided?

• We name pilots, LOIs or early adopters: “We’re engaged with [Company A] to pilot our robot for 3 months (starting Q3) and have a term sheet for 10 units upon success.”

• Any early revenue or contract: “To date we have $X in bookings (or pilot contracts) from [Partner B].”

• Mention any endorsements: letters of support, advisory board members from target customers, or media interest.

This complements “Traction” by emphasizing sales pipeline and partnerships. If, for example, a healthcare system agreed to test our medical robot (even unpaid), that’s huge early validation. We quantify: “This pilot covers 3 clinics and is projected to save $Y/month.” We highlight if any contract was won on a proof-of-concept basis. This shows progress on the GTM front and that customers trust us enough to commit time/resources. Investors expect founders to line up the first customers and to have started the conversation; we demonstrate that clearly (e.g. “five LOIs covering a potential $500K annual spend”), which greatly de-risks our go-to-market.

Team

Q: Who are the founders and key team members, and why are we the right team?

• Brief bios: “Our team includes [Name, role] who was X at [relevant company/PhD from Y], [Name, role] with expertise in [field].” Emphasize track records, domain expertise, or unique accomplishments (even outside this company).

• Technical/industry mix: We note we have both strong engineers (robotics/AI) and business/ops people. Robotics investors expect multidisciplinary teams (mechanical, software, domain experts all covered).

• Advisors/mentors: Mention any well-known advisors from the target industry or serial entrepreneurs, which adds credibility.

We must sell ourselves as much as the idea. If founders have prior relevant exits or deep technical creds, we highlight that: for instance, “Our CTO led development of the [successful robotics product] at [BigCorp], and our CEO sold a startup in IoT.” This addresses the investor desire to see grit and relevant experience. We also acknowledge gaps: “We’ve already identified a senior sales hire and a mechanical engineer as our next recruits,” showing self-awareness. Importantly, we stress that all core team members are domain experts in some way, as investors look for domain knowledge. For instance, if building surgical robots, one founder might be a surgeon or have medical device experience. We use this section to portray confidence and capability to execute.

Q: What roles do we need to hire to complete our team?

• We outline immediate hires: e.g. “We plan to hire a senior Sales Director with industrial IoT experience, a hardware engineer for mass production design, and a software lead for cloud infrastructure.”

• We explain timing: these hires are planned post-funding to achieve our milestones (e.g. hire sales in month 3, engineers by month 6).

• We might note the network we have for recruiting (e.g. advisors helping with talent) or mention equity buffers for key hires.

Investors want to know if there are any critical gaps. In bullets we succinctly list the roles and why: for example, “VP of Sales – to close our pipeline; Applications Engineer – to support pilot deployments; DevOps lead – to scale our SaaS backend.” In the narrative, we note we’ll allocate part of the budget to hiring (as we mention in fundraising). We can also reassure by saying “We already have candidates in mind” or “We’ve had positive responses at job fairs”. This shows planning and readiness to scale the team as the company grows, which top-tier investors expect (they often ask “who are you missing?” in due diligence).

Traction & Metrics

Q: What traction or metrics can you share (customers, pilots, revenue, growth)?

• Concrete numbers: e.g. “Achieved $X in pilot contracts; built 3 demo units; on track for Y% MoM user growth.”

• Key metrics: If revenue exists, show growth over last quarters; if users or usage, show engagement rates or growth curves. Emphasize momentum.

• Customer interest: “We have N letters of intent totaling $M, and [big company] is an early adopter.”

VCs look for evidence of demand and execution. We present our most compelling evidence first. For an example: “Over the past 6 months, we secured 2 pilot agreements with enterprise customers and generated $20K in early revenue. Our MRR has grown 30% month-over-month.” If no revenue, we might show “20 companies on trial, N signed NDAs, and X happy quotes from beta users.” We cite guidance that the growth curve is a top signal. If we have financials, we mention burn vs revenue. If not, we highlight non-financial traction like product readiness, press, or user testimonials. This convinces investors we’re on a real growth trajectory.

Fundraising & Use of Funds

Q: How much are we raising and why?

• We state the round size and reason: e.g. “Raising $1.5M seed to reach MVP and initial market launch.”

• Justification: explain how this amount ties to milestones (product dev, hires, market entry). We follow the advice that funds should have clear uses, not be a random number. We outline a budget split (e.g. 40% engineering, 30% sales/marketing, 20% operations, 10% reserves).

• Current status: mention pre-money valuation or percent given (VCs usually expect 15–20% equity for such rounds.

We make a strong case for the round size. For example: “With $1M we can complete our pilot product and begin scaling manufacturing. We arrived at this by modeling our burn and estimating a 12-month runway to key milestones.” We note that angels/seeders usually take ~15–20% post-money, indicating our valuation. Investors want confidence that our ask is grounded; we mention any co-investors (if any) or convertible notes. We also state the proposed timeline: “We aim to close by [date] to hit [milestone] in QX.” This shows we have a plan and urgency.

Q: How will we use the funds and what milestones will this achieve?

• Milestones: List what the funding will achieve, tied to timeline (e.g. “Q3: finish prototype; Q4: complete beta test; Q1 next year: launch v1 product and onboard 3 pilot customers”).

• Use of funds: high-level breakdown (R&D, hiring, MKT/Sales, ops). For instance, “60% R&D to finish product, 20% sales/marketing to start pilots, 15% hiring key engineers, 5% legal/overhead.”

• Next raise: We identify what triggers the next round (e.g. hitting $X MRR or Y enterprise contracts) and planned runway (~18 months from this raise).

Top investors expect a milestone-driven plan We provide that: not just “we want to grow”, but specifics like “acquire 5 paying customers and reach $300K ARR to set us up for Series A”. We tie these to spending: e.g. “Hiring 2 engineers by month 6 enables product completion by month 9.” We ensure that these milestones will lead to significant valuation step-ups (echoing the Robotmascot advice that hitting goals should justify a 2–3× jump). This assures investors their money will create value, and clarifies how long their capital will last.

Q: What valuation and terms are we considering?

• We state the proposed pre/post-money valuation and equity offered (or cap on SAFE, etc.), aligned with market comparables or lead investor feedback.

• If we’ve done valuation calculations, we briefly justify: e.g. referencing comparables or revenue multiples.

• Mention if any special terms: e.g. “standard seed round, 1x non-participating liquidation preference.”

Investors will definitely ask this. We frame it with context: for example, “We’re targeting a $6M pre-money valuation, which implies about 20% equity to investors. This is in line with recent robotics seed deals and our current progress.” We may cite Robotmascot that early VCs expect ~15–20% for risk. We also show openness: “These terms are negotiable, but we believe this is fair given our team and traction.” If a lead VC is involved, we mention it, as it strengthens the ask. We ensure the terms sound standard so investors focus on the business, not get hung on unusual caps or preferences.

Risks & Mitigation

Q: What are the biggest risks for our business and how do we mitigate them?

• Technical risk: e.g. development delays, integration challenges. Mitigation: rigorous testing (as investors recommend proving prototype performance), iterative design, fallback plans (e.g. modular upgrades).

• Market risk: slow adoption or competition from incumbents. Mitigation: focus on niche with urgent need, build partnerships (as advised), and have a pivot-ready mindset. We validate demand via pilots to de-risk.

• Financial risk: high burn or funding gaps. Mitigation: careful budgeting, exploring non-dilutive funding (grants/SBIR), and aiming for efficient customer acquisition (high LTV:CAC).

• Regulatory/legal risk: compliance requirements (e.g. safety or data privacy). Mitigation: early engagement with regulators, plan for certifications (noting we’ll obtain any needed approvals), and legal counsel on IP.

We acknowledge risks candidly to build trust. For example, we might say “Scaling hardware has risks (supply chain, yield issues). We mitigate this by locking suppliers early, and the Qubit analysis recommends building out scaled manufacturing plans (partnering with a contract manufacturer).” On market risk, we note that we target problems with strong ROI to ensure adoption. We highlight any unique risk mitigations: e.g. if we have exclusive supply contracts, or if our software-first approach avoids some hardware pitfalls. We reference the Qubit and Robotmascot advice that investors will probe risks intensely, so we should show we’ve thought through them, not gloss over. This answer instills confidence that we can foresee and handle challenges.

Vision & Exit

Q: What is our long-term vision and exit strategy?

• Vision: articulate the big picture: e.g. “We envision [Company] becoming the platform for [industry] automation, expanding from our initial product into adjacent applications.” Emphasize multiplying impact (e.g. new product lines, new markets) rather than a single product.

• Exit potential: note possible acquirers (e.g. “Potential exits include major robotics/automation firms like [Company X, Y] or an IPO if we scale broadly.”). You can cite an example of a major acquisition (e.g. Amazon’s $775M Kiva deal) to illustrate exit scale.

• Highlight return targets: we plan for a 10×+ return, matching VC expectations.

This final question ties everything to investor returns. We share a compelling story: for instance, “5 years out, we’re a multi-product company serving all major manufacturing hubs. We aim to be the [‘X of robotics’]” or something vivid. We convey confidence but also realism: yes, large companies do acquire robotics startups (as with Kiva)qubit.capital. We say, “Given the size of our market (>$1B) and our defensible tech, we believe industry giants (like [name companies in space]) would be interested buyers, or we could pursue an IPO if the market allows.” We reiterate that our plan is aligned with the goal of delivering outsized returns (the Robotmascot piece reminds that VCs want at least 10× multiple). This shows that we’re thinking big and aligning our strategy with investor objectives.

