6 questions to ask before you choose your energy due diligence platform

Want to ensure your investment in an energy infrastructure due diligence platform is well-placed? Here’s your guide to the 6 key questions you must ask.

As transaction volumes rise and energy infrastructure deals become increasingly complex, traditional due diligence processes are struggling to keep pace. In a buyer’s market, rich in opportunities, investment teams are overstretched with the result that early-stage screening and non-binding offer (NBO) decisions are being made based on limited intelligence. At the same time, developers are competing for capital and therefore want to reduce the time it takes to prove their project is investment-ready and bankable. With AI, all these challenges can be easily overcome - helping developers and investors analyse more information, move faster and make better decisions.

But the market is already becoming increasingly crowded. Deal teams on the buy and sell-side have to weigh up a growing number of options, ranging from general-purpose AI tools adapted in house, established software providers adding AI capabilities to existing products, and a new generation of AI-native platforms built specifically for the challenges of complex energy infrastructure transactions.

Choosing the right solution requires looking beyond a list of features. From understanding the expertise behind the platform to ensuring sensitive deal information remains secure and that outputs can be trusted, asking the right questions is essential to getting maximum value from your investment.

Our buyer’s guide gives you the 6 key questions to ask when looking for your energy infrastructure M&A due diligence solution.

1. How mature is this provider and how long is this provider likely to be around?

Why this question is important:

With AI having made development of applications, websites and AI generated content very easy, many “startups” have emerged in the space of energy due diligence claiming to be able to do a very wide range of things, often with only 1 to a handful of people and many with no meaningful funding raised.

This can on the surface feel powerful as AI is able to generate what feels like a highly functional user interface and reports, but is that then any better than your in-house AI subscription?

Understanding your provider’s financial stability and longevity is also a key part of this. Due diligence software may hold critical deal data and become embedded in your processes. Using a solution from a very early stage company with no substantial funding is much more likely to mean that they won’t be around in 12 months’ time vs. a player with significant funding and backers, who can keep supporting and expanding their platform in the long run.

What is the size of your team? How long have you been doing this? How much funding did you raise? How long is your runway?

Veridue’s answer:

Veridue is the most mature AI-native player in this space in Europe, whether that is funding, company size, years of industry and deal experience on the team.

Veridue raised $4M - by far the largest funding by any company in this space - that is 2x and 5x more than the next biggest startups offering similar solutions in Europe.

Thanks to all that, Veridue is the long-term partner of choice for funds, IPPs, utilities and developers. We love seeing every customer relationship as such, and our customers are incredibly excited about everything we will be launching in the next 6-12 months.

2. How much of the deal and asset lifecycle does the platform cover, or is it a point solution?

Why this question is important:

Many AI solutions in the market promise to help customers improve one aspect of their workflow, like siting, or deal sourcing, or screening, or due diligence, or preparing an investment committee (IC) memo or maintenance. (Though also watch out for the opposite; providers that promise to do it all with a ~2 person team.)

Established data room providers may also add very basic AI functionality to their existing products, while portfolio monitoring software focuses on what happens after an asset has been acquired.

The key question is whether you are getting a specific solution for one part of the workflow or an integrated platform that provides value across the wider deal and even asset life cycle. Using multiple disconnected tools can create silos, require teams to move information between platforms and make collaboration more difficult.

A more integrated approach can reduce the number of platforms a business needs to procure and manage, while giving deal, development and asset management teams a shared, AI-native, single source of truth. This makes adoption easier and multiplies the benefits.

Veridue’s answer:

Veridue is designed as an integrated solution spanning the entire deal lifecycle, with broader asset lifecycle functionality also coming soon to the platform.

In a single platform, we incorporate everything from managing the deal pipeline, deal screening, due diligence, automated Q&A flows, VDR and beyond - replacing the need for separate providers. Teams can collaborate on the same platform, with legal, technical, commercial, financial and tax advisers able to access the relevant information and tasks. Instead of coordinating through Excel trackers, fragmented email threads and static 100-page reports, deal teams can collaborate directly, share evidence, resolve questions and outstanding tasks, and co-author diligence reports in one place.

The platform can also be accessed outside of its core interface. For example, via email, users can interact with Veridue directly, allowing information and attachments from conversations to be added to a deal pipeline, priorities to be identified and follow-up emails to be drafted.

The value goes far beyond providing PDF due diligence reports in hours instead of weeks. We also give deal teams a shared, AI-native workspace that helps them move faster while maintaining the rigour required for high-stakes infrastructure investment decisions.

