AI Evaluation
Measure your AI systems with the right tools
We build the test set from your own traffic, score correctness, grounding, coverage and behaviour under attack, and hand you one accuracy figure with the working behind it.
The demo passed. That is not the same as the system working.
Between the demo that impressed the room and the system that runs in front of customers, there is a gap nobody has measured. Every answer that lands wrong on the other side of that gap is one someone downstream acts on.
One good average, hiding one bad failure
A single accuracy number smooths over every way the system can be wrong, so a failure that matters sits underneath a headline score that looks fine.
A test set that isn't your traffic
Without a set drawn from what the system actually sees, coverage is a guess, and the edge cases already producing wrong answers go untested.
No number anyone can defend
Ask how much you can trust it and developers, testers and the board each give a different answer, because none of them are working from the same measurement.
Every question you get asked about the AI gets a measured answer.
The questions that stall a release — is it right, is it covered, how far can we trust it — stop being matters of opinion the moment each one has a number attached.
Correctness, broken out
We score the right-answer rate separately for each way the system can be wrong, so a healthy average never hides a failing case.
Coverage you can see
We draw the test set from your real traffic, your edge cases and the failures you have already shipped, so “does it handle everything” becomes a list you can read.
Trust with a number on it
We give you a confidence figure and the working underneath it, so the answer to “how much can we trust this” is the same one whoever asks.
Why measure your AI's accuracy with Avipra?
We measure your system on your own data, using a method we would stand behind in front of your board.
The judge gets judged
We calibrate every automated scorer against human verdicts on your data before we report a single score, so the number means what you think it means.
Pressure is a correctness test
We measure whether the system still answers correctly under injection and sustained attempts to break it, not only whether it refuses.
The number carries downstream
The accuracy bar we set here is the same one your effort estimate and your payback figure are built on, so the numbers agree with each other when it matters.
Ship with the answer already in hand.
When the accuracy question arrives, you are already holding the figure, the method, and the evidence.
Certainty
You know what the system gets right, what it does not, and by how much.
Alignment
Your developers, your testers and your board are all reading the same number.
Scalability
The method holds as the system grows, so the next version is measured exactly the way this one was.
Let's put a number on it.
Tell us what your system does today — running, mid-build, or still on paper — and we will show you what measuring it would look like.