Law Firms

AI applied to real legal work, not the promise.

We partner with mid-sized and boutique law firms to integrate AI where it truly moves the needle: contract review, due diligence, massive document management, and semantic search over the firm's archive. Not replacing the lawyer — multiplying their capacity.

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Serving law firms nationwide · Confidentiality and traceability by default

The Challenge

The bottleneck of a modern law firm is not legal. It is documentary.

Every law firm we audit repeats the same pattern: partners and associates with high hourly rates spending hours on mechanical tasks — reading long contracts to find three clauses, reviewing hundreds of documents in due diligence, drafting first versions of repetitive briefs. AI doesn't decide, but it accelerates and secures everything surrounding the decision.

  • Manual review of long contracts

    Billable hours consumed in locating clauses, comparing versions, and verifying consistency across annexes. Time the client rarely wants to pay for.

  • Due diligence with impossible deadlines

    Operations where thousands of documents must be reviewed in a week. The bottleneck is not legal criteria — it's reading speed.

  • Knowledge trapped in the archive

    Twenty years of briefs, opinions, and precedents in PDFs. Retrieving an argument used in a similar case three years ago depends on who remembers it.

  • Risk of generalist providers

    SaaS solutions promising "legal AI" without understanding confidentiality, professional secrecy, or traceability of decisions made with the model's support.

Use Cases

What actually works in a real law firm

Five cases where we have seen clear ROI at 6-12 months. The common rule: the model assists, the lawyer decides. Every output of the system is traced for later auditing.

  • 01

    Assisted contract review

    The system reads the contract, extracts key clauses (jurisdiction, indemnities, non-compete, termination, warranties) and compares them against a checklist or the firm's playbook. It highlights deviations so the lawyer only reviews what falls outside the standard.

    What changes

    60-70% reduction in first-review time on repetitive contracts (NDAs, service agreements, leases).

  • 02

    Accelerated due diligence

    Semantic indexing of the data room. Natural language searches ("contracts with change of control clauses", "pending payment commitments over €50,000") returning exact quotes with their source. The team prioritizes what to read entirely and what not to.

    What changes

    DD teams that spent 5 days reading enter the analytical phase on day 2, dedicating recovered time to drafting the report.

  • 03

    Semantic search of the historical archive

    The entire firm archive — briefs, opinions, favorable rulings — indexed and searchable in natural language. "Have we ever defended an unfair competition case based on protected clientele?" returns the files with the relevant passage cited.

    What changes

    Institutional knowledge no longer depends on who is in the office that day. Juniors stop reinventing arguments.

  • 04

    Assisted first draft

    Generation of initial drafts for briefs, standard contracts, and client communications based on the firm's templates and case context. The lawyer reviews and signs — the blank page disappears.

    What changes

    First draft time reduced to minutes in repetitive documents. The criteria remain the lawyer's, but they start with a product.

  • 05

    Massive extraction and normalization

    Pipelines processing invoices, certificates, deeds, or administrative resolutions, extracting structured data (dates, amounts, parties, references) and dumping them into the firm's management tool. Goodbye typing data from PDFs to Excel.

    What changes

    Administrative processes that consumed saturated paralegal profiles shift to the background — freeing hours for billable work.

Our Approach

Confidentiality and traceability by design. Phased implementation.

We work on infrastructure controlled by the firm — models in private environments or deployments with signed data processing agreements — strictly defining what goes in, what comes out, and who has access. Every model output is logged with prompt, context, and response so any AI-supported decision can be audited later.

  • Legal-Tech Diagnosis

    2 weeks

    Mapping of firm processes, document volume, and opportunities. Result: 2-3 prioritized use cases with estimated ROI.

    • Interviews with partners and practice areas
    • Documentary and tool inventory
    • 12-month roadmap by practice area
    • Compliance plan (GDPR, professional secrecy, ethics)

  • Single-case Pilot

    6-10 weeks

    End-to-end implementation of one use case (typically contract review or semantic search), integrated with the firm's management tool.

    • Private deployment or approved cloud environment
    • Initial indexation of the corpus
    • Integration with existing document manager
    • User training and responsible use guide
    • Savings and accuracy metrics during 4 weeks post-go-live

  • Firm-wide Platform

    4-6 months

    For firms wanting their own unified AI layer instead of scattered licenses. Search, review, and drafting over a single index of the firm's archive.

    • RAG architecture over firm's archive
    • Integration with legal CRM, document manager, and signature tools
    • Permissions by practice area and client
    • Maintenance, retraining, and addition of new use cases

Results

What changes in the firm at 6 months

  • Review time of repetitive contracts reduced by 50-70%, validated against pre-implementation baseline.
  • Due diligences entering the analysis phase days earlier — more margin for the report and fewer late nights.
  • Firm knowledge stops relying on the memory of the 20-year partner.
  • Juniors and paralegals freed from mechanical tasks, dedicated to higher-value work (and higher retention).
  • Full traceability of what was done with model support and what the lawyer decided — fully auditable.
  • Internal AI use policy documented, aligned with ethics and professional secrecy obligations.

FAQ

What we are asked in the first meeting

  • Does this replace the lawyer?

    No. It would replace judgement if we let it decide alone, and we do not do that. AI accelerates reading, information retrieval, and first drafts generation. The signature, professional responsibility, and legal judgement remain the lawyer's — and the system's traceability proves it.

  • What about professional secrecy?

    We treat it as a hard constraint from day one. We work with private deployments or cloud providers under processing agreements that forbid using data to retrain models. Client information never leaves the agreed perimeter, and accesses are segregated by case or practice area.

  • How much does it cost to start?

    A pilot on one use case usually ranges €18,000-€35,000 depending on doc volume and integrations. First, we run a 2-week diagnostic audit (€4,000-€7,500) ensuring the chosen case has a defensible ROI. If the diagnosis says it's not the right time, we tell you.

  • Do the models hallucinate false information?

    They can. That's why for legal use cases we always design with documentary grounding: the model only responds by citing sources from the firm's archive, and explicitly states when there is no basis. Final verification is human, but inventing facts is prevented.

  • Does it work if I have paper archives or scanned PDFs?

    Yes. Digitization and OCR of the historical archive are part of the project when needed. It is the first deliverable because without an indexable corpus, there is no use case.

Want to see which use cases apply to your firm?

A 30-minute call to review your practice mix, document volume, and current tools. We leave with 2-3 candidate use cases and an order of magnitude of investment. No strings attached.

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