Reddit has niche communities where tech enthusiasts gather. By sharing your startup on relevant subreddits, you can reach people interested in AI and robotics. Always check each subreddit’s rules before posting, as many have strict policies on self-promotion. Here are some key subreddits:

• r/MachineLearning – Premier machine learning subreddit (~3+ million members) focused on ML research, projects, and news. Direct promotional posts are not allowed on the main feed. Instead, mods provide a weekly “[D] Self-Promotion Thread” for personal projects, startups, etc.aiexpert.network. You should post your startup there, including details and perhaps a question to invite discussion. Outside the promo thread, you may share project updates (tagged [P]) if they contribute value (e.g. releasing open-source code). Etiquette: Be transparent that it’s your project and don’t spam. Engage with any feedback in comments. Link: reddit.com/r/MachineLearning – look for the latest “Self-Promotion” sticky thread.

• r/artificial (Artificial Intelligence) – Largest general AI subreddit (1M+ members) covering AI news, research, and startups. Self-promotion is permitted sparingly under a strict “10% rule” (no more than ~10% of your posts/comments should be self-promotional)reddit.com. In practice, this means you should be an active, helpful community member before plugging your startup. Pure ads or link-drops will likely be removed. Etiquette: Share your startup only if it’s genuinely interesting to AI folks (e.g. a new open-source tool or a compelling use-case) and frame it as “here’s something we built, would love feedback,” rather than a sales pitch. Link: reddit.com/r/artificial.

• r/robotics – Community of robotics enthusiasts (from hobbyists to professionals). It’s common to share personal robotics projects, demos, and news here. Self-promotion policy: Moderation is lighter if your post is on-topic and showcases tech or asks for feedback (avoid blatant marketing). Often, project posts are tagged as [Project] or similar. The subreddit occasionally hosts “Showcase” threads for community projects. Etiquette: When posting your robot or AI-driven hardware, include photos/videos and technical details. Emphasize what’s interesting (e.g. “Built a warehouse robot that uses AI for navigation – here’s a demo, let me know what you think!”). This invites discussion. Link: reddit.com/r/robotics (you can create a new post, just ensure it’s informative).

• r/startups – Large subreddit (1.8M members) for startup advice and sharing. No direct ads allowed on the main subreddit; instead they have a weekly “Share Your Startup” thread (usually pinned to the top) where founders can post a blurb about their startupnotifier.so. They also run weekly threads for feedback and specific topics. Etiquette: Only post your startup in those dedicated threads. Keep your blurb concise: explain what your startup does and include a clear ask (e.g. “looking for beta users” or “feedback welcome”). Engage by responding to any Redditors who comment on your share. Outside of those threads, you can ask startup-related questions on r/startups, but frame them generically (e.g. “How do I validate market X?” rather than “Please download my app”). Link: reddit.com/r/startups (check for Weekly “Share Your Startup” post).

Posting Tips: On Reddit, timing matters less than relevance and authenticity. Ensure you contribute positively to the community (answer others’ questions, join discussions) before and after promoting your own project – users check your history. When you do share, be straightforward about being a founder, and invite feedback or discussion. This transforms a self-promo into a community conversation, which is much better received.

Tech Newsletters & Email Digests

Tech newsletters can broadcast your startup to thousands of readers. Many accept submissions or tips about cool new projects, especially in AI/robotics. It’s often best to craft a short email pitch to the editors. Here are a few to consider:

• TLDR (Tech & Startups Newsletter) – A daily tech newsletter with byte-sized news and product highlights, read by a broad tech audience (engineers, founders, etc.). It often features interesting new tools or startups. While TLDR is curated, you can submit a story tip by emailing the team (or via their website form). Keep it very brief: one sentence on what your startup does and why it’s noteworthy. For example, mention a unique achievement (“Our drone startup just open-sourced a navigation algorithm”) or how it benefits the readers. Self-promotion policy: They are open to cool new tools (especially if free or open-source for readers to try). Alternatively, TLDR offers sponsorship slots where startups get a short feature – this is paid, but reaches ~1M+ readers (useful for launch). Link: tldr.tech – see the “Submit a link” or “Contact” page.

• Benedict Evans’ Newsletter – A weekly email by analyst Benedict Evans (~200k subscribers) covering “what mattered in tech this week”ben-evans.com. The content is usually big-picture analysis, but he sometimes links to notable industry news or exemplary products. There’s no formal submission process, but if your startup has newsworthy impact (e.g. significant funding round, or a demo pushing technological boundaries), you could send him a polite note via his website or Twitter. Emphasize the trend or problem your startup illustrates rather than just “please feature us.” (E.g. “Hi Benedict, we built a robot that automates warehouse inventory – an interesting example of AI + logistics – thought you might find it relevant.”) Additionally, his newsletter accepts sponsorsben-evans.com, which is another route to get in front of his audience (mostly tech execs and VCs). Link: ben-evans.com (see “About & Contact” for email).

• Tech and AI Digest Newsletters – Inside AI (from inside.com) and The Batch (deeplearning.ai’s weekly AI newsletter) are examples that focus on AI news and breakthroughs. These typically cover research and major product announcements. To get featured, frame your startup as news: e.g., publish a press release about a breakthrough or a funding announcement and send it as a tip. Inside AI has a tip submission form; The Batch might consider notable AI applications (especially if you have an academic angle or if an AI thought leader is involved). Link: inside.com/ai (has “Submit story” option).

• Robotics-specific Newsletters – e.g. The Robot Report (industry newsletter/blog for robotics) and ROBO Global Newsletter. These cater to robotics professionals and investors. Getting a mention usually requires a press release or a significant milestone (product launch, patent, partnership). You can contact their editors with a brief news pitch. Ensure it’s factual and highlights why your robot is innovative. Etiquette: avoid hype; tie your news to industry trends the newsletter follows (e.g. automation in healthcare robots).

Newsletter Pitching Tips: Keep your email very concise – newsletter curators sift through many submissions. Include a clear subject line (e.g. “Tip: AI startup using drones for crop monitoring”). In 2-3 sentences, cover: what your startup does, the news/hook (launch, new feature, etc.), and a link (press kit or blog post). Example: “Hi, I’d like to share FarmAI – a new startup using drones + AI to scan crops for disease. It’s like a ‘doctor for plants’, using computer vision to alert farmers early. We just opened our beta (free for farmers). If you find this interesting for TLDR, you can check it out: [link]. Thanks!” Align your tone with the newsletter’s style (informative, not overly salesy). Finally, be mindful of timing: send your pitch a few days before the newsletter’s issue is compiled (many weekly ones are finalized on weekends or Monday for a Tuesday send-out).

Product & Startup Launch Platforms

Launching on dedicated platforms can dramatically increase your visibility among early adopters, investors, and the press. Each platform has its own submission process and community norms. Here are the main ones:

• Product Hunt – The go-to platform for launching tech products. You create a listing (product name, tagline, images/media, and a first comment). The community of tech enthusiasts will upvote and comment. Posting rules/etiquette: Don’t explicitly ask for upvotes (on or off the platform) – Product Hunt penalizes thatproducthunt.com. Instead, in your maker comment, tell your story: why you built this, key features, and invite feedback (“Please let us know what you think” or “Would love your feedback!”). Be responsive on launch day: reply to every comment, thank people, and answer questions. Ideal timing: Launch at 12:01 AM Pacific Time to get a full day of exposureproducthunt.com. Tuesdays, Wednesdays, or Thursdays tend to have higher traffic (Mon can work too), but avoid major holidays or big competing launches. Link: producthunt.com – use the “Ship” or “Launch” feature to schedule your post if needed. Pro tip: Engage with the PH community before launching (follow others, comment on products) so you’re not posting in a vacuum.

• BetaList – A platform featuring pre-launch or early-stage startups to an audience of early adopters. Founders use it to gather beta users or mailing list signups. Submission process: You fill out a form with your startup name, one-liner, description, and link. There’s often a queue (free submission can take weeks; they offer paid expedited options). Posting rules: Your product should be new and not previously featuredbetalist.com. BetaList specifically wants tech startups (software or hardware) with either a coming-soon page or a recently launched betabetalist.com. Make sure you have a decent landing page – they require that it not be a generic template and that users can sign up or request accessbetalist.com. Etiquette: BetaList readers expect to be “early access” users, so consider offering them something (e.g. immediate invite, or a small discount) and mention it in your listing. Ideal timing: No specific day – but do it as early as possible in your launch cycle to start building an email list. BetaList will email you if you get featured. Link: betalist.com/submit (ensure you follow their guidelines to avoid rejection).

• Alternative Startup Directories – There are many other listing sites: BetaPage, Startup Buffer, Launching Next, KillerStartups, etc. These typically allow you to submit a short description of your startup. They vary in impact (often smaller audiences than Product Hunt or BetaList), but listing on them can boost your SEO and snag a few early users. Example: BetaPage is a community where new startups are upvoted (like a mini Product Hunt); Startup Buffer and Launching Next showcase “startup of the day” on their site/newsletter. Posting rules: Generally straightforward – fill out a form with your info. Many have free and paid options. Etiquette: Use your one-liner effectively to entice readers (“X is like Uber for Y”) and ensure your website is ready for any traffic. Ideal timing: Whenever – just be sure the info is up-to-date and that you’re ready to handle sign-ups or inquiries.

• Product Launch Communities – Outside official platforms, consider communities like Showcase pages or threads. For example, some tech forums (and subreddits like r/SideProject or r/InternetIsBeautiful) allow sharing new products. r/InternetIsBeautiful (16M members) specifically allows posting genuinely useful or unique websites – if your startup has a consumer-facing web app or demo, you can post there (just don’t oversell; let the product speak for itself). Hacker News (Show HN) is another (see Forums below). These aren’t “profiles” to create, but one-time launch posts that can drive significant traffic.

