Legal
Know how often your AI agent is right before it goes in a filing.
We turn your lawyers' judgement into criteria a machine can check, for AI agents doing research, document review and drafting. Built for teams where a confidently invented citation is the failure everybody has now read about.
You build the AI agent, we evaluate it. We measure how often your AI agents are right, and we show you how accurate that measurement is.
01 / What we take on
Four situations we are usually called into.
These are the ones we see most often. If your situation looks different, we would still like to hear about it.
01
A definition of correct that lives in a review memo
TodayA partner wrote down what a usable answer looks like. It is a page in a memo, and the release still turns on whoever read thirty outputs that morning.
With KayznBuilt and running in your own pipeline. The automated judge, the model doing the scoring, is measured against your experts before it is allowed to block anything, and your team logs into a running view to see how often it agrees.
02
Evals that pass, and a citation that does not exist
TodayThe suite is green. The answer that cited a case nobody can find was not in it, and nobody knows what else is not in it.
With KayznA catalogue of the ways it actually fails, built from your own traces, the records of what your AI agent did and what it had in front of it. Then the criteria, the scoring guide and the labelled examples that would have caught it.
03
An AI agent in production that nobody quite trusts
TodayIt is live, associates use it, and no one can tell you how often it is right or what it does when the authority it needs is not in the corpus.
With KayznWe read what it actually did, score it against a written definition of correct, and tell you the error rate of our own finding as well as the agent's.
04
One lawyer is the only one who can say whether an answer is right
TodayThe labelling, the criteria and the re-checking all sit with them, and their billable time is the thing you have least of.
With KayznYour team trained to run the labelling, maintain the criteria and re-calibrate when the model moves, so it is yours to do.
02 / What the work produced
Numbers from a published engagement.
Aggregate across fifteen scored dimensions, including the criteria that were nowhere near good enough to gate anything yet.
- 69%→77%
- Judge to human agreement
- 9.9%→7.7%
- False positives
- 20.9%→15.4%
- False negatives
That engagement was in financial services rather than legal. Citation correctness was one of the scored dimensions in it, which is the criterion most legal teams ask about first, and on the call we will tell you what else we would have to build from scratch for your corpus.
03 / How it starts
Begin wherever you are today.
Every step ends with something you keep. What you keep is a running view of how often your AI agents are right, and it keeps updating.
The two free ways in ask nothing of you but what you already have. A Trace Review takes traces your AI agent has already produced and shows you what an evaluation of it would find. Sending us one criterion needs no data at all, and answers a narrower version of the same question in writing.
What happens to your traces is written down rather than promised on a call. Who holds them, how long, who else touches them and what is deleted when the work ends are all on the data handling page. We would rather you read it before you send anything than take our word for it on the phone.
Start with a conversation
Book an Evaluation Fit Call.
Free, and useful whether or not we work together. Bring one AI agent at any stage, from an early design to production. We will tell you honestly what can be evaluated now, what has to exist first, and what an evaluation would and would not establish.
Not ready for a call? Send us the one criterion you trust least and we will write back on what it would take to make it decide something.
No deck. No discovery workshop. One AI agent, what it does or is meant to do, and a partner on the call.