Veridue is already the provider with the most all-encompassing offering, and that gap is only going to grow - thanks to our significantly larger funding. We will soon be releasing additional AI-native solutions specifically for the various lifecycle stages of energy infrastructure - stay tuned and reach out to tell us what would help you the most.

3. What is the underlying technology layer, and why can I trust the output?

Why this question is important:

Not all AI is built for the accuracy, consistency and traceability required in M&A due diligence.

1. a) Buyers should ask whether the platform can ground every finding in the original source material, rather than producing a black-box answer that cannot be verified.
2. b) Buyers should ask how hallucinations are avoided, whether the technology can reliably assess an entire data room without missing documents or information, and what the largest data room it has successfully processed was. General-purpose AI tools have context-window limits and limits on how long they can think for, meaning they cannot necessarily process every document and may analyse only a subset of the available evidence without making that clear to the user. In energy infrastructure deals documentation can run into 1,000s of files and 10s of GBs of data. Such large data rooms require purpose-built orchestration across documents, reliable retrieval and coverage checks to confirm that the complete dataset has been assessed and assessed thoroughly. Simply pointing a general-purpose AI tool such as Copilot, Claude or ChatGPT at a folder of files cannot provide that assurance, creating a risk that important documents, dependencies or red flags are missed.
3. c) LLMs are also probabilistic in nature. If providers rely too heavily on off-the-shelf models, which risks get evaluated can become a matter of chance. The assessment can lack consistency, comparability and certainty that all important risk factors have been checked.

Finally, buyers should ask whether the technology is genuinely purpose-built for their sector and workflow. Energy infrastructure transactions involve highly specific risks, contractual terms and requirements that vary by technology and jurisdiction. A generic AI tool - such as Claude or ChatGPT - may be able to read documents, but that does not necessarily mean it knows what to look for. Even M&A software tools that are sector agnostic lack the specific technological, country or regional expertise that renewables diligence requires. What a renewables diligence platform needs is practical transaction and development knowledge codified into its ontologies and properly tested.

Veridue’s answer:

Veridue is designed to avoid AI hallucination, with every finding fully traceable to original, verifiable source material. This gives deal teams an auditable evidence trail rather than a black box answer. If the system cannot find information, it does not invent an answer; it identifies, and flags, that there is insufficient data.

Veridue is also built to ensure that not a single document or piece of information is missed. This is why our AI runs for hours, while your chat-based AI runs for minutes at most.

Veridue is not simply a chatbot pointed at a data room. It is AI trained on a proprietary dataset of real deals and their diligence outcomes, running on a purpose-built agent layer to ensure results are deterministic with 100% traceable accuracy. Missing even one document can skew an entire diligence - we have built a rigorous system to track and analyse every document reliably, even across the industry’s largest data rooms.

Veridue’s proprietary architecture combines deterministic and autonomous layers, with thousands of technology-, jurisdiction- and region-specific risk factors, required documents and contractual terms built into the system. Structured checks define what needs to be assessed, while coordinated AI agents analyse the evidence. This creates a more consistent process than relying solely on an LLM, where the same prompt can produce different outputs and potentially assess different risks each time.

Unlike other players, Veridue can do this, thanks to our scale, funding and expertise. Our library of risk factors has been developed by our exceptional in-house deal experts in collaboration with top energy M&A law firms that have an in-depth understanding of each jurisdiction and its nuances. This also enables Veridue to deliver comprehensive, consistent, and comparable outputs – combining the flexibility of AI with the structured approach required for high-stakes investment decisions.

Veridue also has the unique advantage of being trained on a proprietary dataset of real renewable energy deals and their diligence outcomes. We secured contractual permission to train and evaluate our AI on these transactions, enabling us to offer an exceptionally powerful solution while never training on customer data. This separation also helps preserve our impartiality, as one customer’s data or preferences cannot influence the analysis delivered to another.

Finally, we combine the best of AI - the latest models and the best one for a given subtask - with the best of human expertise. Every due diligence is reviewed by a top human expert before it goes to our customers, ensuring that layer of human judgement. This combines AI output with practical deal experience and human judgement that our customers can trust.

4. What experience and credibility does your team have?

Why this question is important:

The next generation of energy infrastructure M&A platforms will not be built on AI alone. They will combine the power of the most advanced AI models with deep transaction experience, commercial and technical judgement, legal expertise and specialist energy-sector knowledge. Veridue’s team comprises senior professionals with first-hand experience of energy infrastructure transactions gained as members of investment teams and technical, legal and commercial advisory firms.

