Document classifiers, clause extractors, private RAG over your know-how and case-scoring models — trained on your matters, not on a generic internet dataset.
A law firm, a consultancy or an engineering practice has its own taxonomy: clause types, proposal templates, client-specific jargon and success criteria that don’t live in any off-the-shelf model. Asking a generic LLM to classify your opinions or estimate project cost from a brief ends in answers that look right but aren’t reliable — and weekly manual corrections.
We build models trained on your history: document classification by type, critical-clause extraction, case- and project-success scoring, and a private RAG that answers from your work product — not from general knowledge. Evaluated against a gold set signed off by partners before going live.
Contracts, opinions, proposals, technical reports, court filings — classified using your internal taxonomy. Integrates with SharePoint, M365 or whichever DMS you use and routes each document to the right matter file.
Reads contracts and opinions and extracts limitation-of-liability, non-compete, jurisdiction, deadline and penalty clauses — with field-level confidence and a back-reference to the source paragraph, ready for review.
Search assistant over your memos, precedents, opinions and reports — respects practice-area and client permissions. Always cites the source and never mixes information across matters.
Model trained on your history of closed matters: estimates success probability, expected timeline and cost for a new case. Built to support the accept/decline decision, not to replace it.
Estimates hours per role and total cost for a project from the client brief and similar closed matters. Cuts under-priced proposals and gets quotes out within 24 hours.
Junior staff spend 6–9 hours a week classifying incoming documents and locating specific clauses in long contracts. Proposals are drafted from a template, with no reuse of similar prior work. Project-hour estimates rely on the responsible partner’s judgement, with average deviations of 35%.
The classifier auto-assigns 92% of incoming documents to the correct matter with high confidence — the remaining 8% goes to manual review. The extractor pulls critical clauses in seconds and leaves the contract annotated for review. The hours predictor brings estimate deviation down to 12% and shortens proposal turnaround by 60%.
Mid-market CRM with broad APIs — a natural fit for sales agents and lead enrichment.
Enterprise CRM with fine-grained permissions — AI workflows that respect the data model.
CRM with strong adoption among Spanish SMBs — automations and agents at a contained cost.
Enterprise CRM/ERP suite in the Microsoft ecosystem — native fit with 365 and Power Platform.
Spanish cloud ERP widely adopted by SMBs — invoicing, expenses and reconciliation automation.
Modular open-source ERP — AI agents and workflows on top of sales, inventory and project modules.
Email, calendar and SharePoint as channel and context — triage, drafting and RAG over your inbox and files.
Models are trained and run in private infrastructure — your Azure or AWS tenant or your own servers — without data leaving for third-party APIs. Embeddings and model weights stay inside your perimeter and logs are auditable. We sign an NDA and, where required, a data-processing agreement under GDPR.
The case-success scoring is a decision-support tool, not a decision-maker. We document the training set, the features the model uses and its performance by sub-segment (client type, matter, jurisdiction). We audit known biases before going live and provide a panel for partners to review predictions whenever they diverge from expert judgement.
A model in production isn’t a finished model. We agree a retraining cadence — typically quarterly — using the new history, and monitor drift in real time. If quality drops below the agreed threshold, the model is retrained or, if that isn’t enough, the dataset and architecture are revisited. It’s in the contract, not a surprise.
No. We work on top of SharePoint, M365, Google Workspace, Notion and the most common legal DMS via API or intermediate connector. The rule is: the model adapts to your stack, not the other way round. If the system has no public API we look at webhooks or scheduled exports.
We design for privacy from the start, human control, traceability, usage limits, permissioning and documentation. For sensitive processes, we help assess risk and applicable obligations under GDPR and the EU AI Act.
Every engagement is led personally by one of the partners. If there's a fit, you get a personal first read of your case within one business day — not a canned demo.
