Deeptech Due Diligence: Expert Q&A Session

Deeptech investing is complex and requires a different approach than other sectors. It involves evaluating technologies rooted in advanced science, assessing long timelines for commercialization, and navigating unique risks. Here's what you need to know:

• Key Challenges: Deeptech ventures face technical uncertainties, scalability issues, and long development periods. Traditional due diligence methods often fall short here.
• Evaluation Frameworks: Tools like Technology Readiness Levels (TRLs) help assess a technology's maturity. A phased approach - covering technical feasibility, market potential, and comprehensive risk analysis - is essential.
• Risk Factors: Investors must evaluate scientific, market, and regulatory risks. These include technical obsolescence, competitive threats, and barriers to adoption.
• Expert Input: Effective due diligence relies on insights from both technical and commercial experts.
• Tools: Platforms like Innovation Lens use data and predictive analytics to identify trends, validate findings, and streamline research.

Deeptech investing is high-risk but offers the potential to support transformative technologies. A structured, milestone-driven approach is critical for success.

Why Deep Tech Startups Need Different VC Due Diligence

Core Frameworks and Best Practices

Deeptech due diligence requires structured methods to tackle its inherent complexity and uncertainty. Successful investors lean on well-established frameworks that break the evaluation process into smaller, more manageable parts while maintaining the rigor needed for high-stakes decisions. These frameworks provide a clear, step-by-step approach to guide the assessment process.

A solid framework is essential for breaking down the risks associated with technology, market dynamics, and operational challenges.

Technology Readiness and Validation Processes

The Technology Readiness Level (TRL) framework, initially created by NASA, is a trusted tool for gauging the development stage of a technology. This nine-level scale starts at TRL 1, where basic principles are observed, and progresses to TRL 9, where a system is proven in an operational environment. Deeptech investors often focus on technologies that have surpassed the early conceptual stages and are showing signs of operational feasibility.

To ensure reliability, key results should be independently validated through third-party laboratories or research institutions. Additionally, performance benchmarks should be compared against existing solutions to confirm measurable improvements in areas like efficiency, cost, speed, or accuracy.

Multi-Stage Due Diligence Approach

Deeptech investors typically follow a phased evaluation process to assess opportunities systematically:

• Phase 1 – Technical Feasibility: This phase involves reviewing research, patents, and identifying any technical obstacles.
• Phase 2 – Market and Commercial Viability: At this stage, analysis focuses on target markets, competitive landscapes, regulatory requirements, and manufacturing scalability to evaluate the business potential.
• Phase 3 – Comprehensive Integration Analysis: The final phase combines technical, commercial, and team assessments. It often includes detailed financial modeling, risk analysis, intellectual property reviews, and sometimes site visits to evaluate facilities and capabilities. Clear exit criteria at each phase help investors avoid wasting time on opportunities that fail to meet critical benchmarks.

Using Expert Consultations

Expert consultations play a pivotal role in deeptech due diligence. A balanced evaluation requires input from both technical and commercial experts.

Technical advisory panels should consist of individuals with expertise in both theoretical principles and practical implementation challenges. These experts might come from fields like scientific research, hardware development, or software engineering, depending on the technology being assessed.

On the commercial side, experts evaluate market potential, regulatory pathways, and strategies for commercialization. By using structured evaluation criteria - such as defined risk assessments and milestones - investors can turn expert feedback into actionable insights.

Tools like Innovation Lens enhance these consultations by leveraging predictive analytics and curated reports. These resources help investors identify the right technical experts and benchmark emerging technologies against broader research trends, strengthening the due diligence process.

Evaluating Risk in Deeptech Investments

When it comes to deeptech investing, risk assessment isn't just a box to check - it's a necessity. The scientific, market, and regulatory uncertainties in this space require a thorough look at technical feasibility, market conditions, and the expertise of the team behind the innovation. Below, we break down how these risks unfold across scientific, market, and operational dimensions.

Scientific and Technical Risks

The first step in evaluating scientific and technical risks is to validate the proof of concept. This ensures the core principles of the technology work as intended and can scale from the lab to full production. However, scaling isn't always straightforward. Factors like material availability, production complexity, and quality control can present significant challenges. If the technology relies on specialized materials or equipment, these hurdles can grow even larger as production ramps up.

Another key concern is the risk of technical obsolescence. In fast-moving fields like quantum computing and artificial intelligence, technologies can become outdated before they even hit the market. Investors need to ask: Does this technology offer sustainable advantages? And could alternative approaches achieve the same results more efficiently?

Market and Regulatory Risks

Technical risks are only part of the story - market and regulatory factors are equally important. Timing is everything in the market. Even the most promising technologies can fail if they launch too early or too late. It’s essential to evaluate market demand, customer readiness, and the infrastructure required for adoption.

