AI for professional firms: accountants, lawyers, consultants

The fastest return is on three fronts: extracting data from documents (invoices, contracts, records, payslips, PDFs of all kinds), searching the archive — finding in seconds the clause, case or precedent you need — and preparing recurring drafts from the firm's templates and data. These are high-volume, repetitive activities where the professional today spends valuable time on mechanical work.

The goal is not to replace the professional, but to remove the mechanical part: reading, copying, searching, formatting. Judgment, advice and accountability stay with the person, who reviews and signs. AI surfaces the ready data and the exceptions, not the decisions.

On a case covering insurance and mortgage files — the same mechanics as a firm: a pile of documents arriving at different moments, from different people, in formats nobody chose — processing hours were cut in half. AI reads the file, pre-fills the data and flags what is missing; the decision stays with the case handler, who signs. The full account is on the case page, among the links at the bottom: names omitted because the end client cannot be disclosed, but the number is real.

It is not a promise you can transfer blindly. How much you recover depends on how repetitive your documents are and how many times the same value is re-keyed before it reaches its destination: a firm receiving everything in one format has less to gain than one receiving from twenty different counterparties. That is why the diagnosis looks at your documents before quoting a number.

The subject matter changes, the mechanics do not: documents arriving from outside, data to extract, an archive to find things in. What changes from one firm to the next is which set of fields actually matters, and that is defined in the initial analysis by looking at your real documents rather than a generic template.

From invoices, contracts, records, payslips and scans, AI extracts the fields you need — amounts, dates, codes, parties, deadlines — and returns them structured, ready for your system or a spreadsheet. It works on non-standard documents and on scans of varying quality, because it understands context: it recognizes the same value even when it changes position or wording from one source to another.

Unlike rigid legacy OCR, it does not just read characters: it interprets the document. This reduces manual correction and frees up staff time for the work that truly requires professional expertise.

AI searches by meaning, not just exact words: you ask a question in plain language — "where is the termination clause in client X's contract", "which cases have a June deadline" — and it finds the relevant passage in your documents and gives you the reference it came from, so you can verify it. It helps firms with large archives, where finding the right information takes time.

The search works on the firm's documents, not on generic knowledge: it answers from your cases and cites the source, leaving final control to the professional. No "made-up" answer without a verifiable reference.

Yes. The firm's data stays yours: no shared model training, encryption in transit and at rest, EU data residency on request, and least-privilege access with audit logging. For the most sensitive data, an isolated-environment model can be used, keeping everything in house. What can leave and what cannot is decided in the project's initial analysis.

For a professional firm, confidentiality is a constraint, not an option: that is why the architecture is designed GDPR by design, and the choice between an external service and an isolated environment depends on the sensitivity of the data handled.