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.
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.
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.
Billable hours consumed in locating clauses, comparing versions, and verifying consistency across annexes. Time the client rarely wants to pay for.
Operations where thousands of documents must be reviewed in a week. The bottleneck is not legal criteria — it's reading speed.
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.
SaaS solutions promising "legal AI" without understanding confidentiality, professional secrecy, or traceability of decisions made with the model's support.
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.
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.
60-70% reduction in first-review time on repetitive contracts (NDAs, service agreements, leases).
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.
DD teams that spent 5 days reading enter the analytical phase on day 2, dedicating recovered time to drafting the report.
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.
Institutional knowledge no longer depends on who is in the office that day. Juniors stop reinventing arguments.
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.
First draft time reduced to minutes in repetitive documents. The criteria remain the lawyer's, but they start with a product.
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.
Administrative processes that consumed saturated paralegal profiles shift to the background — freeing hours for billable work.
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.
Mapping of firm processes, document volume, and opportunities. Result: 2-3 prioritized use cases with estimated ROI.
End-to-end implementation of one use case (typically contract review or semantic search), integrated with the firm's management tool.
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.
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.