HEARTFELT Tree of Questions
Before we back a company, we have to answer four questions together: Why you? Why this? Why now? And why is this a good deal, for investors and for you as founders? You don't answer them for us. You answer them for yourself first. You need to know what you know, and what you know you don't know yet. The part where we might help is the third one: the things you don't know you don't know. Sometimes a good conversation sheds some light there. And we are constantly learning ourselves. Use this tree with your own AI to think your company through before you talk to anyone, us or anyone else.
Open a branch to explore its questions. Drag to pan; scroll or pinch to zoom. Dashed lines mean “only if”; a star marks a must-visit question when one is set.
Read as an outline
Why you?
The founders and the team.
D1 · Founders
2.1 Who on the team works on this full-time, and since when? Do you have side jobs, boards or other projects (and roughly what share of your time), and how much equity do you hold? If someone is not yet full-time, what is the trigger and the date for going all in?
Why we ask
Role, equity % and commitment: full-time status and start date; side jobs, boards, projects and % of time; trigger and date for going full-time; relocation constraints
2.2 Where did you study and work before this, in which roles and for how long? Did you go through a program such as YC, EF, Antler or EXIST?
Why we ask
Background: education (most relevant degree(s) and institution); work history (employers, roles, tenure, seniority, team size led, years in the target industry or function); programs and fellowships (YC, EF, Antler, EXIST, etc.)
2.3 What have you personally built, sold or scaled before, and what was your own role in it? If an earlier startup did not work out, what happened and what did you change?
Why we ask
Track record: what they personally shipped, sold or scaled (own role and contribution first; employer brand counts only if their role there was meaningful); prior startups: role, outcome, capital raised, exit value, and if failed, why and what changed
2.4 Is there something outside business, such as sport, art, science, a competition or an unusual life path, where you reached a very high level?
Why we ask
Exceptional achievement in any domain, incl. outside business: top-level sports, arts, science, competitions, unusual life paths; evidence of having excelled at something
2.5 Where can we see your work in public, for example code, papers, patents, talks, writing or social posts?
Why we ask
Public footprint: code, papers, patents, talks, writing, social; absence recorded as unknown, not negative
2.6 Can you build the product yourself, including with AI tools and agents? Tell us about a workflow you built that combines people and AI: what worked and what broke?
Why we ask
Hands-on building ability: can they build the product themselves, incl. AI-native building (agent-assisted workflows, shipping speed); can they tell a concrete story of a workflow they personally built combining people and AI (what worked, what broke)?
2.7 How long can you personally live without a salary, and how much of your own money is in the company? Is there anything from your past, such as disputes or insolvencies, that we should hear from you first?
Why we ask
Personal finances and history: personal runway, salary needs, own money invested; past disputes, litigation, insolvencies
2.8 Who could we talk to who has worked closely with you, such as a former boss, colleague or co-founder, and who is not connected to whoever introduced you to us?
Why we ask
Founder references: realistically ~2 on the main founder (former colleagues, bosses, co-founders), independent of introducer and founder
2.9 Where are you, in your own view, among the best 1% at something? What is the evidence?
Why we ask
Outlier evidence: where is this founder truly exceptional, i.e. among the best ~1% at something (a skill, an achievement, drive, a way of thinking)? One extreme strength matters more than all-round competence.
2.10 When did you last hold a view most people around you disagreed with, and what happened? Tell us about a time you pushed back or found an unusual way around an obstacle.
Why we ask
Edgy character, shown in behaviour over time: thinks independently and holds non-consensus views; pushes back when it matters; obsessive about their problem; breaks conventions within ethical lines; finds unusual ways around obstacles.
2.11 Why you for this problem? Have you lived it yourself, or what do you know about it that others do not? If you are new to the field, how did you get deep into it?
Why we ask
Founder-market fit: has lived the problem or holds an earned secret; as an insider, can explain why it hasn't been built yet; as an outsider, shows real depth of research
2.12 How big do you want this company to become, and what would it take for you to go all in for ten years or more?
Why we ask
Drive and ambition: determination, resilience, agency; ambition to build a €100M-revenue / €1B-valuation company; motivated by venture scale rather than lifestyle or impact alone, matching the exit size the case needs; ready to go all-in for the long haul
2.13 What is the hardest problem you solved recently, and how? How do you tell whether what you build, or what AI builds for you, is good or bad?
