5 min read How to Diligence a Deeptech Startup
Deeptech investing sits at the frontier of science and innovation where research, engineering, and commercialization intersect. Unlike pure software startups, deeptech ventures build defensible advantages through hard science: novel materials, AI hardware, quantum systems, robotics, medtech, or energy storage breakthroughs.
These opportunities are exciting but also carry unique risks. Traditional venture diligence frameworks often fall short when technology readiness, regulatory hurdles, and manufacturing scalability dominate the risk stack.
This article distills key lessons from Startup Funding Espresso episodes on diligence, technology risk, and commercialization. The goal: to help investors and founders apply a structured, evidence-based approach to evaluating deeptech startups.
Why Deeptech Diligence Is Different
Every deeptech company compresses three risk domains—science, engineering, and market adoption into a single domain. The challenge is not just whether the technology works, but whether it can be built repeatedly, validated independently, and sold at scale.
Traditional diligence focuses on traction and market fit; deeptech diligence begins earlier, with proof-of-concept data, IP defensibility, and regulatory feasibility. Investors must verify not only what exists today but also how quickly risk can be reduced per dollar invested.
Key mindset shifts include:
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From forecasting revenue to mapping technology readiness.
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From tracking KPIs to testing falsifiable hypotheses.
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From scaling growth to scaling evidence.
1. Technology and IP — The Foundation of Value
The first diligence lens is technical validation. Investors must separate scientific promise from engineering reality.
Ask: What has been independently demonstrated, and what remains unproven?
Use a Technology Readiness Level (TRL) framework to map progress:
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TRL 3–5: Lab validation and prototype proof-of-concepts.
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TRL 6–7: Pilot or limited field trials with defined performance metrics.
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TRL 8–9: Qualified production systems ready for deployment.
Request replicable data, third-party lab results, prototype test reports, and failure analyses. Look for structured experimentation and defined “next proof milestones.”
Equally critical is IP defensibility. Strong patent portfolios are not measured by quantity but by claim strength, enforceability, and market coverage. Review patent claims with a freedom-to-operate (FTO) analysis and assess continuation plans.
Green flags: independent validation, multiple claims granted, clear continuation strategy.
Red flags: single provisional filings, overlapping prior art, or reliance on unprotected trade secrets.
2. Regulatory and Standards Pathway
Many deeptech sectors—especially medtech, energy, and advanced materials face heavy regulatory oversight.
Evaluate:
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Applicable standards and certifications (FDA, CE, UL, ISO, ITAR, etc.).
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Defined testing phases, costs, and lead times.
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How compliance and certification are budgeted within the funding round.
A credible startup presents stage-gated plans, design verification, safety testing, and third-party audits with clear contingency budgets. Failing to anticipate certification hurdles can derail commercialization even when performance is strong.
3. Manufacturing and Supply Chain
Deeptech ventures live or die by manufacturing scalability. A prototype in a lab is far from a product on a production line.
Focus diligence on:
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Design for Manufacturability: Has the team engineered around tolerances, yield loss, and cost variability?
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Supplier validation: Are key materials single-sourced or dual-qualified?
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Cost curves: Does the company model BOM reduction as a function of volume?
Unit economics should improve with scale—not degrade under real-world conditions. Review supplier references, pilot production data, and yield reports, along with contingency plans for critical components.
4. Market Validation and Go-to-Market Strategy
Deeptech adoption is slower and more conservative. Market traction is measured by pilots, paid proofs of concept (POCs), and letters of intent (LOIs), not user downloads.
Ask:
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Who are the anchor customers?
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Are there signed pilots with success metrics?
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Do successful pilots convert to purchase orders or rollout commitments?
Look for evidence of market pull rather than push. Strong startups identify a beachhead market, a narrow, high-value initial use case to expand from once technical and regulatory risks are reduced.
5. Business Model and Unit Economics
Deeptech success depends on translating innovation into a sustainable business.
Assess:
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Pricing logic: value-based or cost-plus.
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Service model: warranty, maintenance, support obligations.
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Margin progression: gross margin by production lot and expected BOM reductions.
Validate a clear path to profitability once production stabilizes. Many deeptech companies underestimate hidden costs tooling, certification renewals, and warranty exposure. Confirm how gross margins evolve with scale and how service costs are managed.
6. Team and Execution Capability
Winning deeptech teams blend:
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Scientific depth (core technology and IP creation).
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Engineering discipline (system integration, QA, and reliability).
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Commercial skill (enterprise sales and regulatory navigation).
Assess whether the team balances technical and operational expertise, or knows when to bring in external expertise.
Signs of execution maturity:
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Detailed experiment and pilot schedules.
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Hiring plans tied to risk milestones.
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Advisors or board members with relevant domain expertise.
The best teams demonstrate self-awareness and a plan to close capability gaps as they grow.
7. Financial and Capital Planning
In deeptech, capital efficiency equals risk reduction per dollar spent.
Each round should connect the capital directly to the risk milestones:
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Proof of principle → validated prototype.
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Prototype → regulatory clearance or pilot.
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Pilot → scaled production or recurring revenue.
Ensure the round fully funds the next de-risking event. Partial funding during critical phases, such as certification, can dramatically increase failure risk.
Common Red Flags
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Reliance on anecdotal or non-replicable data.
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Undefined or unrealistic regulatory timelines.
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Missing supplier validation for long-lead components.
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Pilots without success criteria or written commitments.
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IP limited to provisional filings without FTO analysis.
When several appear together, they signal structural risks, not minor gaps.
Clear Green Flags
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Independent third-party validation of test data.
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Detailed regulatory roadmap with time and budget estimates.
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Qualified manufacturing partners and early yield data.
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Paid pilot contracts with expansion pathways.
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Documented BOM and cost-reduction roadmap.
These signals indicate a company transitioning from research to execution, where investors can confidently participate in scaling innovation.