Human expertise is particularly important in energy infrastructure, where investment decisions depend on understanding complex project structures, contractual arrangements, regulatory frameworks, counterparties, financing structures and risks that may not be immediately visible in the data.

For an AI-native M&A platform to be trusted with high-value investment decisions, its underlying workflows, analysis and outputs need to be informed and challenged by professionals who have actually executed transactions and carried the associated commercial and fiduciary risk.

So always ask: Who is on your team? Are these technology people, who were looking for a problem to solve, or deep domain experts who have been in my shoes, understand all the details of my industry and went and built a solution? How much prior deal experience do they have, in years, transaction volume, etc.? Which technologies and countries have they actually done deals in? Have they been on the investor side, in an IC, or just advisors? How many of these people do you have? Do they review every AI output - do they have the capacity and are they qualified to do so? Would you trust them with your due diligence if they wrote it themselves?

Veridue’s answer

Veridue was founded by Daniel Csonth and Xander van den Eelaart. Daniel brings more than $10bn of energy M&A experience as an adviser at McKinsey and an investor at Inclusive Prosperity Capital. Xander led AI risk modelling for more than $1 trillion in insured energy-asset value at SCOR, including agentic AI for insurance underwriting and contract review, following earlier experience at Shell.

The team is made up of senior industry professionals with first-hand experience across energy infrastructure transactions, investment, technology, legal advisory and strategic consulting.

The team’s pre-Veridue experience includes:

Daniel Csonth, CEO

• More than $10bn in energy M&A transactions as an investor and adviser across the US, Europe, Latin America and Asia.
• Experience spanning energy investment, due diligence and AI product development.
• McKinsey and Inclusive Prosperity Capital; studied AI and energy at Yale.

Xander van den Eelaart, CTO

• Led AI risk modelling for more than $1 trillion in insured energy-asset value at SCOR.
• Built and deployed agentic AI for energy-asset insurance underwriting and contract review.
• Previous experience at SCOR and Shell.

Ian Crawley, Head of Legal

• 18+ years of M&A and legal diligence experience, including more than £1.8bn of UK energy and infrastructure investments.
• Former Senior Counsel and Associate Director, Infrastructure Equity Origination, at Aviva Investors; previously at IFM Investors and Addleshaw Goddard.
• UK-qualified solicitor since 2007; law graduate of the University of Oxford.

Pietro Baravelli, Head of Legal US and Europe

• Advised on more than $500m of cross-border corporate and M&A transactions across the US, UK and Europe.
• Former associate at Linklaters, Chiomenti and Latham & Watkins, with experience leading cross-border legal teams of 10+ associates.
• Admitted to the Italian Bar; passed the New York Bar exam, with admission pending; Master of Laws from Harvard Law School.

Shaun Youssuf, Technical Due Diligence Lead

• 10+ years in energy engineering and technical advisory, including leading buy-side and vendor due diligence across the UK and Europe, with additional US experience.
• Expertise across solar, BESS, wind, data-centre power, gas generation and waste-to-energy.
• AFRY, Arup and Fichtner Consulting Engineers; MEng in Chemical Engineering from Imperial College London.

Quentin Pletinckx, Commercial Lead

• Advised on 20+ GW of transactions across Southern Europe, covering portfolio optimisation, capital allocation and curtailment.
• Co-built curtailment and flexibility analytics used across 150+ Iberian renewable assets.
• Power-market modelling expert, with extensive work on forward power-price curves at Baringa.

Melchior Villet, Founder’s Associate

• Advised on 5+ GW of energy projects across Northern Europe, covering revenue projections and asset optimisation.
• Previously led operations at Capture Energy, an energy supplier and optimiser of behind-the-meter BESS assets.
• Power-market modelling expert, with extensive work on forward power-price curves at Baringa.

The team also includes experienced software and AI engineers: Sam Burdge, with 20+ years in software engineering and experience leading teams of 40; Thomas Tiotto, a PhD-trained AI engineer who previously worked at Kyndryl and Artificialy; and Justin Fung, who builds the platform’s front-end interfaces and workflows.

This is the most experienced team any AI-native due diligence solution provider in the industry has - and it is this that provides the deepest differentiation, credibility, reliability and trust in the quality of the due diligence.

