AI agent evaluation
Kayzn is an AI agent evaluation consultancy. We turn expert judgement into measurable criteria, and keep score as your AI agents evolve.
You build the AI agent, we evaluate it. We cover the evaluation process end to end: we audit what you have, we design what is missing, we build it, and we teach your team to run it.
Both partners, on every engagement.Step 1A customer asks
One real request, out of thousands.
Step 2Your agent looks things up
Was I charged twice for my August payment?
It pulls what it thinks it needs.
Step 3It answers
Confident, and it sounds right.
Step 4Kayzn checks it
Backed by what it actually looked up
Points at the right document
Used the current policy, not last year's
Answered the whole question left out what happens next
Four checks your own experts wrote.
Step 5What we found
One answer is not the point. This is the pattern behind it.
8.4 / 10
answers meet your team's definition of correctand now you can see the 1.6 that do not
Leaves out what happens next14%
Right document, wrong paragraph6%
Answers from a superseded policy3%
The same failure, counted across every trace you sent.
Step 6What you get
A straight answer about your agent, before you ship it or while it is already live.
Ready to ship for four of your five question types. Not for refunds.
We do not build your agent. We tell you where it stands, in a sentence you can repeat — and what to fix first.
01 / What we take on
These are the ones we see most often. If your situation looks different, we would still like to hear about it.
01
TodaySomeone wrote down what good looks like. It is a page in a wiki, and the release still turns on whoever read thirty samples 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 can block anything. Your team logs into a running view of how often it agrees.
02
TodayThe suite is green. The thing that broke was not in it, and nobody knows what else is not in it.
With KayznA catalogue of the ways it actually fails, read out of your own traces. Those are the record 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
TodayIt is live, it is useful, and no one can tell you how often it is right or what it does when it is wrong.
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
TodayThe labelling, the criteria and the re-checking all sit with them, and the hosted model changes underneath you anyway.
With KayznYour team trained to run the labelling, maintain the criteria and re-calibrate when the model moves, so it is yours to do.
Written for your industryFinancial servicesHealthcareInsuranceLegal
02 / Selected work
What we measured, what it changed, and the numbers to show it, including the part that is not good enough yet.
A financial services firm's AI platform team worked with Kayzn on one Evaluation System Build. Each run is compared against prior results, criterion by criterion, so a regression surfaces before release. The judge's agreement with human labelers reached 77%, short of the 85% bar Kayzn set before it would let a criterion block a release without a person checking.
Release held one required dimension failed
A financial services firm's AI platform team
03 / Team
The name comes from kaizen, Japanese for change for the better: small steady steps rather than one big push. Evaluation works the same way. You do not settle trust once and move on, you measure one criterion, improve what the number shows you, and measure it again.
Partner · Los Angeles
Senior engineer and tech lead at Amazon, with over a decade across full-stack, cloud, MLOps, and DevOps. He has led technical teams and mentored hundreds of developers, from bootcamp graduates to senior engineers. He has built agentic systems and RAG pipelines himself, which is where he learned how quietly they fail, and that experience now points at one thing: the criteria, the calibration of the judge, and the gate that decides whether a release ships.
Partner · Montréal
A product and technology leader with a decade at Expedia Group. Seven years engineering the systems behind hotel pricing and recommendations, then leading the AI evaluation and quality frameworks that put AI-driven travel in front of millions of travellers. He has set the quality bar AI had to clear before it reached a customer, and been the person accountable for the number behind it.
04 / Services
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.
05 / Blog
What we have learned doing this work, explained plainly. Take what is useful.
Before you let a model grade your AI agent, someone has to grade the model. We ran four judges, including Jev, TypeSafe’s new System One model, over 230 support conversations with a hidden answer key.
Your eval score moved seven points and nobody can say why. What you have to record for a number about an AI agent to still mean something six weeks later.
GenAI output is probabilistic, so the old question, did it match the label does not work. Why AI products fail silently, and why spot-checking cannot catch it.
Start with a conversation
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.