AI Due Diligence Software for M&A: 6 Tools Compared

This is software evaluation and industry analysis, not legal advice. We do not sell any of these tools. One disclosure: Harvey holds a featured listing in our directory, which is labelled and has no effect on how it is described or ranked here.

Which parts of due diligence does AI handle well?

Due diligence is not one task. A deal team runs four distinct jobs, and AI is good at them in very different degrees.

Ingest. Getting a data room into a reviewable state: unpacking folders, reading scanned PDFs, sorting documents into contract types, spotting duplicates and missing exhibits. This is the least glamorous stage and the one where machines are most reliably better than people, because the work is mechanical and the cost of a miss is visible (a document that never loaded is easy to find).

Extract. Pulling specific provisions out of each document: change of control, assignment, exclusivity, term and termination, indemnity caps, governing law. This is the original use case for contract AI and it still carries most of the value. A reviewer asks the same fifteen questions of three hundred agreements, and a tool answers them in a grid with a link back to the clause.

Flag. Deciding which extracted answers matter. This is where the tools diverge most. A pure extraction engine tells you what a clause says. A flagging layer compares it to a standard or a playbook and tells you it is unusual. The second is more useful and more dangerous, because a wrong flag looks exactly like a right one.

Report. Turning findings into the thing the client actually receives: a diligence memo, a summary of material contracts, a disclosure schedule. Generative AI has changed this stage the most in the last two years, and it is also the stage where an unverified output does the most damage.

The pattern across all four: AI is dependable on ingest and extraction, useful but needs checking on flagging, and fastest-moving and least proven on reporting. If a vendor demo spends nine-tenths of its time on the report and the polished summary, ask to see the extraction grid on your own documents instead.

How do extraction tools differ from general legal AI platforms?

This is the distinction that decides most purchases, and it is easy to miss because every vendor now describes itself as a platform.

Extraction-first tools were built around the grid. You load a set of contracts, you define or pick fields, and the system fills a table. Litera's Kira is the clearest example: Litera's own review page describes 1,400+ pre-built smart fields plus fields you train yourself, review grids for M&A, real estate, banking and finance, and answers that link back to the source contract. Extraction-first tools are narrow, and the narrowness is a feature. The output is structured, comparable across documents and easy to audit.

Deal-first tools are built around the transaction rather than the contract. Marveri, per its directory listing, reads a data room and returns cited findings and disclosure schedules without needing to be prompted, aimed at deal teams rather than general legal chat. LEGALFLY's Multi Review is described on its own site as reviewing hundreds of documents at once using your playbooks, extracting key terms, flagging risks and surfacing inconsistencies across entire data rooms.

General legal AI platforms started as assistants for research and drafting and added bulk review later. Harvey describes its Vault as a place to securely store, organise and bulk-analyse legal documents, with Workflow Agents running diligence protocols across an agreement set. Legora's Tabular Review is pitched as an AI-powered spreadsheet that extracts insights from thousands of documents, each one cited to its source, with templates a team can reuse.

The trade is breadth against depth. A general platform gives a firm one login for research, drafting, review and diligence, and a diligence workflow is one of many things it does. A deal-first tool does one job and is easier to evaluate precisely because the job is narrow. Neither is better in the abstract. What matters is whether diligence is a recurring core workload for your team or an occasional one.

Which tools are built for transactional teams?

Here are the six, in the order a deal team tends to meet them. Every price line below comes from the tool's directory entry, which we verify against the vendor.

Marveri. Built specifically for M&A, venture financings and corporate transactions, founded in 2023 and based in Cambridge, Massachusetts according to its own structured site data. The pitch is a data room in, cited findings and disclosure schedules out. Its security page is headed "SOC 2 Type II + Zero Training", which is a claim to ask for the report behind. Pricing on request. Directory listing.

LEGALFLY. An in-house legal operating system covering intake, contract review, drafting, due diligence and research, with client-data anonymisation built into each workflow and a Word add-in. Its due diligence capability is Multi Review. It is aimed at in-house teams rather than law firms, which is worth knowing if you are outside counsel. Pricing on request. Directory listing.

Litera. A long-established document and workflow suite whose contract analysis product, Kira, is the extraction engine many law firms already know. Litera now also offers Lito, an AI layer across Microsoft 365. Per Litera's review page, a Lito diligence report works from the Kira extraction report, and the Lito diligence capability is included for Kira customers at no additional charge. The same page states 95%+ precision on target provisions. That is a vendor figure, and it should be tested rather than quoted. Pricing on request. Directory listing.

