How can AI help a law firm review a large disclosure set?

AI can shorten the slow parts of a disclosure review: reading everything once, pulling out dates and names, grouping documents by theme, drafting a chronology and flagging the handful of pages that matter. It cannot decide what is relevant, what is privileged or whether your search has been reasonable. Those are legal judgements, and they stay with the fee earner who signs the disclosure statement.

What AI is genuinely good at here

Most of a disclosure exercise is not difficult, it is long. A tool that reads every page and answers questions about what it read takes the length out without taking the judgement out.

The tasks that suit it:

• First pass triage. Sorting a set into obvious categories: correspondence, invoices, internal notes, duplicates, documents that mention a particular person or date range.
• Finding the needle. Asking a plain question, such as what the parties said about delivery dates in March, and getting back the pages that address it.
• Chronology building. Extracting dated events into a draft timeline that a fee earner then corrects and completes.
• Summarising long documents. A hundred page report reduced to a page, with the page numbers so you can check it.
• Consistency checks. Comparing what a witness says now against what the contemporaneous emails say.
• Spotting gaps. Noticing that a chain of correspondence stops abruptly, or that an attachment is referred to but not present.

None of that is a substitute for a fee earner reading the important documents properly. It is a way of finding out which documents those are, faster.

What must stay with a human

Relevance is a legal question about the issues in the case. It shifts as pleadings are amended and as the evidence develops. A tool can find documents that mention a topic; it cannot decide that a document is disclosable because of an issue that arose at a case management hearing last week, unless someone tells it.

Privilege is the sharper risk. Legal advice privilege and litigation privilege turn on who was speaking to whom, in what capacity and for what dominant purpose. A document can look like ordinary internal correspondence and be privileged, or look like advice and not be. Never let a tool make the final privilege call, and never let an automated flag stand as the only check before a document goes out.

The reasonableness and proportionality of the search is also yours. The Civil Procedure Rules expect a party to conduct a reasonable and proportionate search and to be able to explain what was searched and what was not. If AI was used to narrow a set, you should be able to describe how, in the same way you would describe a keyword search.

A workflow that holds up

A structure that works for a firm of this size:

1. Fix the issues first. Write down, in a short list, the issues the disclosure goes to. Everything else follows from that list.
2. Get the set into a readable state. Scanned bundles, photographs of documents and handwritten notes vary in quality. Know which pages could not be read properly before you rely on any summary of the set.
3. Segregate the likely privileged material early. Correspondence with your firm and with previous solicitors should be pulled out and reviewed by a human before anything else happens to it.
4. Use AI for the first pass, not the last. Let it categorise, summarise and surface candidates. Record what you asked it.
5. Have a fee earner read every document that will actually be relied on. Summaries are for triage. Evidence you will stand behind gets read in full.
6. Check the citations. If an answer says a fact appears on page 412, open page 412. If the tool cannot point at a page, treat the point as unverified.
7. Keep a note on the file. What tool, for what task, checked by whom. If disclosure is later challenged, you want a contemporaneous record rather than a reconstruction.

Confidentiality and the practical questions

Disclosure sets often contain the other side's confidential material, third party personal data and sometimes special category data. Before a single document goes into any tool, you need to know where the data is held, who at the supplier can see it, whether it is used to train anything, and how long it is retained. UK GDPR obligations apply to this material exactly as they do to anything else on the file, and the Information Commissioner's Office expects you to have thought about it before, not after.

Inside the firm, ask whether the tool respects matter boundaries. If everyone with a login can query every matter, you have created a confidentiality problem that did not exist when the papers sat in a locked room.

Finally, be honest about cost. If you are billing hourly and the review takes half the time, say so and bill what you did. If you are working to a fixed fee, the saving is yours. Either way, the client is entitled to a straight answer about how the work was done.

Where Alesis fits

Alesis reads a matter's papers page by page, answers questions from those papers and names the page each answer came from; if the papers do not say, it says so, and any page it could not read is flagged rather than skipped. It prepares drafts for a qualified person to review and sign off. The firm's information is held in the UK, kept apart from every other firm and never used to train anything for anyone else, and inside a firm people see only the matters they are on. It assists qualified professionals and does not replace them.