Why we ask
Ability and judgment: raw intelligence and problem-solving; taste (knows what to build and can tell good output from bad); technical depth relative to what the product needs
2.14 How do you sell and tell the story of this company? Who has already joined or helped you, and who in your network would back you with money beyond friends and family?
Why we ask
Commercial and people skills: selling, storytelling; early indication of recruiting ability; relationship building and emotional intelligence; network position (well- vs. under-networked) and ability to raise money beyond friends and family, incl. non-venture capital for capital-heavy models
2.15 When did you choose the honest route although a more convenient one was available? What would people who know you well say about your integrity?
Why we ask
Integrity and character: honest, transparent, trustworthy (character over résumé)
2.16 Tell us about a time you changed course because of feedback. What is core to your company, and what is tactic you would change? Which gaps do you see in yourself?
Why we ask
Learning and adaptability: learns fast and turns feedback into decisions quickly; data-driven; coachable without losing conviction; knows what is core and what is tactic, and can pivot the latter; early indication of switching roles as the company grows; aware of own gaps
D2 · Team
3.1 How many founders are you, and who covers building, product, selling and domain knowledge? If you are solo, how do you cover the gaps?
Why we ask
Number of founders (solo is possible); functional coverage (building and shipping capability / product / commercial / domain); complementary vs. overlapping skills
3.2 Who is the full-time builder on the team, and do they hold equity? What do they build with, and how deep does their technical skill need to be for this product?
Why we ask
Named, full-time builder with equity (can be the solo founder): owns getting the product built and shipped, with AI tools rather than necessarily writing code by hand; genuine technical depth where the product needs it (e.g., deep tech, robotics)
3.3 How is the equity split between the founders, and how did you decide it?
Why we ask
Equity split and how it was decided
Only if
number of founders > 1 (solo founder: item does not apply)
3.4 How long have the founders known and worked together, and in what setting? Did anyone join shortly before this round, and why?
Why we ask
Team origin: how long and in what context founders worked together; assembled or matched by third parties; co-founder added just before the round, and why; personal relationships between co-founders; co-location
Only if
number of founders > 1 (solo founder: item does not apply)
3.5 Who is CEO, and who has the final say on which topics?
Why we ask
Roles and decision rights; who is CEO
Only if
number of founders > 1 (solo founder: item does not apply)
3.6 What are the vesting terms for the founders (schedule, cliff, what happens if someone leaves)?
Why we ask
Founder vesting (schedule, cliff, leaver terms)
3.7 Has a co-founder left before? What happened, and what is their stake today?
Why we ask
Past co-founder departures
Only if
number of founders > 1 (solo founder: item does not apply)
3.8 Who works with you besides the founders, in which roles and where? How much output do you get per person with AI tools?
Why we ask
Employees/contractors, if any: count, roles, location; early indication of output per head
3.9 Who advises you, and how involved are they?
Why we ask
Advisors: names, involvement
3.10 Which skills or key hires are missing today, and do you already know who could fill them?
Why we ask
Gaps and plan to close them: named key hires, already in network?
3.11 Who on your team is truly in this with you, as opposed to supporting from the side?
Why we ask
True team vs. entourage
3.12 Who have you already convinced to join or back you: co-founders, first hires, advisors, angels? How did you win them?
Why we ask
Early indication of team-building ability / talent magnetism: who they have already attracted (co-founders, first hires, advisors, angels)
3.13 What values do you share as founders? Tell us about your last real disagreement and how you handled it.
Why we ask
Chemistry and values alignment; conflict handling (teams only)
Only if
number of founders > 1 (solo founder: item does not apply)
3.14 What happens to the company if one founder leaves tomorrow?
Why we ask
Key-person dependency and continuity if a founder leaves
Why this?
The problem, the product, the business and the competition.
D4 · Problem and customer
7.1 Who has the problem, how often, and what does it cost them today? How do they solve it now, and what does that workaround cost?