Harvey. A broad platform for law firms and in-house teams covering research, drafting and document analysis, with agents built for specific practice areas. Diligence runs through Vault and Workflow Agents. Harvey's own diligence guide recommends asking any platform you evaluate to demonstrate citation grounding on a real document set rather than a curated demo, which is good advice and worth taking from anyone, including Harvey. Pricing on request. Directory listing.

Legora. A collaborative AI workspace with agentic research, a Word add-in and Tabular Review, the module most relevant here. Founded in Stockholm as Leya and rebranded in 2025. Pricing on request. Directory listing.

Luminance. Contract AI sold as a platform of modules, where the diligence-relevant one is Analyze, built for insight across an existing repository of contracts. It is sold on document volume and a multi-year term rather than by seat, which we break down in our Luminance pricing analysis. Pricing on request. Directory listing.

For the broader field of contract review products beyond transactions, see AI contract review software compared, and for the three biggest general platforms head to head, Harvey vs CoCounsel vs Legora.

Which tool does which stage?

This is the matrix. Treat it as a map of what each vendor says it offers, not a performance ranking: we have not benchmarked any of these tools, and the symbols record vendor-stated capability. "Strong" means the stage is the product's headline purpose in its own materials. "Present" means a feature exists but is not the centre of the product. A blank means we found no vendor statement covering that stage.

Tool	Ingest	Extract	Flag	Report	Price line
Marveri	Strong (data room in)	Strong	Strong (findings unprompted)	Strong (disclosure schedules)	Pricing on request
LEGALFLY	Present (document system connection)	Strong (Multi Review)	Strong (playbook-driven)	Present	Pricing on request
Litera (Kira, Lito)	Present	Strong (1,400+ fields)	Present	Strong (Lito diligence report)	Pricing on request
Harvey	Present (Vault)	Present	Present (Workflow Agents)	Present	Pricing on request
Legora	Present	Strong (Tabular Review)	Present	Present	Pricing on request
Luminance	Present	Strong (Analyze)	Present	Present	Pricing on request

Three readings of the matrix.

First, the price column is uniform, which is itself the finding. All six are quote-only. You cannot shortlist on price, so you shortlist on stage fit and then compare quotes on the same document volume, user count and term. Our legal AI pricing guide explains why the whole category behaves this way.

Second, only two tools put the full chain from ingest to report at the centre of the product. If your team is a transactional boutique running many deals a year, that is the shortlist. If diligence is one of many uses, the general platforms deserve a look because you may already be buying one for other reasons.

Third, Litera is the one that comes with a track record in this exact job. Kira's lineage is extraction, and the Lito layer sits on top of it. That is a different risk profile from a newer product that does the whole chain with less history behind it. Older is not automatically better, but it does mean there are more firms you can call for a reference.

What still needs lawyer review?

Everything that leaves the building. The right question is not whether to review AI output but where reviewer time is best spent.

Spot-check extraction by sampling, and sample the hard cases. Pull twenty documents at random and one set of the oddest ones: scanned copies, amendments that restate a clause by reference, side letters, foreign-language contracts. Compare the tool's answers to a human read. The failures cluster in the unusual documents, and a clean sample of ordinary contracts tells you little.

Read every flag, and also read a sample of the non-flags. A false positive costs a few minutes. A false negative is the real exposure, and a review process that only looks at what the tool surfaced has no way to find it.

Treat generated narrative as a draft. A summary paragraph reads confidently whether or not it is right. The mitigation is traceability: every statement in a diligence memo should link to a clause in a document, and a reviewer should be able to click through. All six vendors describe some form of citation back to source. Test it on your own data.

Keep the judgment calls human. Whether a change of control provision is acceptable for this buyer, whether an indemnity cap is adequate, whether a customer concentration is a problem. The tool can put the clause in front of you faster. It does not know your client's appetite.

For a sense of why verification discipline matters, Stanford researchers evaluated legal research tools in 2024 and reported that Lexis+ AI and Ask Practical Law AI produced incorrect information more than 17% of the time, and Westlaw's AI-Assisted Research more than 34%. That study measured research tools, not diligence extraction, so it does not tell you how any of the six products above perform on a data room. What it does establish is that vendor-built legal AI can be wrong at rates a reviewer would not guess, which is the reason to run your own test. We cover the consequences of skipping that step in legal AI hallucination and sanctions.