AI can dramatically accelerate the analysis of complex transaction information. But speed alone does not create investment-grade insight. High-quality investment decisions require professionals who understand what matters, what can go wrong, which assumptions require scrutiny and where the evidence is incomplete or ambiguous.

Experienced commercial, legal, financial and technical professionals help ensure that Veridue’s workflows are designed to:

• identify and prioritise material transaction risks;
• interrogate complex project and contractual structures;
• distinguish signal from noise across large volumes of information;
• challenge assumptions and AI-generated conclusions;
• determine where further evidence or expert review is required; and
• surface issues and opportunities that may not be apparent from the underlying data alone.

Their transaction experience and knowledge of European energy markets also provide an important layer of context, validation and challenge around AI-generated analysis.

This is AI augmenting human expertise by combining advanced models with the judgement, scrutiny and sector knowledge required for investment-grade energy infrastructure M&A.

Veridue AI is being built on that principle: the best of AI, combined with the best of human expertise, to help investors and advisers execute complex transactions with greater speed, diligence rigour and confidence.

5. How is confidentiality and data security handled?

Why this question is important:

For an energy infrastructure M&A platform, confidentiality and data security are fundamental. Due diligence involves highly sensitive commercial, financial and operational information, often while multiple parties are scrutinising a transaction.

Investors and developers need to understand more than whether data is suitably encrypted. Buyers should ask: Who can access the information? Where is it stored? How is access controlled? Is activity logged? Is customer information used to train AI? Does the platform support full EU data residency?

Security should be assessed as an organisational capability, not simply a technical feature. Robust protection requires clear internal policies and procedures, and disciplined processes around how data is accessed, handled and migrated.

Veridue’s answer:

Veridue AI complies with leading data and information security standards and is currently undergoing the audit process for institutional-grade certifications, including ISO 27001 in Europe and SOC 2 Type II in the US. Veridue’s cybersecurity, risk management, and compliance controls are continuously monitored and automated by the Vanta trust management platform used by companies such as Harvey and GitHub. These are the highest levels of certification and are unsurpassed.

Veridue’s customers retain full ownership and control of their data, with clear separation between customers. All data is encrypted while detailed audit logs show exactly what data is being accessed and by whom.

Customers can control who is invited into a deal and set granular permissions around viewing, editing, sharing and downloading. All file-level actions are logged, and documents are also watermarked. The platform supports full EU data residency across its stack - this means your data stays within the EU if you are an EU customer.

Your data is also never used for AI training. That includes the third-party foundation models we use: each provider is contractually barred from training on your content.

We have been in your shoes and know the importance of this - these are high-stakes, confidential deals, and we treat them that way.

Veridue has strict internal policies around data access, deployment and migration, with background checks for new employees. The team’s experience of conducting more than $40 billion in energy M&A due diligence and underwriting $1 trillion in assets also means it understands the commercial and reputational importance of getting data security right. So we have made sure of that.

6. Who are the financial backers of the platform - could it create any conflict of interest?

Why this question is important:

This is an extremely important consideration because of the high potential for conflict of interest in the competitive energy financing and M&A landscape.

Are your provider’s investors linked to companies that could potentially be on the other side of your transaction? Could an investor influence the software provider’s neutrality or future roadmap in a way that benefits other companies you’re competing with?

Veridue’s answer:

At Veridue, independence is a fundamental pillar of our platform. While buyers and sellers see the potential for AI to address process bottlenecks, they often lack a trusted, specialist platform to achieve this safely and effectively. It is important to use a platform that has no cap table conflicts. We have been approached by customers interested in investing in Veridue, but we do not accept any investment that could compromise our independence.

Our institutional investors, Episode 1, HTGF and Pi Labs, are venture capital funds focused on startups. They invest alongside individual angel investors. None has investments that could create competitive conflicts for our customers.

This matters because our customers are understandably concerned about their data protection and proprietary investment strategies. For investors, that can include how they make investment decisions, how they evaluate projects and assess project risk, the deals they have access to, and the developers they have relationships with.

If a competitor gains access to that information, it could create a competitive advantage. That is why Veridue treats independence as a non-negotiable in the way we do business – rather than simply a data security question.

Considering the 6 questions above, would it be better to build an AI solution in-house?

This question deserves a deeper discussion in its own right.

For a closer look at the challenges of building an energy infrastructure M&A AI platform in-house, the eight considerations in the buy-versus-build decision, and why a working prototype is very different from an institutional-grade product, read our Founders’ Q&A here.