Why we ask
Problem: who has it, how often, what it costs today; current workaround and its cost
7.2 How many potential customers have you spoken to, in which segments, and what did they say? Which quote stayed with you?
Why we ask
Customer discovery: number of conversations, segments, key quotes
7.3 Who uses the product, who pays, and who controls the budget? How does the buying decision happen?
Why we ask
Buyer vs. user vs. budget owner; buying process
7.4 What proof do you have that people want this: waitlist, letters of intent, pilots, pre-orders, inbound requests? Does any of it come from outside your own network, and do you have access to top-tier customers?
Why we ask
Evidence of pull: waitlist, LOIs, pilots, pre-orders, inbound; demand proven outside founders' network; access to top-tier customers
7.5 Is there evidence that customers would buy this rather than build it themselves?
Why we ask
Build vs. buy: evidence target customers would buy rather than build
7.6 Which customers or buyers could we talk to about the pain, what they would pay, and whether the budget is confirmed?
Why we ask
Customer / buyer references where available: pain, willingness to pay, must-have vs. nice-to-have
7.7 How urgent is the problem for your customers? Do they already know they have it, or do you have to educate them first?
Why we ask
Severity and urgency; problem awareness (recognised vs. latent need; education required)
7.8 Who exactly is your ideal first customer, described so sharply that you could name ten of them?
Why we ask
ICP sharpness
7.9 What makes this idea non-obvious? Who tried before, and why did they fail?
Why we ask
Idea quality: non-obvious, avoids "tar pit" categories; history of prior attempts and why they failed
D5 · Market and timing
8.1 How large is the market? Please show the maths bottom-up (customers times price) and top-down with sources, including the labour or services spend you could address, not only software spend.
Why we ask
Market size: bottom-up (customers × price) and top-down with sources; labour/services spend addressable, not only software spend
8.2 Which segment do you start with, how do you expand from there, and how many buyers can you reach? How much room do you have to change direction within your target customers?
Why we ask
Beachhead segment, expansion path; number of addressable buyers and room to pivot within the ICP
8.3 How is the market structured: is it growing, how fragmented is it, who sits where in the value chain?
Why we ask
Market structure: growth, fragmentation, value chain
8.4 How capital-intensive or regulated is this market?
Why we ask
Capital and regulatory intensity
8.8 Would someone independent from this industry call the market attractive? Could a small market here become large?
Why we ask
Market attractiveness; can a small market become large
8.9 How would you scale beyond your home market, within Europe and into the US?
Why we ask
Geographic scalability (EU fragmentation, US path)
8.10 What do you believe about this market that most people, including most investors, do not believe yet? What is your evidence?
Why we ask
Contrarian insight: do the founders see something about this market that most people, incl. other investors, don't believe yet, and is there evidence they are right? Outlier outcomes usually come from bets that look wrong to the consensus at the time
D6 · Product and AI
9.1 Can we try a working demo or the product itself?
Why we ask
Working demo / product access
9.2 What is the architecture and stack, and which third-party services does the product depend on?
Why we ask
Architecture and stack; third-party dependencies
9.3 Who built the product: the founders, freelancers or an agency?
Why we ask
Who built it (founders / freelancers / agency)
9.4 How long did it take to get to the first product and the first user? How often do you release?
Why we ask
Shipping speed: time to first product / first user; release cadence where a product exists
9.5 What will you build in the next 6 to 18 months?
Why we ask
Roadmap 6–18 months
9.6 If the product is live: how deeply is it used? If it uses AI, how often are its outputs accepted, and what share runs without human help?
Why we ask
Product KPIs where a product is live (usage depth; for AI: completion/acceptance rate, autonomy share)
Only if
stage in [live, paying], or a working product exists (9.1)
9.7 How do you handle security, data and GDPR basics?
Why we ask
Security, data handling, GDPR basics
9.8 What is the technical risk: a science problem (does it work at all) or an engineering problem (can you build it well)? For hardware or deep tech, at what readiness level are you, and could an independent expert take a look?
Why we ask
Technical feasibility: science vs. engineering risk; TRL for hardware/deeptech
9.9 How well does the product fit into your users' daily work, and what do users love about it?
Why we ask
Product quality and workflow fit; user delight
9.10 How hard would it be to rebuild the product or change direction technically? Are you locked in by hardware or capital spending?
