I review AI products and features using evals, so you can get an independent read on how you perform against the base models — and find out where your product is actually reliable, where it isn't, and what to fix first.
Get startedA buyer asks why they shouldn't just use GPT for this. All you have is your own benchmark — a third party review is proof they'll believe.
You swap models to cut cost. Something quietly gets worse and nobody notices for a month.
Your AI does something wrong in front of a real user, and they find it before you do.
Same work underneath. What changes is who the results are for.
A third party read on how your product performs against the base models, on the tasks your customers actually care about. Something you can put in front of a prospect who asks why they shouldn't just use GPT — or in front of your board.
Pressure-testing and eval work that tells you where your product is reliable, where it breaks, and where the biggest opportunities to improve are. For you, not for anyone else.
There's no single process. Usually some combination of:
A third party read on how your product performs — and the eval work behind it — for less than a fraction of what the alternatives cost. Founding rate, no contracts, cancel anytime.
You need someone who's actually shipped this — not someone reading about it.
I'm Justin. I've spent years shipping real AI products — LLM features, RAG systems, and 0→1 platforms taken to enterprise scale — which means I know the specific, unglamorous ways they fail in the wild.
AI generates the options. The hard part is judgment: knowing what "good enough" means for your product, and spotting the failure a generic automated check waves right through. That's pattern recognition built from years of doing it — not theory, and not something you can fully hand to another model.
Before this I worked across Google, Two Sigma, and Oliver Wyman — building data and analytics products and learning how the best organizations make product decisions.
No. I'm not testing whether hackers can break in — I'm measuring whether your product performs, for ordinary users doing ordinary things. Product quality and performance from the user's side of the screen, not cybersecurity.
Introductory pricing for the first few clients. The price goes up once those founding spots are filled. Lock it in now.
Seed through Series A — shipping an AI product to real customers, and starting to get asked how you know it's better than the base model.
AI can help run the tests, but it can't be the final judge of its own output — it shares the same blind spots as the model you're checking. Deciding what counts as a failure, and catching the subtle ones, still takes a human with product judgment. That's the part you're paying for.
Cancel anytime. No contracts, no minimum, no awkward conversation. Just email and you're done.
A few founding client spots at $1,000/month. When they're gone, the price goes up.
Get started today