Most legal AI vendors run good demos. Few of them survive privilege, hallucination, jurisdiction, or the kind of bias audit a regulator will read. We have written the comparison of Harvey, Ironclad, Spellbook, Casetext, and the rest. We will tell you on the discovery call which fits your matter and which does not — and where the right answer is to build something a vendor cannot. The agent we build is yours, the model is your choice, the data stays in your tenant.
Legal AI must be defensible. Every output cites the source clause, the source authority, the source contract, the source case. We build to that standard by default.
Automated first-pass review for NDAs, MSAs, vendor contracts, and procurement paper. The agent reads the document, flags clauses against your playbook, suggests revisions in track changes, cites the source clause for every flag. Reviewer takes the second pass with full context.
AI agents that work through hundreds of thousands of documents in litigation review. Privilege detection, responsiveness scoring, issue tagging, with sample-based human review and continuous learning. Reduces review hours by 70-90% on routine matters; lawyer time goes to the documents that need judgement.
RAG over your precedent library, internal memos, drafted clauses, and matter files. The agent answers "have we negotiated this clause before, and what landed?" with cited source documents. Replaces the precedent-search ritual that consumes associate hours.
First-draft generation from your firm's clause library. The assistant builds the document around your standard positions, flags deviations from the playbook, leaves placeholders for fact-specific terms. Lawyer drafts in minutes from your precedent, not from scratch.
If anyone tells you legal AI replaces a lawyer's judgement, they are selling you something.
Privilege is non-negotiable. Your data does not leave your tenant. We deploy in your Azure or your AWS, with your model provider keys. Vendor-hosted models that train on your prompts are not appropriate for privileged work and we will say so on the discovery call.
Hallucination is a real risk. Every output cites the source. If the agent cannot cite, it does not answer. The pattern is RAG-with-citation, not free generation. Standards we apply: 100% of substantive outputs trace to a source document; uncertainty is surfaced, not hidden.
Jurisdiction matters. An AI trained primarily on US case law gives bad advice on UK contracts. We pick or fine-tune the model for the jurisdiction. UK firms get UK-tuned, US firms get US-tuned, multi-jurisdiction matters get a routing layer that knows which jurisdiction the question belongs to.
Bias is documented, not hand-waved. Where the agent's outputs could affect people (employment matters, sentencing-adjacent work, immigration), we run bias audits and document them. The methodology page is published, like our AI Job Impact Calculator approach.
We have read the comparison literature on Harvey, Ironclad, Spellbook, and Casetext. Each has a place. Each has a place we cannot recommend either. We will tell you which is right for your matter, and when none of them are.
You are a GC at a mid-market company reviewing 100+ NDAs and MSAs a quarter, and your team is the bottleneck.
You are a law firm partner who has been told to evaluate Harvey or Ironclad and you want a buyer's-side opinion before procurement signs the cheque.
Your firm has a deep precedent library and the partners are the only people who know where anything is. You want every associate to have that knowledge on tap.
You have a litigation matter with hundreds of thousands of documents to review and the timeline does not allow a contract attorney army.
Your data sensitivity rules out vendor-hosted AI. You need agents that run inside your tenant on your infrastructure.
You have evaluated three legal AI tools and none of them quite fit. You want something built for your workflow, not adapted from someone else's.
We map your matters, your tools, your data sensitivity, and the specific work that would benefit from agentic acceleration. You leave with a written specification, a vendor-vs-build recommendation, and a build estimate.
One agent built end to end against a defined matter type. RAG over your data, citation discipline by default, deployed in your tenant. Goes live with human-in-the-loop on every output until partner review confirms quality.
We monitor accuracy, retrain as your playbook evolves, push fixes when output quality drifts. Quarterly bias and citation audit included. Model and infrastructure costs at cost.
Legal work overlaps finance (contract terms extraction) and data (knowledge agents over precedent). Same studio, same standards, different domain.
Contract terms extraction at scale, AP automation, AR chasing. Where legal review meets the finance team's contract data, both sides benefit.
Internal knowledge agents, RAG infrastructure, document extraction. The plumbing under most legal AI work, available as a standalone build.
Voice receptionists for the firm front desk, voice intake for new matters, voice-first associate training. Voice AI is a separate practice; the same studio runs both.
We work with in-house legal teams and law firms across the UK, US, and Australia. Citation by default. Privilege protected. Vendor-agnostic, opinionated where it matters.