Why we ask
Technical lock-in: rebuild / tech-debt risk; ability to pivot vs. hardware/capex lock-in
10.1 Which AI models does your product rely on, and how easily could you switch? What does the AI cost per task or per customer?
Why we ask
Model dependency: which AI model providers the product relies on (e.g., OpenAI, Anthropic) and how easily it could switch; early indication of what running the AI costs per task or per customer (model usage fees), which drives gross margin
10.2 Do you hold proprietary data or feedback loops? Do you have the rights to the data you use (copyright, scraping, licences)?
Why we ask
Proprietary data and feedback loops; rights to source data (copyright, scraping, licensing)
10.3 Where do humans still sit in the loop, and how much of the work do they do?
Why we ask
Human-in-the-loop share and where it sits
10.4 Does the product get better the more it is used, or does every customer start from scratch? In 18 months, will growth come mainly from the system learning or from hiring more people?
Why we ask
Compounding: does the product get better the more it is used (data and feedback loops), or does every customer start from scratch? In 18 months, will growth come mainly from the system improving (learning loops) or from hiring more people to deliver (headcount loops)?
10.6 If a strong team with about 2 million euros and three months rebuilt everything you add on top of the AI models, how close would they get?
Why we ask
Copy test: if a strong team with ~€2M and 3 months tried to rebuild everything this company adds on top of the AI models (product, data, workflow, integrations, customer relationships), how close would they get? If very close, there is little defensibility beyond speed
10.7 Do you sell a finished result that customers would otherwise pay people for, or a tool they use themselves? Which budget pays, and how do you price it?
Why we ask
Selling work vs. selling tools: does the company sell a finished result the customer would otherwise pay people for (e.g., completed tax filings, resolved support tickets), paid from labour/services budgets and often priced per outcome? Or a tool people use to do the work themselves, paid from much smaller software budgets? Selling work usually means bigger budgets and benefits from model improvements. Selling tools carries the risk of competing with the AI models themselves
D7 · Traction and business model
11.1 If you have revenue: what did you earn each month, how is it split between recurring, usage, pilot, services and one-off, and how does it tie to your bank and your books?
Why we ask
Revenue, if any: monthly history (not cumulative, not GMV) and growth rate; split into recurring / usage / pilot / services / one-off; metric definitions
Only if
stage in [paying]
11.2 Which experiments and channels did not work, and what did you learn?
Why we ask
Failed experiments and channels; learnings
11.3 Do customers keep coming back out of lasting need, or did they mainly try you out of curiosity?
Why we ask
Durable vs. experimental ("vibe") revenue: lasting customer behaviour or curiosity spend?
Only if
stage in [paying]
11.4 Relative to the time and money you have spent, how good is the traction you have got, in your own view?
Why we ask
Traction quality relative to time and money spent
11.5 How many customers pay and how many are only pilots? Who is your largest customer, what is the contract worth, and how concentrated is your revenue?
Why we ask
Customers: paying customers vs. pilots; largest customer and contract value; customer concentration
Only if
customer type includes B2B and stage in [live, paying]
11.6 Which deals are in your pipeline and at what stage? How many pilots turn into paying customers?
Why we ask
Pipeline by stage; pilot-to-paid conversion
Only if
customer type includes B2B and stage in [live, paying]
11.7 Have customers renewed, expanded or introduced other customers?
Why we ask
Early signs of expansion, renewal and champions introducing other customers
Only if
customer type includes B2B and stage in [live, paying]
11.8 How many people use the product actively, and what share pays?
Why we ask
Users: active users; share paying / conversion to paid
Only if
customer type includes B2C and stage in [live, paying]
11.9 How many users are still active after three months, and how often do they use the product?
Why we ask
Early retention and engagement: cohorts (M3-rebased), daily/monthly active use, usage frequency
Only if
customer type includes B2C and stage in [live, paying]
11.10 How many users find you through word of mouth or referrals?
Why we ask
Early signs of word of mouth: share of users coming organically or via referrals
Only if
customer type includes B2C and stage in [live, paying]