Launch Platform Tips: Prepare your materials in advance – screenshots, a promo video/gif, a crisp tagline, and a paragraph pitch. Consistency matters: you might use the same tagline across PH, BetaList, etc., but tailor the extended description to the audience. For instance, Product Hunt can be a bit playful or emoji-friendly in maker comments, whereas BetaList is more utilitarian (just what it is, and link to sign up). Also, if you get featured on one platform, leverage it on others (e.g., “#1 Product of the Day on Product Hunt” is social proof you can mention later on your website or in emails).

Online Forums & Communities

Beyond social media and launch sites, there are independent communities and forums where startup founders and tech professionals congregate. Participating in these can help you build an audience and get feedback. Tailor your approach to each community’s culture:

• Hacker News (Show HN) – An extremely influential tech forum (run by Y Combinator) where developers and founders share projects. Posts reach a highly technical audience. To promote your startup here, make a post titled “Show HN: [Your Product Name] – [One-line description]” when you have something launch-ready. In the post text, introduce your startup like speaking to peers: explain the problem you solve, how you built it, and avoid any marketing fluff (“HN will ignore anything that reads like an ad or PR”news.ycombinator.comdev.to). For example, mention the tech stack or a challenge you overcame – HN users love technical detail. Posting rules: Don’t ask people to visit or upvote in the title or comments. Also, no link stuffing – provide your product’s URL in the submission form and perhaps once in text if needed. HN’s guidelines explicitly forbid vote manipulation and overly promotional language. Etiquette: Stay around to answer questions! Engage politely with feedback or criticism; HN commenters can be frank. If someone is wrong about your product, respond with insight rather than defensiveness. Ideal timing: Many suggest posting in the morning US time on a weekday (to catch Silicon Valley and East Coast users) – roughly between 9 AM and 12 PM Pacificdev.to. However, great content can catch fire anytime. If your first post doesn’t get traction, HN allows re-submitting later (ideally with improvements) – just don’t spam it repeatedly.

• Indie Hackers – A forum and community for entrepreneurs, particularly those building bootstrapped startups and “building in public.” You can create a product page on IH and post updates (milestones, revenue figures, etc.), as well as participate in the forum discussions. Posting suggestions: Write an introductory post in the “Products” or “Launches” category detailing your journey or a milestone (e.g. “Launched my AI SaaS – here’s what I learned in 3 months of development.”). The tone should be conversational and honest – share both challenges and wins. Self-promotion is welcome as long as it comes with personal insights or questions for the community. For instance, instead of just “Check out our robot!”, you might post “We built a warehouse robot that cuts inventory counting time in half. AMA about the tech or our go-to-market – would love your feedback!” This invites engagement. Etiquette: Be supportive of others (comment on their posts, offer help). If you’re not an active member and just drop your launch post, it may get little attentionreddit.com. So try to build some presence. Also, IH has a “no trolling/harassment” policy – keep it positive and transparent. Ideal timing: IH has global users, but many posts do well in late afternoons U.S. time and weekends when founders are catching up on forums.

• AI Alignment Forum (and LessWrong) – A specialized forum for AI researchers and theorists (focused on AI safety, ethics, and alignment). This is not a place for product promotion in the usual sense. However, if your startup directly contributes to AI safety or you’ve written a technical paper/blog post about your approach, you could share that here to spark discussion. Posting rules: Posts are expected to have research value – e.g., a deep dive into your algorithm or a critique of AI approaches. Simple product announcements will be downvoted or removed. New users can post on LessWrong (the broader rationalist community) and if the content is strong, it might be cross-posted or promoted to the Alignment Forum by moderatorslesswrong.com. Etiquette: Extremely high standards – be prepared for insightful, sometimes tough, feedback. Don’t use marketing speak; use academic tone. Use this channel only if your startup’s mission aligns with advanced AI topics (for example, you’re working on explainable AI or a safety-critical robotic system and you want input on your methods).

• Other Niche Forums: Depending on your startup, there might be relevant communities: e.g. Robot Operating System (ROS) Discourse for ROS-based robots, Arduino Forums if you have a hardware DIY angle, or Kaggle Forums if it’s ML-heavy and you’re sharing a challenge/dataset. When posting in such places, focus on the technical aspects and community benefit (perhaps release a library or ask for testers). Always read the room – if the forum explicitly bans self-promo, find a way to share value (like a tutorial that incidentally uses your product, with disclosure).

Slack/Discord Communities & Professional Groups

Private communities on Slack and Discord, as well as groups on LinkedIn and Facebook, can be excellent for targeted promotion. The audiences here tend to be smaller but highly engaged. Always follow community rules – many have specific channels or days for promotions.

• “Startup Chat” Slack – A large invite-only Slack community for startup founders (with channels for #advice, #jobs, #feedback, etc.). Posting rules: Some startup Slacks require you to introduce yourself first in an #introductions channel. Do that – share who you are and what you’re working on (without a hard sell). For promotion, see if there’s a dedicated channel (many have something like #shameless-plugs or #launch). In that channel, you can post about your startup. Keep it short and engaging: explain the problem you solve and include a call-to-action (“We just opened our beta – would love for this community to try it out!”). Because it’s a chat, people may respond with thread comments – be ready to answer questions. Etiquette: Don’t DM people unsolicited links; also, remain active in other conversations (Slack communities quickly flag drive-by promoters). Link: (You typically need to apply or get an invite via the community’s website. For Startup Chat, it’s a paid membership in some casesstartups.comspringworks.in.)

• Discord Communities (AI & Robotics) – Discord has many public servers for tech topics:

  • Learn AI Together (Discord) – ~80k members interested in AI development, from newbies to researchers. It has channels like #daily-news, #projects, #questions. You can share your project in a project showcase channel. Etiquette: Provide context (e.g. “Built an AI tool that <does X>. It’s free to test – would love feedback!”). Don’t just drop a link; start a conversation around it. The community is supportive if you’re genuine.digitalocean.com

  • Robotix or Open Robotics Discord – communities for robotics developers (Robotix.io’s Discord is noted as very activereddit.com). Here you might find channels for specific interests (drones, ROS, 3D printing robots, etc.). Post in the relevant one. Etiquette: Engineers here appreciate technical details. For example, share a quick spec of your robot or an obstacle you overcame, along with a demo link. This invites peers to discuss or even help.

  • Indie Hackers Discord – (unofficial, since IH is primarily a forum, but there are founder-driven Discords out there like “Indie Worldwide” etc.) – If you join one, treat it like Slack: introduce yourself, engage, and only then mention your startup in the allowed context.

• LinkedIn Groups – LinkedIn hosts groups such as “Future Technology & Artificial Intelligence” (260k+ members) or regional startup groups. Posts here appear in a feed for group members. Posting rules: Usually no blatant ads. A good strategy is to share an insightful post about your startup’s domain. For example, in an AI group, you might post: “AI in healthcare is notoriously hard – we’ve spent 6 months building a tool that scans X-rays for anomalies. It’s 2x faster thanks to [technique]. Curious to get your thoughts on this approach!” and then in comments or at the end mention, “(This is actually our startup, [Name], now enrolling beta users – DM if interested.)”. This way, you’re starting a discussion rather than just promoting. Etiquette: Professional tone, no memes or slang as you might on Reddit. Engage with any comments promptly (LinkedIn group posts can sometimes get comments days or weeks later due to the algorithm). Also, posting during business hours (Tue-Thu, 9am-1pm in the group’s predominant time zone) tends to get better visibility. Link: (Find groups via LinkedIn search; join and read rules on the group page before posting.)

• Facebook Groups – There are numerous Facebook groups like “Artificial Intelligence and AI Robotics” or “AI Startups” (some local, some global). These can have anywhere from a few hundred to tens of thousands of members. Posting rules: Almost all FB groups have a rules section – read it. Many forbid “self-promotion” except in specific weekly threads or with admin permission. Some groups do “Promo Friday” where you can post your business. If promotion is allowed, treat the post like a story: include an image or video (visuals drastically increase engagement on FB). For instance, post a short clip of your robot in action with a caption explaining what it does and a polite ask (“We’ve been working on this for a year – finally ready to show the world. Would love feedback from this group!”). Etiquette: Be ready for comments – respond to each, as Facebook’s algorithm will show your post to more people if it’s getting engagement. Time-wise, evenings and weekends often get more traction in hobbyist/enthusiast groups (people browse off-work hours).

• Special Interest Chat Groups – e.g. Slack channels for robotics startups (if you find one via communities like Hardware Herd or IoT communities) or Discords like r/entrepreneur’s Discord server. In any small community like this, the key is to give before you take: answer questions, join discussions, and build goodwill. Then when you announce your own project, people will be more receptive.

In summary, adjust your tone and content to the community: more technical in developer circles, more story-driven in founder circles, and more straightforward in professional groups. Always follow the specific posting rules (many groups have a pinned message or description with their do’s and don’ts). When in doubt, reach out to a mod/admin – a polite message asking “Would it be okay if I share my startup? It does XYZ, which I think the group would find useful” can go a long way and prevent your post from being removed.

Example Promotional Posts (Copy-Paste Templates)

Below are sample post templates for each channel type. You can adapt these to your own startup. They demonstrate a concise, engaging style with a clear description and call-to-action:

1. Forum Example (Hacker News – “Show HN” post)

Title: Show HN: RoboScan – AI-powered warehouse robot that audits inventory overnight

Hi HN! My co-founder and I built **RoboScan**, an autonomous warehouse robot that uses computer vision to count inventory on shelves.