Regulatory hurdles add another layer of complexity. For instance, medical devices must navigate the FDA approval process, which can be both time-consuming and expensive depending on the product’s classification. Similarly, autonomous vehicle technologies face evolving regulations at federal, state, and local levels. Energy innovations, on the other hand, often contend with utility standards and environmental regulations.

The competitive landscape is another area to scrutinize. It’s not just about direct competitors - alternative technologies could also render an innovation unnecessary. Established companies with deep resources might develop rival solutions, while entirely different methods could bypass the need for the innovation altogether.

Customer adoption barriers shouldn’t be overlooked either. In industries like healthcare and manufacturing, decision-makers often prefer sticking with proven solutions over adopting untested, cutting-edge alternatives. These challenges need to be factored into realistic projections for market penetration and revenue.

Intellectual Property and Team Evaluation

Strong intellectual property (IP) is critical in deeptech. A thorough evaluation of the patent portfolio is necessary to ensure coverage and enforceability. This includes identifying prior art and potential workarounds. Conducting a freedom-to-operate analysis helps uncover patents that might block commercialization. This process involves searching patent databases, reviewing competitor portfolios, and assessing litigation risks - especially in cases where the technology originates from academic or institutional research.

The expertise of the team behind the innovation is equally important. Beyond academic credentials, it’s crucial to assess their practical experience. Can they scale the technology? Do they have the know-how to navigate complex regulatory pathways? Have they successfully turned scientific breakthroughs into market-ready products before? A proven track record in similar ventures can significantly reduce investment risks.

To manage these risks, targeted strategies are essential. This might mean forming key partnerships, strengthening regulatory expertise, or bringing in experienced industry professionals. The goal isn’t to eliminate risk entirely - that’s impossible - but to manage it effectively while paving the way for transformative innovation.

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Tools and Platforms for Deeptech Due Diligence

When it comes to navigating the intricate world of deeptech investments, advanced tools and platforms are proving indispensable. The sheer complexity of deeptech due diligence has led to the rise of specialized platforms capable of analyzing vast amounts of scientific data to uncover emerging opportunities. These tools are critical for investors aiming to evaluate technologies from multiple angles while keeping up with the rapid pace of research advancements.

By utilizing AI and machine learning, these platforms can sift through patterns in scientific literature, patent filings, and research funding data. This capability not only allows investors to identify promising technologies before they hit the mainstream but also provides a sharper view of the competitive landscape. This digital transformation sets the stage for more advanced predictive and comparative tools, which we'll explore further.

Using Predictive Analytics and Datasets

Predictive analytics is reshaping deeptech investing by processing millions of research papers, patent filings, and funding decisions to uncover trends on the horizon.

Take Innovation Lens, for example. This platform integrates data from sources like PubMed and arXiv, updating weekly to highlight high-potential research areas. By using algorithms to prioritize underexplored fields, it aligns perfectly with the structured due diligence frameworks discussed earlier. In deeptech, where the volume of emerging research can be overwhelming, such systematic prioritization is invaluable.

But the platform doesn’t stop at trend analysis. It goes deeper, analyzing citation patterns and funding decisions to identify fields gaining traction before they attract widespread attention. Unlike approaches that rely on limited samples or anecdotal evidence, platforms like Innovation Lens validate their findings by cross-referencing multiple datasets. This ensures that emerging trends are backed by robust, comprehensive evidence.

Comparing Features of Due Diligence Platforms

The table below highlights how modern platforms like Innovation Lens outshine traditional research methods:

Feature	Innovation Lens	Traditional Research Methods	Impact on Due Diligence
Predictive Analytics	Algorithm-driven trend detection from extensive datasets	Manual reviews of literature and expert opinions	Delivers faster, forward-looking insights
Data Integration	Pulls from multiple databases into one platform	Requires separate searches across different sources	Saves time and enhances research depth
Customization	Offers tailored recommendations based on specific goals	Relies on generic reports that need manual filtering	Provides more relevant insights, reducing noise
Update Frequency	Weekly automated updates on research trends	Quarterly or annual reviews	Keeps investors informed on rapidly changing developments

One standout feature is API access, which allows organizations to seamlessly integrate these insights into their existing workflows. This means due diligence findings can automatically populate investment reports or risk assessments without requiring manual data entry.

Modern platforms also excel in providing customized recommendations. Whether focusing on specific technology sectors, geographic regions, or investment stages, these tools deliver targeted intelligence that aligns with an investor’s strategic priorities.

Another game-changer is their ability to validate findings across multiple data sources. For instance, when assessing a startup, a platform can simultaneously analyze scientific papers, patent landscapes, and funding trends to deliver a comprehensive risk profile.