11.11 What is your transaction volume and your take rate? How often does a request on one side get matched on the other?
Why we ask
Transaction volume (GMV) and take rate; match rate / liquidity per market
Only if
business-model type includes marketplace / two-sided and stage in [live, paying]
11.12 Do both sides, supply and demand, come back and repeat?
Why we ask
Retention and repeat use on each side (supply and demand)
Only if
business-model type includes marketplace / two-sided and stage in [live, paying]
12.1 How do you charge (per seat, usage, workflow or outcome), and how does your price compare with the alternatives?
Why we ask
Revenue model and pricing (seat, usage, workflow, outcome); price point vs. alternatives
12.2 What is your gross margin, including AI and compute costs, and where is it heading? What does it cost to win a customer, and how fast does that pay back?
Why we ask
Unit-economics sense-check: gross margin incl. AI/compute costs and its trajectory; cost to acquire a customer and payback; contribution margin
12.3 How much room do you have to raise prices, and how do you see the model scaling?
Why we ask
Pricing power and scalability: early indication, and founders' view of how the model scales
12.5 If you started with services or pilots, how will you move to a product?
Why we ask
Path from services or pilots to product
12.6 How big are your contracts, how long does a sale take, and who signs from which budget?
Why we ask
Early indication of contract size and sales cycle; who signs and from which budget
Only if
customer type includes B2B
12.7 How many users convert to paying, how much does each pay, and how often do they buy again? For physical products, what margin is left after returns?
Why we ask
Early indication of conversion to paid, revenue per user and repeat purchase; for physical products, margin after returns
Only if
customer type includes B2C
12.8 What is your take rate, and which side pays? What stops buyers and sellers from going around you?
Why we ask
Take rate and which side pays; risk that buyers and sellers bypass the platform
Only if
business-model type includes marketplace / two-sided
D8 · Go-to-market
13.1 How did your current customers find you, and which channels have you tried with which results?
Why we ask
How current customers were acquired; channels tested and results
13.2 Which platforms, app stores or partners do you depend on to reach customers?
Why we ask
Distribution dependencies (app stores, platforms, partners)
13.4 What is your go-to-market plan for the next 12 to 18 months, and what do you need to make it work?
Why we ask
GTM plan realism for next 12–18 months
13.5 How do you sell: founder-led, product-led, enterprise or through partners? Do you have access to the people who decide?
Why we ask
Sales motion: founder-led / product-led / enterprise / channel partners; access to decision-makers
Only if
customer type includes B2B
13.6 Which paid channels have you tested, and what did a user cost? Do you have a brand, community or content engine?
Why we ask
Consumer acquisition: paid channels tested and cost per user; brand, community or content engine
Only if
customer type includes B2C
13.7 How do you get enough supply and demand going at the start, and which side is harder?
Why we ask
Cold-start plan: how they get enough supply and demand going at the start; which side is the bottleneck
Only if
business-model type includes marketplace / two-sided
D9 · Competition
14.1 Who else tackles this problem: direct competitors, indirect ones, big incumbents, open source, and simply doing nothing? What do you know about their funding, traction and prices?
Why we ask
Competitor map: direct, indirect, incumbents, open source, do-nothing; their funding, traction, pricing
14.2 What makes you different, and what do you understand that your competitors do not?
Why we ask
Differentiation: stated, and what founders understand that competitors don't
14.3 How hard is it to copy what you do? Could it become a feature of a bigger company instead of a company of its own?
Why we ask
Differentiation vs. defensibility: hard to copy? standalone company or feature absorbable by incumbents?
14.4 What protects you today, and how do you plan to build that over time (speed, data, network effects, switching costs, brand, IP, regulation or something else)?
Why we ask
Moat: early indication today, and founders' view of how it builds over time: speed, workflow/system of record, data, network effects, switching costs, brand, scale, counter-positioning, IP, regulation
14.5 Would a big incumbent want to do what you do, and could they easily do it?
Why we ask
Incumbent threat: awareness × bias to act × adjacency
14.6 How do you win against better-funded competitors?
Why we ask
Right to win against better-funded players
Why now?
Why this is the moment. Being early can be right; being late rarely is.