**What it does:** It navigates through a warehouse at night, scans barcodes and shelves with an onboard camera, and uses an AI model to detect missing or misplaced items. In the morning, it generates a report of inventory discrepancies.

**Why:** We both worked in e-commerce and saw how tedious (and error-prone) nightly inventory checks were. We’re using SLAM and a custom object-detection model (YOLOv5) to let the robot move around and identify products in real time.

We’re in a closed beta with two local warehouses, and so far they’ve been able to reduce manual inventory labor by ~30%.

**Ask HN:** We’d love your feedback on our approach and any suggestions for improving the AI detection (especially in low-light settings, which is our current challenge). Also happy to answer any questions about how we built the hardware or software. Thanks!

Demo video: [link to a 1-min demo video]

Why this works: It clearly states what the product is and does in the title and first lines. It provides technical details for the HN audience (SLAM, YOLOv5), gives the backstory (“why”), and ends with specific requests for feedback. It avoids marketing language (no “revolutionary!” or sales pitches) and instead speaks peer-to-peer. The tone is humble and curious. The call-to-action is implicit: give us feedback or ask questions. (Including a demo link is great for HN as long as it’s not behind a signup wall.)

2. Newsletter Pitch Email Example (to a tech news curator)

Subject: New AI tool for farmers (drones + ML) – tip for your newsletter

Hi <Name>,

Love the newsletter! I wanted to share a quick tip: **AgriAI** – a startup using drones and AI to help farmers spot crop diseases early.

They launch swarms of camera-drones that scan fields and use a computer vision model to detect blight or pests automatically. It’s like an “AI crop doctor.” This could save large farms tons of money (and reduce chemical use) by treating issues sooner.

The team just opened their beta for free to farmers this week.

I thought this mix of AI + agriculture might be a cool one for <Your Newsletter>.

More info: <link to press kit or blog post with details>.

Thanks for reading!
- <Your Name>
<Title>, AgriAI
<contact info>

How to use: This is an email you might send to a newsletter like TLDR or a tech blogger. It’s brief (under 150 words), leads with the product’s one-liner and why it’s interesting (AI + drones for farming, a novel use case). It’s written in an informational, third-person tone as if you’re just a fan (you can send it as the founder or even have a friend/co-worker send it to avoid too much self-praise). The call to action is subtle: it provides a link for more info. You’d swap in your startup’s details accordingly. Always personalize a bit (e.g., mention the curator’s newsletter name).

3. Social Media Example (LinkedIn Group or Facebook Post)

“🚀 **Excited to share our project with you all!** We’ve built a robot called **RoboChemist** that automates chemical lab experiments. 🧪🤖

In our biotech lab, we noticed researchers spend hours pipetting and mixing reagents by hand. RoboChemist is a tabletop robot that can do those repetitive experiments 24/7 with precision. It uses computer vision to measure color changes and an AI to adjust on the fly.

**What this means:** labs can run experiments overnight and get results faster – one team using our prototype saw a 40% increase in throughput.

**Why I’m posting:** We just opened up **early access** for labs and would **love feedback or questions** from this group. If you’re in biotech or chem and have ideas (or want to try it out), please reach out or comment!

*(Pics in comments of RoboChemist in action.*)*”

(This would be accompanied by a few photos of the robot or a short demo video.)

Why this works: It starts with an enthusiastic tone appropriate for social media and uses emojis to grab attention. The post explains the problem and solution in lay terms (assuming a mix of technical and non-technical readers in the group). It highlights a concrete benefit (40% throughput increase). It ends with a clear call-to-action: inviting feedback and offering early access. The poster’s excitement is evident but not braggy – they frame it as “we built this because we saw a problem, here’s how it helps, let us know what you think.” This encourages group members to comment or ask questions. The mention of pictures/videos helps because visual proof will increase engagement on Facebook/LinkedIn. Remember to adjust formality based on the group: for a LinkedIn group, you might tone down the emojis; for a Facebook group, the casual style is usually fine.

4. Launch Platform Example (Product Hunt)

(On Product Hunt, you submit a tagline and then add a comment. Below is a template for the maker comment after posting your product.)

**Hi Product Hunters!** 👋 I’m <Name>, one of the makers of **SafeBot**.

We built SafeBot to **make construction sites safer using AI and robotics**. 🏗️🤖 It’s a rugged autonomous vehicle that patrols a worksite and uses computer vision to spot hazards (like unattended tools, spills, or people without safety gear). If it detects something, it alerts the site manager in real-time.

💡 **Inspiration:** Last year, a friend got hurt on a job site due to a missed safety check. We realized many construction sites do safety sweeps only once a day. With SafeBot, we can monitor 24/7 and prevent accidents before they happen.

**How it works:** SafeBot roams around mapping the site (LIDAR + GPS). Our custom AI model (trained on thousands of “unsafe scenario” images) flags things like spilled chemicals or an employee missing a hardhat. We’ve tested on 3 sites so far and reduced minor incidents by ~30%.

**We’re launching on PH** to get feedback from you all. 🙏 This community’s insights are gold. **Ask:** If you were to deploy an AI robot in your workplace, what would you want it to catch or report? And do you have ideas to make a system like this better?

We’d love for you to check out our video and **join our beta** if you’re in construction or industrial safety. Thank you!

➡️ **Link:** [Our website] – (PH exclusive: skip the waitlist with code **PH2025**)

*(We’ll be here all day to answer questions!)*

Why this works: It reads like a friendly conversation. The tone is appreciative and humble. It starts by clearly stating what the product is and the problem it solves (in layman’s terms, for a broad audience). It adds a backstory (personal inspiration makes it relatable), and gives enough technical insight to satisfy curiosity without overwhelming. It includes emojis and formatting to make it easy to skim (common on PH). The makers explicitly ask for feedback (engaging the PH community) and even prompt with a specific question. A call-to-action is there (join our beta) with a perk for the PH community (promo code), which is a nice way to reward early adopters. This comment, paired with your tagline (“SafeBot – AI robot that patrols construction sites for hazards”), would make a strong Product Hunt launch post.

Feel free to modify these examples to fit your voice and your startup’s specifics. The key elements to preserve are: a clear explanation of what your product does, a bit of context or story to make it relatable, and an invitation for the audience to engage (be it trying the product, giving feedback, or asking questions). Good luck with your promotion! 🎉

Finofo’s CEO secured a meeting (and ultimately an investment lead) through a cold Twitter DM to an Andreessen Horowitz (a16z) partnerbetakit.com. In 2022, a16z’s Kimberly Tan tweeted about how she got into venture by cold-DMing; Finofo’s founder took note and messaged her directlybetakit.com. a16z rarely invests at pre-seed, but they were impressed and referred Finofo to other funds – one of which (Motivate VC) led a $1.6 M CAD pre-seed roundbetakit.combetakit.com. This DM was effective due to a mix of smart timing and personalization:

• Relevant Context: The founder reached out after seeing the investor publicly endorse cold outreach, referencing her story. This showed he paid attention to her interests and advicebetakit.com.

• Bold yet Brief: The DM cut straight to the point, introducing Finofo and requesting a meeting. It likely conveyed confidence without a lengthy pitch, aligning with the investor’s own hustle story.

• Timing and Opportunity: The message came when a16z was open to meeting scrappy founders (Kimberly had invited such outreach). That timing led to a quick response and a meetingbetakit.com.

• Outcome – Warm Intros: Even though a16z didn’t invest directly (pre-seed was outside their scope), the DM wasn’t a dead end. Impressing one well-placed investor led to multiple warm introductions and a funded roundbetakit.com. The clear ask and credibility earned through that initial DM essentially “unlocked” Finofo’s lead investorbetakit.com.

Sources: Finofo interview in BetaKitbetakit.combetakit.com (Canadian startup news).

2. Learning Loop – Instagram DM Yields VC Investment (Angel/VC Funding)

First-time founder Sina Meraji raised capital by reaching out over an unconventional channel: Instagram. After struggling to get responses via email or LinkedIn, Sina noticed one prominent VC’s LinkedIn and Twitter were closed – but his Instagram DMs were openindiehackers.com. He sent a concise cold DM: “Hey, I’m building [Learning Loop] and raising $70k, $30k committed. Interested?”indiehackers.com. To his surprise, the investor replied the next day asking for a deck, then jumped on daily Instagram video calls for a week – resulting in a signed deal (the VC offered far more than the $70k ask, ultimately investing a few hundred thousand dollars)indiehackers.com. Key reasons this DM worked include:

• Extreme Brevity & Clarity: The one-liner introduction DM immediately stated the startup’s name, that they were raising a small round, and that nearly half was already committedindiehackers.com. This piqued the investor’s interest by conveying traction (money already committed) in just a sentence.

• Informal, Personal Tone: Using Instagram made the outreach feel more casual and human. The tone was friendly (“Hey… interested?”) yet direct. This stood out compared to formal emails, and the investor responded where he was comfortable (social media).

• Credibility Signals: Mentioning that $30k was already committed acted as social proofindiehackers.com. It suggested others believed in the idea, making the investor more comfortable engaging despite never meeting the founder.

• Persistence & Channel Choice: Sina tried multiple avenues (emails, LinkedIn) with no luckindiehackers.com. By creatively using Instagram – a channel many founders overlook – he reached the investor in a less crowded inbox. The DM’s timing was right after several mutual contacts had told Sina to “talk to this VC,” so the investor’s name was warm on his mindindiehackers.com. This persistence paid off once the message finally got through.

• Clear Call to Action: The DM essentially asked if the investor wanted to learn more. Once the door opened (the investor requested a deck), Sina followed up quickly with a one-pager and hopped on callsindiehackers.com. The quick move from DM to calls built real rapport, validating that the founder was serious and the opportunity was real.

