Corporate Legal
AI infrastructure for legal departments that own their own data.
Contract history, regulatory filings, and internal policies — structured into a knowledge layer that your legal team controls.
Corporate in-house legal departments are closer to the medical hospital model than to outside law firms: the corporation owns its own data, can authorize its use internally, and faces ethical constraints that are organizational policy rather than ABA ethics rules. That makes the data rights problem solvable — and the AI opportunity real.
The data problem in legal — corporate in-house
Corporate legal departments accumulate contract history, regulatory correspondence, litigation files, and internal policies over decades without a unified system for querying across that corpus. The institutional knowledge of what was negotiated in a prior contract, how a regulatory agency has responded to similar filings, or what indemnification language the company has accepted before lives in individual attorneys' files.
Contract review and drafting consumes a disproportionate share of in-house attorney time on work that follows predictable patterns: the same commercial terms, the same negotiated fallback positions, the same company-specific redlines that every transactional attorney learns over time. That knowledge is not encoded anywhere; it transfers through mentorship and accumulated experience.
Regulatory monitoring across multiple agencies, jurisdictions, and product lines creates a reading burden that outpaces staffing. Staying current with relevant guidance, rulemaking, and enforcement actions for a complex company is a full-time function that most legal departments underfund.
Outside counsel costs scale with the volume of work sent out. Reducing outside counsel spend requires either doing more in-house or doing it faster. Both require infrastructure that the typical in-house department does not have — document retrieval, precedent search, issue spotting — without outsourcing to a general AI tool that has no understanding of the company's specific legal posture.
What we deliver
Synthetic data
Synthetic contract datasets modeling the company's typical commercial terms and negotiation patterns — for training intake staff, testing contract management system configurations, and validating extraction models without using live executed agreements.
Custom models
Contract analysis and issue-spotting models trained on the company's own contract corpus and negotiation history, tuned to flag the specific terms and fallback positions that matter to this legal department.
Knowledge & retrieval
A structured index of the company's executed contracts, regulatory filings, internal policies, and outside counsel correspondence — with document-level retrieval so attorneys can find prior positions without manual search.
See the full architecture for how these layers fit together.
Common deployments
Contract redline and issue spotting
Model trained on the company's negotiated contracts that flags deviations from standard terms, surfaces prior fallback positions, and drafts redlines consistent with the company's established playbook.
Regulatory monitoring digest
Automated ingestion and structured summarization of regulatory developments relevant to the company's business lines, delivered as a weekly briefing with citations.
Precedent search across executed contracts
Retrieval system over the company's full contract corpus that surfaces how specific terms have been handled in prior agreements, with document-level citations.
Outside counsel instruction drafting
Model that drafts structured matter instructions and RFP questions based on the company's standard engagement terms and prior matter history.
Frequently asked
Discuss your legal — corporate in-house deployment
Tell us about your data, your constraints, and your workflows. We'll design the layers around them.
Start the conversation