Deeligence vs Claude

Deeligence vs Claude: which one should run your due diligence?

A senior associate spends a couple hours working with Claude on a draft NDA and comes away impressed. They reasonably wonder whether the same tool can run the M&A diligence starting Monday. Claude is one of the best general-purpose AI assistants available. Deeligence is a platform built specifically for the M&A due diligence workflow. They are good at different jobs, and the difference becomes practical once a real data room is involved.

This page sets out what each tool is built for, the architectural differences that matter when due diligence is the task, and how firms typically use both alongside one another.

Quick verdict

Use Claude when:

• You have one or two contracts to read and want to think through clauses, draft amendments, or test arguments.
• You are early in your AI journey and want to learn what AI can usefully do on legal work.
• You need a drafting partner for something that does not involve a data room.

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Use Deeligence when:

• You are running a real diligence on a data room with dozens or hundreds of documents.
• You have a team allocating work across contract types and need to see who has reviewed what.
• The data room changes day by day and you need to know what is new since Tuesday.
• You want a Word report created perfectly in the firm’s house style, not a chat transcript.

What Claude actually does well for lawyers

Claude is a general-purpose AI assistant built by Anthropic. It is one of the most capable language models on the market, and it is remarkably useful across a wide range of legal tasks: clause comparison, plain-language explanation, drafting, research, summarising a single contract, talking through a knotty problem at midnight.

A lawyer who learns to use Claude well will get real productivity gains on a meaningful slice of their work. None of what follows is a criticism of Claude as a product. It is a description of what changes when the job stops being “read one contract” and starts being “run diligence across a data room.”

What Deeligence does

Deeligence is built for the way M&A due diligence actually runs. The data room arrives. The team gets assigned. The AI scans everything overnight and surfaces red flags by the next morning. Lawyers verify rather than starting from a blank page. The platform tracks who has reviewed what and when it's client ready. When the seller swaps out documents on day six, the Change Tracker tells the team where to look. When it is time to deliver, the report exports in the firm’s Word template, ready for the partner.

The AI inside Deeligence does two specific jobs. The Early Warning System runs across the full data room on day one and surfaces the items that need lawyer attention. The AI Contract Screener extracts over 100 fields from material contracts with local law summaries. Mayne Wetherell measured an 83% reduction in material contract review time on a recent deal using this approach.

Deeligence works on top of Ansarada, Intralinks, DataSite, Google Drive and SharePoint. Documents stay where the client put them. The platform is built for professionals and contracted as such: enterprise terms, DPAs, the access controls procurement teams ask for.

The architectural differences that matter for due diligence

The differences below are about how each system is built, not about how well either one does its intended job. Claude is built to be a strong general-purpose assistant. Deeligence is built around the specific shape of a due diligence engagement. The trade-offs become visible when the same task lands on both.

Working memory and document volume

A working memory in a general AI assistant is bounded by its context window. Claude’s current context comfortably holds individual contracts and runs sophisticated reasoning across them. A typical M&A data room has several orders of magnitude more content than that window can hold at once. The practical workaround is to process documents one at a time and summarise. This works for individual contracts. It is less suited to the diligence workflow, where a finding in one document often only becomes a finding when it is reconciled with information in another: the IP question in one folder against the partnership agreement in another, the litigation files against what management disclosed in the IM. Deeligence is built around processing the full corpus in parallel and reasoning across it.

Coverage under cost pressure

Inference is metered. When Claude's agentic workflow has to choose between reading every document fully and reading some documents and summarising others, the published general-AI skills tend toward the second. The diligence-issue-extraction skill in Anthropic’s claude-for-legal plugin instructs the model to focus on the “40 documents that really matter” in a typical diligence. That guidance is reasonable: most diligence findings live in a smaller subset of documents.

The challenge it creates is that the subset is only knowable once everything has been read. In internal testing on representative data sets, out of the box and without configuration, the general-AI workflow returned under 50% of the known issues. Tuning improves the picture; the underlying constraint on coverage at scale remains. Deeligence is built to process the full data room as the starting point, which removes the trade-off.

Deterministic versus probabilistic tracking

Tracking what has changed in a data room is a deterministic problem (i.e. a problem with an actual answer that can be solved correctly or incorrectly): which documents are new, which have been replaced, which have been redacted. Probabilistic systems can describe changes well in plain language; they are not the right architecture for being the system of record on what is in the data room from day to day. Deeligence’s change tracker is built deterministically for this reason. General-AI assistants are not and so are error-prone for this task. Missing a document because Claude didn't flag it is something regularly observed in testing.

