Case Study

Document Gap Analysis

We analysed data rooms across wind and solar portfolios. Here’s what we found — and what was missing.

Gautier Moulin

Let me walk you through a real portfolio analysis — what we found, how we found it, and why 15–30% of documents are typically missing.

Gautier Moulin · Co-founder & COO

Background PathsBackground Paths

AI-Powered Document Gap Analysis — a glimpse

Aevy automates
the answer.

A four-step due diligence automation built for onshore wind and utility solar assets. It maps every checklist item to the right document, surfaces the answer, and flags what's missing — all before your first coffee.

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  1. 01

    Upload your checklist

    Each requirement becomes a tracked item. Use your own list or Aevy’s template — no more Excel guesswork.

  2. 02

    Aevy maps the dataroom

    Aevy scans every document in your data room and matches each requirement to its most likely source.

  3. 03

    Answers are extracted & validated

    AI agent Squirrel surfaces quality-assured answers, ready for your review.

  4. 04

    A source-linked report, ready to share

    Every finding links back to the original document, ready to transfer to the next owner.

Inside the Document Gap

The information is there.
It’s just buried.

Every wind farm and solar park comes with a paper trail. Most of it ends up in a data room that nobody has fully read. That’s where the problems hide.

What documents get checked in renewable energy due diligence?

O&M contracts, turbine warranties, grid connection agreements, land leases, environmental permits, insurance policies. That’s the short list. A mid-size wind portfolio easily runs into thousands of files. Miss one expired lease or lapsed permit, and the buyer inherits the problem at closing.

Why manual document review breaks down at scale

According to McKinsey and IDC, professionals spend 1.8 to 2.5 hours per day searching for information they already have somewhere. In renewable DD, that looks like an analyst opening files one by one, matching each against a checklist in Excel, logging findings in a second spreadsheet. 80% of the data is unstructured: PDFs, scanned inspection reports, email threads. None of it is indexed. A single transaction can burn 3 to 5 days on verification alone, before any real analysis starts.

How Aevy automates the gap analysis

Aevy scans the data room and matches checklist requirements to documents based on content, not file names. If a land lease is buried three folders deep under the wrong name, it still gets matched. Its AI agent, Squirrel, extracts the answer from the matched document and flags what’s missing or expired. Every finding links back to the original page, so reviewers click through to verify instead of hunting.

What happens when gaps turn up?

A missing turbine warranty can shift a risk allocation clause. An expired grid agreement can mean re-permitting. These things affect what the asset is worth, and they’re easier to negotiate when you catch them before the SPA. Because Aevy’s output is structured and links back to source documents, asset managers inheriting the portfolio after closing can pick up where the DD team stopped rather than starting a second review from scratch.

Gautier

Dealing with a messy data room? Happy to help.

Gautier·Let’s talk →

Frequently Asked Questions

Common questions about document gap analysis

Document gap analysis is the process of comparing a due diligence checklist against the contents of a data room to identify what’s missing, outdated, or mismatched. In renewable energy transactions, this means checking that every required document — from O&M contracts to grid connection agreements — is present, current, and covers the correct assets.

Aevy’s AI scans every document in a data room and matches each checklist requirement to its most likely source based on content, not just file names. It then extracts answers from those documents, flags gaps, and produces a source-linked report. The whole process runs in hours rather than the 3–5 days a manual review typically takes.

Common gaps include expired or missing O&M contracts, outdated grid connection agreements, incomplete land lease documentation, lapsed permits and compliance certificates, and missing warranty or insurance records. These gaps are often invisible until a structured checklist review is performed against the full data room.

A manual document gap analysis typically takes 3-5 days per transaction, depending on the size of the data room and the complexity of the checklist. With Aevy, the same analysis completes in hours because the matching, extraction, and gap identification are automated.

In a manual review, analysts open each document, check it against the checklist, and record findings in a spreadsheet. This is slow, error-prone, and the work doesn’t transfer between teams. Automated review uses AI to scan, match, and extract across the full data room at once, producing a structured report with source links that any team can pick up and continue.