Practical Strategies for Deeptech Investors

Investing in deeptech requires a tailored approach to navigate its unique challenges, such as long development timelines, technical hurdles, and regulatory complexities. To succeed, investors need strategies that go beyond standard due diligence, focusing on structured funding models and milestone-based progress tracking.

Milestone-Based Funding Strategies

In deeptech, tying funding to specific technical milestones is a smart way to manage risk and maintain accountability throughout the extended development process. One effective method involves using Technology Readiness Level (TRL) benchmarks to guide funding decisions. With this approach, investors release capital in stages, ensuring that each tranche is contingent on the company demonstrating technical feasibility and progress at a given TRL stage.

For instance, early-stage milestones - typically at TRL 3 to 4 - focus on basic feasibility and transitioning to a prototype. These stages often represent lower-risk entry points for investors. Achieving these early milestones not only demonstrates progress but also builds credibility, which can make it easier to secure additional funding in subsequent rounds.

Experts in the field highlight the importance of aligning funding with technical achievements:

"Deep Tech is about constant investment in new hypotheses, refinements, increasing production for a healthy cost structure, expansion, and team growth." - Aleksei Skorik, Founder and CEO, Nanga Agency ^[1]

"The more credibility a project achieves by the time the product is released the easier it will be to organize pilots and set up further distribution." - Aleksei Skorik, Founder and CEO, Nanga Agency ^[1]

Conclusion: Key Takeaways for Deeptech Due Diligence

Deeptech due diligence requires a specialized approach that goes beyond traditional venture capital methods. It combines detailed technical validation with thorough risk assessments, addressing areas like scientific feasibility, regulatory hurdles, intellectual property (IP), and team capabilities. This approach is essential for navigating the extended timelines and intricate challenges often associated with deeptech investments.

One critical tool for this process is Technology Readiness Levels (TRL). These benchmarks help structure investments and track progress by tying funding to specific technical milestones. This method not only ensures accountability but also helps mitigate risks throughout the journey toward commercialization.

Another growing trend in due diligence is the use of predictive analytics and extensive datasets. These tools provide investors with early insights into emerging opportunities, often before they become apparent to the broader market. This early advantage allows for sharper decision-making and a stronger competitive edge.

Given the rapid evolution of the deeptech landscape, staying informed about new trends and maintaining connections with expert networks across various scientific fields is essential. Investors who excel in deeptech due diligence - combining rigorous technical assessments with milestone-based funding strategies - are better positioned to identify and support innovations that could shape the future of technology.

Ultimately, success in deeptech investing hinges on patience, deep expertise, and precise analytical tools. While the risks are high, the potential rewards of supporting transformative technologies make a methodical, milestone-driven approach indispensable. By integrating these strategies, investors can ensure they are backing groundbreaking innovations with clarity and precision.

FAQs

How does due diligence for deeptech investments differ from traditional venture capital due diligence?

Deeptech due diligence dives deeper than just analyzing business basics - it zeroes in on the science and technology driving the innovation. This means looking at how feasible and scalable the technology is, how original it might be, and what kind of market influence it could have.

On the other hand, traditional venture capital due diligence tends to focus more on areas like financial metrics, market potential, legal considerations, and the capabilities of the founding team. While both methods aim to gauge risk and opportunity, deeptech demands a more tailored approach to address the distinct challenges and extended timelines tied to scientific advancements.

How does predictive analytics improve the deeptech due diligence process, and what key benefits does it provide?

Predictive analytics is transforming how due diligence is conducted in the deeptech sector. By using advanced data models and algorithms, it helps forecast outcomes and spot trends, giving investors a clearer lens through which to evaluate emerging technologies. This approach provides a more precise understanding of risks, market potential, and scalability, enabling smarter decision-making.

Here’s why predictive analytics stands out:

• Risk Mitigation: It identifies potential challenges and red flags early, reducing the chances of costly missteps.
• Efficiency: Automated data analysis streamlines the process, saving both time and resources while speeding up decision-making.
• Sharper Forecasting: It paints a more detailed picture of future market opportunities, making it easier to pinpoint high-growth investments.

By weaving predictive analytics into the due diligence process, investors can better handle the complexities of deeptech innovation and base their decisions on solid, data-backed insights.

Why is milestone-based funding important in deeptech investments, and how does it help mitigate risks?

Milestone-based funding plays a key role in deeptech investments by linking capital to specific, measurable achievements. This method ensures that funding is distributed in stages, based on progress in areas such as advancements in technology, product development, or proving market demand.

Tying funding to clear objectives helps investors manage risk, promote accountability, and ensure resources are allocated wisely. It also provides the flexibility to adjust strategies if obstacles emerge, making it an effective way to address the unique challenges of deeptech ventures.