Sources: Founder’s IndieHackers postindiehackers.comindiehackers.com (“I raised money by cold DM-ing a VC on Instagram”).

3. dataroomHQ – LinkedIn Message Secures $3.5 M Seed Round (VC Funding)

In late 2022, Jeff Schwartz, CEO of dataroomHQ, wasn’t even actively fundraising when a single LinkedIn message sparked his entire seed round. Jeff saw investor Steven Rosenblatt (Oceans VC) comment on a LinkedIn post about a pain point that dataroomHQ’s software solves. Sensing an opening, Jeff sent a cold LinkedIn DM essentially saying: “Hi Steve, hope all is well. The software we built solves this exact use case… we’re kicking off fundraising if you wanted to connect.”carta.com. Rosenblatt replied within minutes and set up a meeting, which led to Oceans swiftly issuing a term sheetcarta.com. dataroomHQ ultimately closed a $3.5 M seed round led by Oceans, all kicked off by that opportunistic DMcarta.comcarta.com. Why this approach worked so well:

• Laser-Focused Personalization: Jeff’s message referenced the exact topic the investor had just discussed publiclycarta.comcarta.com. This wasn’t a generic pitch – it was a direct reply to something on the investor’s mind. That relevance grabbed attention instantly.

• Value-Add Mindset: Rather than begging for funding, the DM offered a solution: “We’re building software for that.” It positioned the startup as helpful to the investor’s thesis, not just seeking cash. This tone of adding value made the investor eager to learn more.

• Brevity with Context: The initial outreach was only a sentence or two plus a friendly greetingcarta.com. It respected the investor’s time, yet by mentioning a specific use case and that they were about to fundraise, it opened the door to talk. Curiosity did the rest.

• Timing and Momentum: The outreach happened just in time – the investor’s post had essentially described a need, and dataroomHQ came forward as the solution. Meeting before a formal fundraising process gave the VC a proprietary look, which quickly led to a term sheetcarta.com. This “surprise” inbound opportunity actually put the founder in a great position to negotiate and then fill the round with additional investors via introscarta.comcarta.com.

• Clear Outcome & Ask: Jeff’s DM subtly indicated they were kicking off a fundraising process, implying an ask for a meeting without over-sellingcarta.com. Once the investor bit, Jeff provided a polished product demo and metrics (a “minimum remarkable product”), which impressed the VC and made saying “yes” easycarta.comcarta.com.

Sources: Carta “Fundraising Files” interviewcarta.comcarta.com (Jeff Schwartz explains how a cold LinkedIn message led to a term sheet).

4. Factmata – Cold Email to Mark Cuban Results in $500 K (Angel Funding)

When Dhruv Ghulati, founder of Factmata, needed capital for his AI startup, he went straight to high-profile angels who cared about his mission. In 2017, Ghulati emailed billionaire investor Mark Cuban out of the blue – and landed a quick $500,000 investment (Cuban sent $250k immediately and another $250k months later)inc.cominc.com. The email that hooked Cuban was later shared publicly, and it’s a masterclass in effective cold outreach:

Subject: Reaching out — Factmata

Dear Mr. Cuban,

Apologies for my cold message. I am the founder of a Google-backed startup called Factmata that uses artificial intelligence to perform automated fact checking and referencing. We are a team of three NLP researchers and scientists with 30-plus published and cited papers in natural language processing, question answering, and information extraction. I am currently fundraising from people who care about the problem of online misinformation, want to reduce mistrust in the media, and change the way we consume online content. I would love to tell you more about us if of interest, especially given your recent public discussions about this topic.

Look forward to hearing from you soon,

Best regards,

Dhruvinc.cominc.com

Why it worked: Cuban himself has explained that this email did exactly what he looks for:

• Immediate Context & Relevance: The opening lines politely acknowledged the cold approach and immediately stated what Factmata does (AI for automated fact-checking)inc.com. Within two seconds, Cuban knew the pitch was about fighting online misinformation, a topic he’d been outspoken about – instantly making it relevant to himinc.cominc.com.

• Credibility Up Front: The email packed credibility signals early: mentioning Factmata was “Google-backed” and that the founding team were published AI researchersinc.com. This established serious validation and expertise. In Cuban’s words, it “immediately captures attention and builds credibility,” showing the startup “has already gained significant validation.”inc.commailtrap.io

• Focus on Facts, Not Hype: Rather than grandiose promises, Ghulati stuck to concrete facts – team credentials, number of published papers, and the problem they are tacklinginc.cominc.com. He avoided clichés or excessive claims. This no-nonsense approach resonated with Cuban, who “dislikes over-the-top claims.”inc.cominc.com

• Personalization & Shared Mission: The closing line referenced Cuban’s “recent public discussions” about misinformationinc.com. That personal touch – showing he followed Cuban’s statements – signaled a shared passion for the mission. It framed the pitch as seeking a partner in solving a problem Cuban cared about, not just money.

• Modest Call to Action: Notably, Ghulati didn’t ask Cuban outright for money in the first email. He asked if he could “tell more” about the project if Mark was interestedinc.com. This low-pressure ask was easy to say yes toinc.com. Indeed, Cuban replied quickly asking for more info, and after a short follow-up email with a one-pager and a teaser deck, Cuban began “peppering with questions,” which led to a dealinc.cominc.com. The stepwise engagement (interest → info → Q&A → investment) all started from that humble but powerful cold email.

Sources: Inc. Magazine (email text and analysis)inc.cominc.com, featuring Dhruv Ghulati’s successful pitch to Mark Cuban and why it grabbed Cuban’s attentioninc.cominc.com.

5. Mapistry – Data-Rich Cold Email Nets $2.5 M Seed (VC Funding)

An excerpt of the cold email sent by Mapistry’s CEO, Allie Janoch, to investor Jason Lemkin. It concisely introduces Mapistry, highlights big-name customers and 15% MoM growth, provides recent revenue and TAM, and even references Lemkin’s own SaaStr conference talk – all in one screen.saastr.comsaastr.com

When Allie Janoch, CEO of environmental compliance startup Mapistry, needed capital, she crafted a model cold email that led to a $2.5 M seed round led by SaaStr’s Jason Lemkin. Her email (sent Oct 2017) is shown above, and Lemkin was so impressed he often cites it as an example of “cold emails that worked”saastr.comsaastr.com. What made Allie’s approach outstanding:

• Complete yet Concise Pitch: In a few short paragraphs, the email summarized the opportunity, early customers, traction, growth, and market sizesaastr.com. It opened with one sentence on what Mapistry is (a SaaS for industrial environmental regs) and immediately underscored the pain point (regulations are confusing, current tools are Excel) – a hook for why Mapistry matters. Bullet points then showcased impressive metrics: Fortune 500 customers, 15% month-over-month growth, $67K monthly revenue, and a large TAM【44†】. This gave the investor an instant snapshot of viability.

• Personalized to the Investor: Allie tailored the message to Jason Lemkin specifically. She referenced having followed SaaStr (Lemkin’s content) and even called out his talk with Veeva from the SaaStr conference, tying it to Mapistry’s “vertical SaaS” approach【44†】. This genuine nod to the investor’s own insights demonstrated research and wasn’t a mass email. Lemkin noted the email was “truly personalized,” which made it stand outsaastr.com.

• Strong Credibility and Traction: By naming well-known customers (3M, Tesla, etc.) and real growth numbers, the email established credibility fast【44†】. It wasn’t vague hype – it showed Mapistry already had big clients and revenue. This aligns with what investors look for: signal that a team can execute. Lemkin later wrote that this cold email “does a good job of summarizing… early customers and traction, growth profile, and market size” – exactly the data points VCs care aboutsaastr.com.

• Low-Pressure Close: Allie ended by suggesting a 30-minute call or coffee and politely asked if he had time the week after next【44†】. This respectful, low-pressure ask (rather than an urgent plea) exuded confidence. Lemkin highlighted this as a “low drama” approach – the founder wasn’t desperate, she was professionalsaastr.com. Giving the investor a comfortable option to talk later made it easy for him to say yes.

• Tone of Confidence: Throughout, the tone was factual and upbeat but not overly salesy. For example, stating “we feel strongly we have great people and aren’t giving their time away free” when describing their services strategy showed assertiveness about her business model【44†】. The email projected that Mapistry was a winning opportunity, and the calm confidence (with data to back it) likely made the investor feel FOMO about letting it pass. Lemkin indeed responded, and this cold email ultimately turned into $2.5M in funding for Mapistrysaastr.comsaastr.com.

Sources: Jason Lemkin’s SaaStr blogsaastr.comsaastr.com (sharing Allie Janoch’s actual email and commentary on why it was effective) and Allie’s own case study on using a cold email to secure a seed roundsaastr.comsaastr.com.

Each of these examples shows a different strategy for cold outreach – from ultra-brief social media DMs to richly detailed emails – but all succeeded by hitting the right notes of personalization, clear value, credibility, and respectful tone. These founders proved that with the right approach, a cold message can warm up even the most skeptical investor and lead to real funding. Each DM/email was not a generic blast; it was thoughtfully tailored and timed, which is why they opened doors to checks being written.

Read more

Read more

Read more

Not only robotics: teleoperation, motion capture, and embodied AI. Manus started as a small student project and grew into a deep tech company trusted across the robotics world.

We talk about Stephan's path from studying law and economics to discovering he was meant to build things, not file documents. He explains how the first Manus glove was built in evenings and weekends, how an early Kickstarter failure opened better doors, and how an accelerator program became the turning point for the company.