D5 · Market and timing
8.5 What has changed recently (technology, regulation, behaviour, cost) that makes this possible now? How mature is the enabling technology?
Why we ask
Why now: the enabling shift (tech, regulation, behaviour, cost); maturity of enabling technology
8.6 How long will the demand behind your business last? Could you be too late, and is your buyer's own industry being disrupted?
Why we ask
Timing risk: durability of the demand driver; "too late?" (adoption/saturation); is the buyer's own industry being disrupted?
8.7 How far does what AI models can do today exceed what your customers actually use? What holds adoption back?
Why we ask
Diffusion gap: model capability vs. actual adoption in segment
8.11 If you had started two years earlier, would it have worked? If you started two years from now, what would you have missed?
Why we ask
What changed to make this possible now. Being early can be right; being late rarely is.
8.12 What has to be true in 18 months for your timing to be right, and what would tell you early that you are too early or too late?
Why we ask
Timing milestones and early warning signals.
D6 · Product and AI
10.5 What happens to your company when the next AI model arrives: does it help you or replace you? Could model providers or big platforms absorb what you do?
Why we ask
Long vs. short the frontier: next model helps or kills it; absorption risk by model owners/platforms
D8 · Go-to-market
13.3 Building is getting cheap: what is your plan to reach customers better than others can?
Why we ask
Distribution advantage when building is cheap: early indication, and founders' plan to build it
Why is this a good deal?
For investors and for you as founders: terms, ownership and a financing path that works for both.
D7 · Traction and business model
12.4 Why is this a venture-scale business rather than a profitable consultancy or a lifestyle business?
Why we ask
Venture-backability: VC case vs. PE / debt / lifestyle / consultancy
D10 · Round, terms and returns
5.1 What is the legal name and legal form of the company, where is it based, when was it incorporated, and when did you start working on it?
Why we ask
Company: legal name, legal form, seat, incorporation date, date work started
5.2 Where is the company headquartered, where do the founders live and where does the team sit?
Why we ask
Locations: HQ, founders' residence, team locations
5.3 How would you describe the company in one sentence?
Why we ask
One-liner in founder's words
5.4 What sector are you in? Who pays you (businesses, consumers, public sector), how do you earn money, and where are you today: idea, prototype, live product or paying customers?
Why we ask
Sector; customer type (B2B/B2C/B2G); business-model type; stage (idea / prototype / live / paying)
5.5 Who introduced you to us, or how did you find us?
Why we ask
Sourcing channel and named referrer
5.6 Which materials can you share with us: deck, demo, data room, financial model?
Why we ask
Materials (deck version, demo, data room, model)
5.7 Are you raising right now? How much, in what form, by when, who leads, who has committed and which approvals are still pending? Is this your first-ever financing, or is someone already in at a lower valuation?
Why we ask
Current round: amount, instrument, timeline, lead status; committed investors; pending approvals
6.5 What do you want from us beyond money, and where could we help you most?
Why we ask
Value-add beyond capital: what founders want from us specifically
15.1 How much cash do you have, what is your monthly burn, and how many months does that last?
Why we ask
Cash in bank; monthly burn; runway
15.2 Do you have debts or shareholder loans?
Why we ask
Liabilities and shareholder loans, if any
15.3 What is your financial plan, and which assumptions does it rest on?
Why we ask
Financial plan and key assumptions
15.4 What will you use the money for? What do you need to have shown in 12 to 18 months to raise the next round, and can this round get you there?
Why we ask
Use of funds; milestones for next round (12–18 months); achievable with this round?
15.5 Have you received or applied for grants or subsidies, and are there conditions or clawbacks?
Why we ask
Grants/subsidies received or pending; conditions, clawbacks
15.6 How much money do you need before you see the first decisive market signal, and what is the cheapest way to test it?
Why we ask
Capital needed before the first decisive market signal; cheapest test possible before raising
15.7 Who would invest in your next round, and do they want this category? What is your plan if it takes longer?
Why we ask
Financing risk: next-round realism, bridge scenarios; expected next-round investor type and whether they want this category
15.8 What have you achieved per euro and per person so far?
Why we ask
Early indication of capital efficiency: what they achieved per euro and per head so far
15.9 What financing story do you want to build: how many rounds, from whom, towards which outcome? Do your timeline and your investors' timeline fit over the next five to ten years?