Workflow integration

A diligence engagement involves more than reading documents: work allocation across a team, status visibility, comment threads, deliverable production in firm templates, linkages between all parts of the report, an audit trail by document. General-AI assistants can be prompted to produce parts of this; assembling the parts into a workflow is the user’s job. The published skills acknowledge this in their handoff documentation, which routes high-volume bulk contract review to specialist tools outside the general-AI environment. The practical implication is that a real deal stitches together a general-AI assistant, a specialist bulk-review tool, and a person managing the connections between them. Deeligence is the single system.

Intended user

The published general-AI workflow tools assume a user who is comfortable with Github, command-line conventions, configuration files, connector setup, and the underlying mechanics of how the model works. This works for early adopters and developer-leaning lawyers.

The senior associate running diligence the night before a signing wants to open the deal and see findings. Deeligence is built for that user.

When Claude is the right answer

None of the above means lawyers should not use Claude. There are specific scenarios on a deal where the honest answer is Claude, not Deeligence:

• One contract, one question. A client sends a draft NDA and asks for a view on the indemnity. Open Claude, paste the clause, get a view. The whole engagement might be 20 minutes. Pulling a DD platform into that would be overkill.
• Drafting work alongside the transaction. While Deeligence runs the DD, the same lawyer might be drafting consent letters or resolving a dispute that's been identified at material. Claude is excellent at this and runs alongside, not against, the DD workflow.
• Personal productivity tasks. Summarising long emails, drafting client updates, comparing two versions of a clause for personal reference. None of these need a DD platform.

A useful frame: Claude is a brilliant generalist. Deeligence is a specialist for one job. Firms that take the work seriously use specialists when the stakes warrant it.

Frequently asked questions

Can I just use Claude for my next due diligence?

For a small transaction with a handful of contracts and one or two lawyers, Claude can carry a long way. For a normal M&A deal with a real data room, three things will stop you: the working memory cannot hold enough of the data room at once to reason across it, the system silently samples rather than processing everything, and the workflow around the AI (allocation, change tracking, reporting, audit trail) is missing. Most lawyers who try Claude on a real DD find the amount of configuration required is beyond their skill level and takes more time than it saves.

What happens when Claude throws an error?

There are often times when Claude runs into issues due to caps on computing power, token limits and its overall popularity leading to outages. If you need support at 11pm on a Sunday night there is no contact, no help line and no way to get a resolution. You simply have to wait for token limits to reset and start again.

What about completeness on a real data room?

In our internal testing on representative diligence data sets, out of the box Claude's general-AI workflows returned under 50% of the known issues. The findings that were missed tended to be the ones that are not obvious from a file name: IP assignment issues buried in boilerplate, change-of-control wording in contracts that do not look material on their face. Configuration and prompting improve the picture but the underlying limit on coverage at scale remains. Deeligence is built to process the full data room as a starting point, which is the design decision that closes this gap.

Does Deeligence use Claude under the hood?

Deeligence uses a combination of AI models in production, including the latest versions of Claude. They are outstanding foundational models. Deeligence also uses a variety of third party and proprietary models to break down and assess risk across the document sets. Ultimately, the value of a DD platform is the workflow, integrations, audit trail and outputs that sit around the model, not which model is used. When underlying models improve, customers benefit without changing anything in their workflow.

Is Claude safe to use on client documents?

Consumer Claude usage on client documents will not satisfy a firm’s information security policy or a client’s engagement letter, depending on how documents are uploaded and which retention settings apply. Anthropic offers enterprise tiers of Claude with stronger data handling commitments; those are worth considering for general firm use. Deeligence operates under enterprise terms with DPAs available as standard.

What about ChatGPT, Gemini, Copilot? Is the comparison the same?

Not really. Claude Cowork and Codex from OpenAI are relatively similar general-purpose agentic systems. However they all face the same architectural limits against a purpose-built DD platform: working memory smaller than a data room, sampling under cost pressure, no native change tracking, no team workflow, no template reports. The choice between Claude, ChatGPT, Gemini or Copilot for general use is a separate question. The choice between any of them and Deeligence for due diligence specifically is the one this page is about.

Can I use both Claude and Deeligence on the same deal?

Yes, and many teams do. Deeligence runs the DD workflow end to end. Claude handles everything else on the deal. The two tools answer different questions and do not conflict.

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