Stephan shares how Manus shifted from VR gaming to B2B simulation, then to motion capture, and now to robotics. He explains why EMF tracking became their core technology and why precise hand data is suddenly in huge demand as humanoids and AI driven robots take off.

We also talk about building a company for ten years, staying alive through hype cycles, making hard calls, and focusing on what real customers need.

They work on end to end driving and generative world models is changing how small teams can compete with billion dollar labs.

We talk about his path from electrical engineering in Belgium and Santa Barbara to joining comma as one of the earliest engineers. Harald explains how he helped turn a hacker project into a focused engineering team that ships reliable autonomy to thousands of real users.

He walks me through comma’s move to a single neural network that controls the car from video input, why deleting code is often more powerful than adding more, and how his team uses world models to train on billions of synthetic miles that never existed on real roads.

Harald also shares what it is like to build inside a company with no CEO, why simplicity beats complexity in autonomy systems, and how the new comma 4 and Body 2 signal a move beyond cars into general robotics.

If you work in robotics, autonomy, or AI systems, this conversation is packed with lessons about engineering clarity, avoiding brittle stacks, and shipping real products with small teams.

In this episode, we talk about how Albane grew up in Paris, pushed her way through the French prep school system, and found her path into entrepreneurship after a semester at Wharton opened her eyes to what was possible.

She explains how she met her two co-founders during the X EC program, and how the first version of Inbolt had nothing to do with robots. They started with a real-time guidance tool for workers and later pivoted to industrial robots after spending months on factory floors and seeing where customers really needed help.

Albane walks through what it takes to sell and deploy automation inside global companies. She talks about why founders need to be on site all the time, and why selling early matters more than waiting for perfect reliability. She explains why deployment is everything in manufacturing and how Inbolt built a system that retrofit existing robots, reduced downtime, and proved value in a few weeks instead of years.

We also talk about ambition, hard work, and the pressure she faced breaking into industries that are not always welcoming to young founders. Albane shares her early years in boxing gyms, her drive to be taken seriously, and the mindset that helped her operate and grow a company that now works with some of the biggest manufacturers in the world.

If you want a clear look at how real robotics gets deployed at scale, and what it takes to build a company inside the most demanding industry in the world, this is an episode you should hear.

We talk about his path from taking apart PCs as a teenager, to building large automation systems at Fraunhofer, to becoming CTO at NEURA Robotics. Hendrik explains why he walked away from a safe leadership role after his father’s heart attacks, how going plant based changed how he sees food, and why he became obsessed with the question: if I can cook healthy meals quickly at home, why is it so hard to get that quality in hospitals, canteens, and on the road.

Hendrik breaks down how GoodBytz works in practice: a compact robotic kitchen that cooks up to 150 meals per hour, runs 24/7, and delivers consistent quality in places like university hospitals and motorway sites. We get into what they learned from running their own Lieferando brand, why he mostly ignores CVs and hires for people who build things for fun, and how a small Hamburg startup ended up signing a landmark contract with the US Army to feed soldiers in South Korea.

If you care about robotics with real deployment, food at scale, or building a deep tech company that actually ships, this episode will be very useful for you.

Role Prioritization: Critical Early Skill Sets

At pre-seed, you need versatile engineers who collectively cover the core technical domains of robotics and AI. Building robots is inherently interdisciplinary, so each early hire should bring expertise in a crucial area (and ideally wear multiple hats)medium.commedium.com:

• Hardware Prototyping (Mechanical/Mechatronics Engineer): Someone who can design and build the physical robot – from CAD and 3D printing to assembling actuators and sensors. This person iterates quickly on prototypes and handles mechanical structure, drivetrain, and physical components.

• Embedded Systems & Controls Engineer: An engineer skilled in electronics, firmware (C/C++/RTOS), and control systems. They integrate sensors, microcontrollers, and motors, and develop real-time control loops for robot motion and behaviordirectrecruiters.comdirectrecruiters.com. Proficiency in robotics middleware (e.g. ROS/ROS2) and system integration is highly sought after in robotics startupsdirectrecruiters.com.

• Perception & AI Specialist: If your robot needs autonomy or intelligence, hire a specialist in computer vision, machine learning and sensor fusion. This engineer can develop algorithms for object detection, SLAM, path planning or other AI-driven featuresdirectrecruiters.com. As AI-driven robotics applications grow more complex, talent in deep learning and vision is increasingly valuabledirectrecruiters.com.

• Full-Stack Roboticist / Systems Generalist (optional): Often one of the first hires is a generalist who can bridge hardware and software. They are comfortable troubleshooting everything from firmware bugs to high-level software. Their broad “systems engineering” view helps glue together mechanical, electrical, and AI components into one working systemmedium.com. In a small team, a “Swiss army knife” engineer who can tackle diverse tasks is invaluable.

Prioritize based on your team’s existing strengths and the product’s needs. For example, if the founder is a software/AI expert, the first hire might be a hardware/mechanical engineer to cover that gap. Conversely, a founding team with hardware experience might seek a controls/ML expert. The goal is to cover the 6–10 different disciplines (mechanical, electrical, perception, controls, software, firmware, etc.) needed to build a robot with just a few peoplemedium.com. Every early engineer should bring essential skills that move your prototype forward – there’s no room for niche specialists or “passengers” at this stage medium.comtechround.co.uk.

Talent Sourcing: Where to Find High-Impact Robotics Engineers

Finding top-notch robotics engineers for a tiny startup can be challenging – the best ones aren’t usually browsing generic job boardsbamboo-x.com. Leverage creative, targeted sourcing strategies:

• Personal Network and Referrals: Start with people you know or those your connections can introduce. Y Combinator advises making a list of the best engineers you’ve worked with (or studied with), invite them to chat about your vision, and ask each for referrals if they’re not available[guzey.comguzey.com](https://guzey.com/links/#:~:text=,). In fact, investors note that your ideal “founding engineer is likely someone you’ve worked with or went to school with”sfstandard.com. Use alumni networks, former colleagues, and mentors to get warm leads on talent who trust you.

• Universities and Research Labs: Tap into robotics and AI programs at top universities. Attend university career fairs, connect with professors or lab directors, or reach out to grad students/postdocs whose research aligns with your startup. Many cutting-edge roboticists are in academia; an enthusiastic student or researcher might jump at an impactful startup role (often for equity upside and real-world application of their work).

• Hackathons and Robotics Competitions: Engage with communities like FIRST Robotics, DARPA challenges, or local hackathons focused on AI/robotics. These events are full of builders who love tough problems. You might meet a talented engineer at a hackathon or robotics club who’s eager to join a real startup adventure. Deep tech accelerators sometimes host hackathons or innovation challenges – a great way to spot passion and skill under pressure.

• Open-Source Communities (ROS, GitHub): Robotics is fueled by open-source. Search GitHub for contributors to projects in your domain (e.g. ROS drivers, SLAM algorithms) and see who’s solving similar problemsbamboo-x.com. If someone has meaningful commits on a robotics repo or maintains a useful ROS package, they might be open to joining a startup. Engage on forums like ROS Discourse or robotics Stack Exchange – answering questions and sharing your vision can attract like-minded engineers.

• Startup Job Boards and Communities: Post (and lurk) where startup-minded tech talent hangs out. Examples: Wellfound (formerly AngelList) for startup job seekers (you can filter for those open to equity-heavy roles), YC’s Work at a Startup job board (which attracts engineers already tolerant of early-stage chaos)bamboo-x.com, and the monthly “Who is Hiring?” thread on Hacker News. Communities like Indie Hackers or Reddit (r/robotics, r/MachineLearning) can also surface folks who tinker on side projects and might join an exciting mission.

• Deep Tech Programs and Accelerators: Leverage networks from programs like HAX (SOSV), EF (Entrepreneur First), Techstars or Startupbootcamp (which has a robotics cohort). Even if you’re pre-seed, mentors and organizers in these programs might connect you to engineers who love startups. Similarly, industry groups and Slack communities for robotics/AI startups can be fertile ground for recruiting.

• LinkedIn and Direct Outreach: Use LinkedIn to identify engineers at robotics companies, especially those at large companies who might crave a startup environment. Craft personal messages about why they specifically would be a great fit and the impact they could have. Highlight any common connections or shared interests in their profile. Note: generic “we are hiring” posts won’t stand out – targeted, passion-driven outreach works better for deep tech.

Be proactive and creative. The truth is, great engineers who thrive in early-stage chaos aren’t waiting around for a random job ad – you have to go find thembamboo-x.com. By tapping communities of builders and using referrals, you’ll increase your chances of landing talent that fits your high bar.

Hiring Strategy: Attracting Talent with Limited Cash & No Brand

Early-stage robotics startups can’t outbid Big Tech salaries, so you must win hearts and minds. Your pitch to candidates should emphasize mission, impact, and growth over short-term money. Here’s how to resonate with top engineers in this space:

• Sell the Vision and Mission: Articulate a bold, world-changing mission that will excite an engineer’s imagination. Deep tech engineers are often motivated by solving hard problems that matter. Show them exactly what big challenge in robotics/AI your startup is tackling and why it could change the world (or at least an industry). For example, one founding engineer joined a startup because “they showed me they have an ambitious long-term vision, and I believed we can make it come true”sfstandard.com. Share your passion – if you inspire them with your vision, they’ll overlook the lack of a fancy office or famous logo.