Why we ask
A financing path where the founders' and the investors' time horizons fit.
16.1 What instrument are you offering, how large is the round, and at what valuation (pre-money, cap, discount)?
Why we ask
Instrument; round size; pre-money / cap / discount
Only if
a financing round is in progress or planned
16.2 Who leads and who else invests (funds, angels), and how much is allocated to whom?
Why we ask
Lead, co-investors, angels (quality and relevance); allocation
Only if
a financing round is in progress or planned
16.3 How was the valuation set: by the lead or by your own ask? What is the lead's fund focus and stage?
Why we ask
How valuation was set (lead-validated vs. founder ask); lead's fund cycle and core strategy
Only if
a financing round is in progress or planned
16.4 Which terms do you plan for the round: tranches, tiers, minimum round size, named lead, deadlines from signing, a fallback if the round fails, and which investor rights?
Only if
a financing round is in progress or planned
16.8 Are the terms of this round good for you as well? Is the valuation one you can grow into by the next round: not so low that you give away too much now, not so high that the next round gets hard?
Why we ask
A valuation with room to grow into the next round, and founders who keep enough of their company.
16.9 Does the composition of this round (who invests, on which terms) make your next financing easier or harder? Is there anything in it a later investor would want removed?
Why we ask
A clean round that does not block the next one.
17.1 Please share your full cap table with all shareholders and their stakes.
Why we ask
Fully diluted cap table: all shareholders and their stakes
17.2 Has anyone been promised equity without paperwork? Are there former or inactive holders, or side deals with former co-founders?
Why we ask
Hidden or dead equity: promised but undocumented; departed or inactive holders; side deals with former co-founders
17.3 Do accelerators, company builders, institutes, advisors or agencies hold equity? What share, what did they give for it, and what must they deliver?
Why we ask
Non-founder strategic stakes (accelerator, company builder, institute, advisors, agencies): %, consideration, deliverables, vesting and KPIs over the full vesting period
17.4 How much will the founders own after this round, and after a Series A?
Why we ask
Founder ownership post-round and projected through Series A
17.5 Have you already given away too much of the company, too early, or to the wrong people?
Why we ask
A cap table that still works for founders and future investors.
18.1 Is there anything legal we should know: claims or litigation, non-competes or earn-outs from past employers or buyers, rules you bend today, or payments to companies owned by founders?
Why we ask
Legal red flags, if any: litigation or claims; founder obligations to former employers or buyers (non-competes, earn-outs); known rule-bending and the plan to fix it; payments flowing to founder-owned entities
18.2 Who owns the key IP, and does any university or former employer have a claim? Who owns the IP and data created in customer projects?
Why we ask
IP ownership, where IP matters: key IP and who owns it; university or former-employer claims; who owns IP and data created in customer projects
Only if
IP matters for this case (e.g. IP named as moat in 14.4, deep tech or hardware, university spin-out)
18.3 Is your structure unusual: a holding company on top, a non-EU entity, tax implications?
Why we ask
Unusual structure, if any: holding vs. operating company, non-EU jurisdiction
18.4 What might you be forgetting? What would a careful investor, or you yourself in two years, wish you had thought about now?
Why we ask
Founders who look for their own blind spots.
19.1 Which licences, certifications or approvals do you need? Do you have them, or what are the path, time and cost?
Why we ask
Licences, certifications and approvals required (e.g., financial licence, medical device class, CE): held, or path, timeline and cost
19.2 If you use AI: how does the EU AI Act classify your product?
Why we ask
EU AI Act classification (if applicable)
Only if
product is AI-based (section 10 applies)
19.3 What evidence supports your claims about health, efficacy, performance or impact?
Why we ask
Evidence behind product claims (health, efficacy, performance, impact)
19.4 What liability do you take on for the output you deliver, such as AI-delivered professional services?
Why we ask
Liability assumed for delivered output (e.g., AI-delivered professional services)
19.5 Could your product cause harm to society, such as screen-time or content moderation problems, and how do you handle that?
Why we ask
Product societal risk (e.g., screen time, content moderation)