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Start with a customer problem, not a technology pitch
Deeptech projects often begin with a scientific or engineering capability. To evaluate it as a startup idea, translate that capability into a testable hypothesis about a particular user and buyer: who has what problem, how they handle it today, and why a new solution would be worth adopting.
The U.S. National Science Foundation (NSF) describes customer discovery as a prerequisite for deep-tech ventures because learning about industrial needs can inform both technology development and venture-launch strategy. In its 2025 I-Corps Biennial Report, NSF says teams use direct customer feedback to validate ideas and can pivot when the evidence points elsewhere.
That makes discovery useful even when it weakens your original idea. Finding that a problem is low priority, the buyer is unclear, or adoption is blocked can save months of building toward a market that is not ready—or help you find a better customer segment or use case.
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Define the assumptions you need to test
Before conducting interviews, write down the assumptions behind the idea. Keep the initial customer segment narrow enough that you can find people with relevant experience. Distinguish the person who uses a product from the person who buys it and the person who controls the budget; in some organizations, these are different people.
- Problem: What job, cost, risk, or operational bottleneck are you addressing?
- Current alternative: What do customers use or do now, including manual workarounds?
- Value: What would improve enough to justify changing the current approach?
- Buyer and process: Who can approve a purchase or evaluation, and how does that decision happen?
- Adoption: What integration, regulatory, safety, performance, or proof requirements could prevent use?
- Venture path: Can the business capture value while the technology is still being developed or qualified?
These are questions to investigate, not boxes to check based on founder optimism. NSF’s I-Corps program overview frames discovery around market validation and commercialization, alongside technology development.
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Compare candidate ideas on evidence that matters
If you have more than one possible application for the same technology, compare them using the same practical criteria. This is a decision aid, not an official scoring rubric or a substitute for customer evidence.
| Criterion | What to learn | Why it matters |
|---|---|---|
| Problem severity and urgency | How often the problem occurs, what it costs, and what happens if it remains unsolved. | A technically impressive improvement may not prompt a purchase if the problem is minor or can wait. |
| Current workaround | What customers do now, how much effort or money it takes, and where it falls short. | Existing behavior is a more useful comparison than an abstract claim that a new technology is better. |
| Buyer and budget | Who experiences the problem, who approves a change, and where funding could come from. | A willing user alone may not establish a path to a funded decision. |
| Access to early adopters | Whether you can reach organizations or individuals with the problem and learn from them. | Without access, it is harder to test assumptions or build a credible route to initial deployments. |
| Adoption and proof constraints | Integration work, regulation, safety or performance evidence, procurement steps, and switching costs. | These can determine whether a promising prototype can be used in a real operating environment. |
| Value capture during development | Whether a customer can pay for a pilot, evaluation, service, or staged capability before the full technology is mature. | Deeptech development timelines can make the path from technical progress to sustainable revenue especially important. |
Interview for real behavior and buying context
Talk to people who have direct experience with the problem: potential users, buyers, technical evaluators, procurement staff, and other stakeholders who influence adoption. Ask about recent events and actual workflows rather than inviting reactions to a polished pitch.
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- “Tell me about the last time this problem came up. What happened?”
- “How do you handle it today? What tools, people, or workarounds are involved?”
- “What does the current approach cost in time, money, risk, or missed opportunity?”
- “Who would need to approve a change, and what would they need to see?”
- “What has prevented you from adopting another solution?”
- “What would have to be true for you to test a new approach?”
Listen for specifics: concrete examples, existing budgets, repeated workarounds, decision steps, and the consequences of doing nothing. Compliments, general enthusiasm, or a hypothetical promise to buy are weaker signals than a practical next step. A data-sharing discussion, a technical evaluation, or an introduction to procurement can indicate interest worth following up; none guarantees a purchase.
Turn interview findings into a decision
After each conversation, record what you learned against the assumptions you wrote down. Separate what the person said from your interpretation, and note where evidence is conflicting or still missing. Look across conversations for consistent accounts of a painful problem, identifiable buying authority, accessible early adopters, and credible next steps.
Rank #4
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- Keep the hypothesis when the same important problem and buying context recur across relevant customers, and a plausible adoption path emerges. Continue testing; early evidence is not a sales forecast.
- Change the segment or use case when the technology appears useful but the original customers do not face an urgent problem, cannot buy, or cannot adopt it. Test the new hypothesis directly rather than assuming it will work.
- Revise the technical plan when customers’ requirements expose a mismatch in performance, form factor, integration, cost, or proof. Discovery can help prioritize what to build or validate next.
- Pause or stop when repeated conversations reveal little urgency, no reachable buyer, or no plausible route through adoption constraints. A weak commercial case is a decision-relevant result, not a failure of the interview process.
NSF’s I-Corps participant stories describe teams learning when an idea may lack commercial potential and when a pivot is warranted. The useful outcome is not simply a yes or no; it is a better-grounded choice about what customer, problem, and technical question to pursue next.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use structured programs when they fit your situation
NSF National I-Corps
For eligible U.S. research teams exploring commercialization, NSF I-Corps offers structured customer discovery. The current National Teams applicant page says participants must complete 100 potential-customer interviews during seven weeks. That is a program requirement, not a universal number of interviews that proves demand. The same page lists awards up to $50,000 and a $10,000 participation fee paid from award funds; check the live page for current eligibility and terms before applying.
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NSF’s program overview reports cumulative figures of more than 2,500 teams, nearly 1,400 startups, and $3.16 billion in subsequent funding. Its 2025 Biennial Report reports 685 National I-Corps Teams participating during FY 2023–2024, 52% of participating National I-Corps teams linked to startups since the program began, and $7.01 billion in follow-on funding raised by teams since inception. These are NSF-reported program figures; they do not show that participation alone caused a startup or investment outcome.
DOE Energy I-Corps
For U.S. national laboratory research teams, the U.S. Department of Energy’s Energy I-Corps is a two-month commercialization program focused on value propositions, stakeholder interviews, and market pathways. DOE’s program overview reports 165 teams from 12 national laboratories and more than 11,500 customer-discovery interviews, as well as more than 160 industry mentors. Those are cumulative program figures, not commercial success rates. See DOE’s Energy I-Corps page for audience and program details.
Use a framework or book without mistaking it for evidence
A canvas or guide can help organize assumptions, but it cannot tell you whether customers have the problem or will adopt a solution. Wiley describes Steve Blank and Bob Dorf’s The Startup Owner’s Manual: The Step-By-Step Guide for Building a Great Company as a 608-page Customer Development guide that uses the Business Model Canvas to organize startup hypotheses. It can structure the work; interviews and observed buying behavior must supply the market evidence.
What customer demand can—and cannot—tell you
Customer discovery helps decide whether a problem, buyer, and adoption path merit further investment. It cannot, by itself, prove technical feasibility, establish regulatory clearance, guarantee a purchase, or predict a startup’s odds of success. For deeptech, the strongest early learning connects both sides: what customers need and what technical work would make adoption credible.
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