• Emphasize Ownership and Impact: Make it clear that as one of the first three engineers, they won’t be a cog in a machine – they’ll be a co-builder of the company. Use phrases like “founding engineer” or “early technical leader.” Explain that they will own entire subsystems and make foundational decisions, which is rarely possible at a larger firm. High-caliber engineers are often drawn to this kind of autonomy and influence. As one Silicon Valley founder put it, seek “someone who wants to be a founder but doesn’t want to do it right now”sfstandard.com – meaning the hire will get many of the thrills of founding (like significant equity and creative control) without having to be the CEO. This framing can attract entrepreneurial-minded engineers.

• Highlight Learning and Growth Opportunities: In a robotics startup, an engineer might get to develop skills in hardware, software, AI, and product development all at once. Stress that they will grow their skill set rapidly by working on cutting-edge technology and solving novel problems. There’s no bureaucracy to hinder them – they can implement ideas immediately. Also mention any notable advisors, investors, or team credentials (if you have a PhD co-founder or backing from a known VC or accelerator, for example) to show they’ll be in an environment of top-tier mentorship and technical excellence.

• Be Honest and Mission-Driven in Your Messaging: Authenticity goes a long way. Acknowledge that the startup journey is risky and hard, but reinforce why it’s worth it. Engineers who join pre-seed teams aren’t there for the salary – they’re there for the visionthink-it.io. So talk about the real-world impact of the product, the chance to “advance the state of the art” in robotics, or to build something that’s never been built before. Early-stage startups thrive on trust, energy, and a shared belief in the missiontechround.co.uk – let that come across in every conversation. Share your excitement for the problem and invite them to be part of the founding story.

• Leverage Culture and Values: Give candidates a feel for your nascent culture. Maybe you value scrappiness, quick iteration, and learning from failure – traits vital in robotics. Point out that your first hires will shape the culture: how decisions are made, how feedback is given, how wins are celebratedtechround.co.uk. This can appeal to engineers who want a say in creating an ideal work environment. Also, if you can offer non-monetary perks like flexible hours or remote work (if compatible with hardware development), mention it – flexibility and work-life balance can partially offset lower paythink-it.io.

• Personalize the Courtship: With no big brand to lean on, the founder’s own involvement is key. Reach out personally (no HR intermediaries) and, once a candidate is interested, spend ample time with them. Invite them to meet over coffee or at your lab/garage to see the prototype. Share your roadmap and even your fears – showing transparency builds trust. Some founders treat recruiting their first engineer like closing a co-founder: they “pull out all the stops” to convince the right personsfstandard.com. This might include having existing team or advisors talk to the candidate, providing references (reverse interviews) so the candidate can back-channel the founder’s reputationsfstandard.com, and generally making them feel deeply wanted. Make the hire feel like a pivotal team member from the start.

Bottom line: Craft a narrative where the candidate sees joining your startup as a once-in-a-career opportunity to do the most meaningful work of their life. When a candidate is intrinsically excited and believes in your mission, they’ll overlook the trappings of a big company. As one recruiter noted, compensation alone isn’t enough – top engineers also seek mission-driven work, growth potential, and strong technical leadershipdirectrecruiters.com. Show that your startup offers those in spades.

Equity & Compensation: Structuring an Equity-Heavy Package

With limited cash, you’ll rely on equity to attract and motivate your first hires. It’s common for early-stage deep tech startups to offer below-market salaries + generous stock options. Here are some guidelines on equity ranges and compensation structure for the first three engineering hires:

• Significant Equity Stakes for Early Engineers: The first few employees of a startup typically receive equity in the single-digit percentages of the company, vesting over 4 years. Exactly how much depends on experience and how critical they are to the team. For a true “founding engineer” (employee #1 who is almost a co-founder), equity can be on the higher end – sometimes as much as ~5%, or even a co-founder-level stake if they are irreplaceablesfstandard.com. More commonly, a very early senior engineer might get on the order of 1% (give or take) at seed-stage companiestechcrunch.com. One founder noted she gave her first engineering hire 3% to ensure he was motivated for the long runtechcrunch.com. In many cases, early engineers join with 0.5% – 2% equity grants, scaling with how early and senior they aretechcrunch.com. The third or fourth hires might be closer to the lower end of that range (e.g. 0.5–1%), whereas the first engineering hire could be 1–2%+ if not designated a co-founder.

• Equity Tied to “Employee Number” and Risk: The earlier someone joins, the more risk they take and the more equity they typically receivetechcrunch.com. For example, a key engineer who is the third person on the team (after two founders) might be viewed almost as a co-founder and get a very large chunk – some sources suggest on the order of ~5–10% is not unreasonable in those casestechcrunch.comsfstandard.com. By contrast, an engineer hired after you have an MVP and some funding might expect a smaller options grant (perhaps <1% if they’re junior). Use equity to reward early risk-taking – those first hires “definitely get a lot more stock than later employees”techcrunch.com. It’s also wise to communicate that these percentages are pre-dilution – as more funding comes in, their ownership % will dilute, so giving a healthy upfront slice is important to make the grant meaningful long-termtechcrunch.com.

• Tiered Approach for First, Second, Third Hire: One way to structure it:

  • Engineer #1 (Founding Engineer): Highest equity. Could be ~2% if they are strong but not quite co-founder, up to ~5% if they are essentially co-founding CTO-level. Often comes with a title like “Head of Engineering” or “Founding Engineer”. Salary might be modest (some startups offer a stipend-level salary) to conserve cash, with the equity as the big carrot.

  • Engineer #2: Still very early, but joining once a bit more progress is made. Perhaps on the order of ~1% equity (+/- 0.5%). Salary maybe slightly closer to market than employee #1, but likely still below big-company levels.

  • Engineer #3: Maybe ~0.5–1% equity depending on their experience. If this hire is more junior or brought on to fill a specific gap, the equity could be lower, but it should still be meaningful. At this stage, you might have raised a pre-seed or seed, so you can pay a somewhat better salary, but many startups still keep early salaries lean (maybe 50-70% of market) until a bigger round is raised.

  These ranges are illustrative – market data shows founding software engineers at pre-seed often get anywhere from ~1% up to ~5% in extreme casessfstandard.comtechcrunch.com. If the person is extremely pivotal (like the only hardware wizard or AI PhD who can build the core tech), err on the higher side to secure them. Remember you likely set aside ~10-15% of your company in an option pool for employees at seed stagetechcrunch.com, and you want to use a chunk of that to lock in your first 2–3 hires.

• Vesting and Cliffs: All equity should vest – standard is 4 years with a 1-year cliff (so if they leave before 1 year, they get nothing). This is expected by candidates and ensures they are incentivized to stay. If someone is given a co-founder level stake (5% or more), double-check with your lawyers/investors, as very large grants might have custom vesting or roles attached.

• Equity vs. Salary Trade-offs: Be upfront that you’re compensating with equity. Some early hires might prefer a bit more salary over equity or vice versa – if you have any flexibility, you can offer a mix (e.g. a candidate could choose between a higher equity/lower salary package or a slightly higher salary for a bit less equity). Many startups offer equity-heavy packages by default. For context, one report advises giving a senior engineer at seed stage ~1% and a junior engineer ~0.15%techcrunch.com – but in cutting-edge fields like robotics/AI, founders sometimes go above these benchmarks to win the talent. Indeed, facing fierce competition, some startups are now offering up to 5% equity for top “founding engineer” hires, or even naming them co-founders and granting 10–15% if that’s what it takessfstandard.com.

• Other Compensation: While equity is the main lever, try to meet basic financial needs with salary if you can. Even if you can only pay, say, half of market salary, that can help an early hire cover living expenses. You can also defer some salary until funding closes (with a clear agreement) if they are amenable. Additionally, consider small perks that mean a lot: relocation assistance if they must move, or perhaps covering conference travel or equipment (a good laptop, robot parts) – these show support even if you can’t pay big money.

• Transparency and Upside: When discussing the offer, educate them on what their equity could be worth if the company succeeds (without overpromising). Show that if the company becomes the next DeepMind or Tesla, their X% could be life-changing. Also be transparent about dilution (e.g. their 2% now might be ~1% after a Series A, etc.), and that the true payoff comes with an exit. This honesty builds trust, and motivated engineers will view the equity as a long-term bet alongside you.

In summary, make your first hires feel like true partners through equity. As one founder put it, early hires should feel like “co-builders, not just employees,” and stock options give them a tangible stake in the outcomethink-it.io. Getting this right not only helps you hire them, but also keeps them motivated to slog through the tough early years for that eventual reward.

Interview Process: Selecting for Skill and Startup Fit

When hiring deep tech engineers at the pre-seed stage, traditional corporate interview loops (with brainteasers or multiple bureaucratic rounds) won’t suffice. You need to assess practical skills, problem-solving approach, and cultural fit – and also give the candidate a taste of working at your startup. Here are strategies for an effective early-stage interview process:

• Focus on Practical Challenges Over Puzzles: Tailor technical evaluations to mimic the kind of work they’ll actually do. For a robotics/AI startup, that might mean discussing how to design a specific system or reviewing code from a relevant project. Many founders find that talking through a candidate’s past projects is far more illuminating than abstract quiz questionsfundersclub.comfundersclub.com. Ask them to describe a tough problem they solved in robotics or a time they had to MacGyver a solution with limited resources. For example, one robotics founder’s interview “includes no actual coding. Instead, he asks candidates to describe products they’ve built, explain engineering choices, and talk about products they admire,” to test depth of thinkingsfstandard.com. This kind of discussion reveals their design philosophy, creativity, and whether they understand the “why” behind engineering decisions.