Is AI due diligence software secure for deal data?

A data room contains the most sensitive material a firm touches: unannounced transactions, customer lists, employee data, pending disputes. Security review should come before the demo, not after it.

Ask for the following, in writing:

1. A SOC 2 Type II report, not a badge. A logo on a website is not an audit. SOC 2 is an AICPA attestation framework, and the useful document is the report itself, including its scope and any exceptions. Our explainer on what SOC 2 means for legal AI goes through what to read.
2. A no-training commitment in the contract. Marketing language about not training on your data is not the same as a clause in the agreement.
3. Where data is stored and who can access it. Including subprocessors and support staff.
4. Retention and deletion. What happens to the data room after the deal closes, and whether deletion is verifiable.
5. How the model risks themselves are managed. The NIST AI Risk Management Framework is voluntary guidance rather than a certification, so nobody can be "NIST certified", but a good vendor can describe how it maps and measures its own risks.

LEGALFLY's anonymisation approach, which strips identifying client data inside each workflow, and Marveri's SOC 2 and zero-training headline are both worth probing in a security review rather than accepting at face value. The full question list is in our legal AI security questionnaire, and the wider picture is in legal AI data security.

How should a deal team run a pilot?

A pilot should be short, adversarial and on a closed deal. Here is a structure that works for a team of any size.

1. Pick a closed matter. Use a data room you already diligenced by hand, so you know the right answers. Get client consent and run it inside the vendor's security terms.
2. Write the answer key first. Before touching a tool, list the twenty provisions that mattered on that deal and where they were. This is your yardstick.
3. Run two or three tools on the same set. Same documents, same questions. Do not let a vendor choose the sample.
4. Score by stage. Did ingest load everything? Did extraction find each provision? Which flags were right, wrong, missed? How much editing did the report need? A single overall impression hides which stage failed.
5. Time the human work too. The time that matters is the reviewer's time to verify, not the tool's time to produce. A fast tool with weak traceability can cost more review hours than a slower one with clean citations.
6. Request pricing at your real scale. Ask for a quote on your expected number of data rooms and users, with a 12-month price next to the multi-year price.

Our legal AI procurement checklist and the broader guide to evaluating legal AI cover the commercial side of that last step.

What does the vendor writing about this category leave out?

Most roundups on this topic are written by one of the vendors in them, or by a data room provider. They are useful for feature lists and unreliable for judgments. Three omissions recur.

They rarely say that the tools are quote-only and that an honest comparison of cost is impossible without a request. They rarely distinguish between what a product does in a demo and what it does on a messy production data room. And they rarely say who owns the tool, which matters when this market consolidates quickly. We track those changes in our legal AI acquisitions tracker.

If you want to see every transactional and contract tool we have verified in one place, the directory is the better starting point than any single vendor's list.

FAQ

Can AI do legal due diligence? AI can do large parts of the document work: loading a data room, extracting provisions, comparing them against a standard and drafting a first report. It does not replace a lawyer's judgment about what the findings mean for a specific buyer, and every output that goes to a client needs human review with traceable sources.

What is the best AI for M&A due diligence? There is no single best. Marveri and LEGALFLY are built around the data room and the deal. Litera's Kira has the longest extraction track record. Harvey and Legora make sense when you want one platform for diligence plus research and drafting. Luminance fits best when the job is analysing a large existing contract estate. Pick on the stage you struggle with, then pilot two on a closed deal.

How accurate is AI clause extraction? Vendors publish their own figures, such as Litera's stated 95%+ precision on target provisions, and none of these is independent. Accuracy varies with document quality, the provision and the language. The only figure that counts is the one you measure on your own documents against a hand-built answer key.

Is AI due diligence software secure for deal data? It can be, but you have to verify it. Ask for the SOC 2 Type II report itself, a written no-training commitment, data location and subprocessor details, and a clear retention and deletion policy. Do this before any demo that involves real documents.

How much does AI due diligence software cost? None of the six tools covered here publishes a price. Expect quote-based pricing shaped by document volume, users and contract term. Ask for quotes at your real scale and compare them on the same assumptions.

Compare the tools

Browse the verified transactional and contract review tools in /categories/contract-review, or see the full list at /tools. If you sell a diligence product we have not listed, you can submit it for free.