• Include a Hands-On Component or Trial Project: There’s no better way to evaluate an engineer than seeing them in action. Consider a small paid trial project or on-site hack session. This could range from a two-hour coding exercise in a relevant codebase, to a take-home assignment (e.g. interface a sensor with a microcontroller, or analyze a dataset and detect objects), to even a short contract stint. Some startups invite the finalist to work with the team for a day or two – for example, Origami (a YC-backed startup) asks candidates to come in for a two-day work trial after initial interviewssfstandard.com. During a trial, you might pair program on a real problem or have them brainstorm on a current technical challenge. Make sure any trial assignment is scoped reasonably (you’re not looking for free labor, but rather how they approach problems, communicate, and learn). This not only lets you see their skills and collaboration style, but the candidate also experiences your culture and the kind of work they’d be doing – it’s a two-way evaluation.

• Structured but Lightweight Process: Even though you’re small, have a clear interview plan. For example:

  1. Initial call (founder screen): A casual conversation about their background and interests, and an enthusiastic pitch about your vision. Gauge their excitement and basic fit.

  2. Technical deep-dive: This could be an in-depth discussion of a past project (ask them to prepare to talk about one in detail), or a whiteboard design exercise specific to your domain (“How would you design a vision system for a warehouse robot that does X?”). You’re looking for problem-solving approach, relevant knowledge, and resourcefulness more than memorized formulas.

  3. Team fit and culture: If you have any other team members or advisors, include them in interviews to assess personality and values fit. Early hires must be low-ego, resilient, and willing to stretch beyond their job descriptiontechround.co.uk. You can screen for this with behavioral questions (“Tell me about a time you had to learn a completely new skill to solve a problem”) or hypothetical scenarios (“How would you handle it if our prototype fails the day before a demo?”). Look for optimism, curiosity, and comfort with ambiguity.

  4. Trial project (optional): As discussed, a short, real-world task can be gold for evaluation. If not a full trial, even a take-home problem to discuss later can work.

  5. References and back-channeling: Don’t skip this – ask for references from past managers or colleagues, and even use your network to quietly check their reputation if possible. In a tiny startup, one bad hire is devastating, so do your diligence. (Likewise, expect that top candidates will also be gauging your credibility – be ready to offer references, like investors or former coworkers, who can vouch for yousfstandard.com!)

• Evaluate for Startup Mindset: Your interview should filter for those who will thrive in a startup. Signs of a good fit include: the candidate enjoys discussing product ideas (not just engineering); they say “we could try X” indicating collaborative, proactive thinking; they show excitement (eyes light up) at your tough technical challenges; they’re okay admitting what they don’t know (important in a frontier tech problem). Avoid candidates who seem too rigid or who only function with big-team support structures. You want builders who are comfortable with chaos and can figure things out without a playbook.

• Sell While You Interview: Remember that top candidates likely have other options. Use the interview process not only to vet them, but to further sell the opportunity. Founders who successfully hire great talent often turn interviews into two-way conversations. For instance, share some interesting data or a demo of your prototype to get them intrigued. Show genuine excitement when discussing technical challenges – make them feel like they’ve peeked into the future by joining your meeting. One founder approached hiring their star engineer “like closing an important sales deal,” ensuring the team “wowed him during the interview process” and even offering up references to assure him of the company’s potentialsfstandard.com. Adopt that mindset: you are selling the candidate on why this is the most impactful place they could work.

• Keep it Fast and Human: Big companies might drag candidates through 6+ interviews over weeks – as a startup, you should move quickly and stay flexible. If you find someone great, don’t hesitate. Also, treat them exceptionally well during the process: be respectful of their time, give prompt feedback, and be transparent about next steps. The way you conduct interviews signals your culture. A thoughtful, efficient process will leave a strong positive impression on candidates.

By the end of your process, you should have a holistic sense of each candidate: technical competence, problem-solving approach, cultural fit, and genuine interest in your mission. It’s wise to prioritize attitude and aptitude over exact experience (a brilliant engineer can learn ROS or a new ML model quickly, but you can’t teach curiosity or grit as easily). If you’ve done the above, you’ll likely identify the individuals who not only can do the job, but will thrive in the uncertainty of an early robotics startup.

Retention: Keeping Early Hires Motivated and Aligned

Hiring great engineers is only the start – keeping them engaged for the long haul is crucial, especially since they hold so much of your young company’s knowledge. Turn your first three hires into true partners in the venture. Here are early actions to ensure motivation and retention:

• Foster a Co-Founder Mindset: Make early hires feel like co-builders, not just employeesthink-it.io. Invite them into big-picture discussions about product roadmap, pivot decisions, and company direction. When people feel ownership, they’re far more likely to stay through tough times. Granting equity (as discussed) is one part; day-to-day, show that you value their input on strategic matters beyond their immediate tasks.

• Mission Alignment and Purpose: Continuously reinforce the mission and show how their work connects to it. In the whirlwind of prototypes and debugging, it’s easy to lose sight of why you’re building this robot. Remind the team in meetings, “We’re doing this to solve X problem that affects Y people.” Celebrate milestones in terms of impact (“Now our system can do ______, bringing us closer to helping people with ____”). Early employees stick around when they deeply care about the mission – “People at this stage aren’t here for the salary – they’re here for the vision”think-it.io. If they ever start to feel the mission is fading or unclear, their motivation will fade too. So be a constant champion of the purpose.

• Equity and Upside Transparency: Help them visualize the payoff. Without going overboard, periodically reiterate how the equity they’re earning could be very valuable if you succeed. This is not to encourage short-term cash-out thinking, but to reinforce that they are building an asset for themselves. It instills a long-term mindset – they’re not just salaried workers, they’re entrepreneurs with you. Knowing that “if we make it big, we all win big” creates a shared sense of destiny.

• Build a Trusting, Inclusive Culture: “Early-stage startups thrive on trust, energy and a shared belief in the mission,” and how you treat your first hires sets the tonetechround.co.uk. Be transparent with them – about challenges, runway, investor updates – so they feel respected and in-the-loop. Encourage a culture where feedback flows openly and wins are celebrated togethertechround.co.uk. Recognize their contributions publicly (even a shout-out in a team meeting or company email goes a long way to make someone feel valued). When people feel seen and appreciated, they develop loyalty.

• Prevent Burnout (Sustainable Pace): In a robotics startup, there will be sprints of intense work (e.g. demo prep or grant deadline), but burnout is a real risk if crunch-time is constant. Keep an eye on your team’s workload and stress. Founders should model balance in whatever ways possible – e.g. if someone pulled an all-nighter to fix a hardware bug, insist they take a day off to recharge. If you can’t afford perks like catered meals or gyms, you can offer flexibility: let them work from home occasionally when possible, or accommodate personal needs. Show that you care about their well-being. A flexible work culture and respect for work-life balance (to the extent possible in a startup) helps retentionthink-it.io. People will give their all when needed, if they know the company respects their life outside work too.

• Growth and Learning Opportunities: Early employees can fear that as the company grows, their role might change or more senior people will be hired above them. Mitigate this by painting a picture of their future with the company. If they’re strong, commit to their growth: e.g. “You could lead the entire robotics division in a few years” or “We’ll support you to become our head of AI as we scale.” Back this up with actions: give them increasing responsibility, maybe a new title as the team expands, or chances to mentor any new hires. Also consider sending them to relevant workshops or conferences (if budget allows even a small stipend) – this signals you’re investing in their development. Create a clear path so they see how they “fit into the company’s future,” which keeps them engagedthink-it.io.

• Maintain the Fun and Passion: Early startups often have a camaraderie – the late-night problem-solving, the sense of “us against the world.” Keep that spirit alive even as you grow from 3 to 5 to 10 people. Do small team traditions: Friday demo sessions, celebratory beers after hitting a milestone, or hackathons on crazy ideas. These things build friendship and loyalty. When your first engineers feel they’re part of a tight-knit crew doing something special, it’s hard for them to imagine leaving. As one article noted, if they don’t feel like they’re “part of building something bigger,” they’ll be quick to leave for a more stable jobthink-it.io. So continually remind them (through both words and the company culture) that they are building something revolutionary and that they are crucial to its success.

• Competitive Alignment (Later on): As you secure more funding, don’t forget to adjust compensation for your early hires. Nothing is worse than hiring employee #10 at a higher salary than your employee #2 who has been grinding for two years. Plan to give periodic equity refreshers or raises once you can afford it, to reward their loyalty. Early on, you can be open with them: “Right now we all take a pay cut; after we raise our Seed/Series A, I intend to bump everyone to more market-level.” This assures them that the current sacrifice isn’t forever. Retention isn’t just one moment – it’s an ongoing effort to ensure your team feels valued both financially and emotionally.

In essence, treat your first hires like founding team members. Give them reason to be as passionate about the company as you are. When early employees feel true ownership, alignment with the mission, and see personal growth, they will ride out the ups and downs rather than jumping ship at the first better offer. The result: a stable core team that sticks together to turn your robotics vision into reality. As Think-it’s startup retention guide put it, the best startups don’t just attract great talent – they keep themthink-it.io, and by implementing these practices from day one, you’ll do exactly that.

Read more

Read more

We walk through Jon’s journey from tearing apart electronics on a tiny New York City workbench to Harvey Mudd, early YC startups in 3D printing, and building one of the first highly automated factories at Voodoo Manufacturing.

Jon explains why those painful years with “last generation” robots convinced him to start Ultra and focus on one thing first e commerce order packing as a beachhead for real industrial deployment.

He breaks down how Ultra’s robots drop into existing pack stations, learn from examples instead of brittle scripts, and why he believes in multi purpose robots before truly general purpose systems.

We talk about force sensitive dexterity, what most people get wrong about warehouse automation, and how a small team in Brooklyn already has robots running live for customers.

If you care about turning AI and robotics into shipped systems instead of slideware, this one is for you.