AI agents that talk to your customers will fail in ways you haven't tested for.
Luku finds the gaps before your customers do.
Critical majority
Already rolled back an AI customer communications agent as a data leak or bad response discovered only after it reached real customers.
Enterprises aren't discovering these problems in testing — they're discovering them in production, from real customers.
We find the gaps, before your users do.
Simulated conversations. Every response, scored. Findings go to your backlog — and become evidence for compliance and sales.
The ways a response can go wrong.
Data that leaks
RP-014Same qualifications, different outcome
RP-052Missing the signs of distress
RP-088Age-inappropriate response
RP-101and growing with every evaluation
From dozens of test cases to thousands
Manual QA covers a handful of scripted conversations. Luku runs each scenario across hundreds of variations — different phrasing, different pressure points, different people — so you're not just checking that the happy path works.
the situations your agent needs to handle correctly, not just the ones you thought to write a test for
the specific ways a response could go wrong for that scenario (privacy, safety, fairness, guidance quality)
who's actually asking. Examples: a distressed user, a first-time customer, a non-native speaker, an adversarial tester probing for weaknesses, a minor or vulnerable user
How it works
- 01
Configure the evaluation.
Define the scenarios, risks, and personas that matter for your product. Set the coverage and depth of testing.
- 02
We run the evaluation.
Tests are executed against your AI agent.
- 03
Get a list of findings.
Findings and vulnerabilities, scored by severity, ready to prioritize — and usable as evidence for compliance reviews or sales conversations.
- 04
Re-evaluate automatically.
Your behavioral regression suite that is triggered by model updates, new features, pipeline changes, new users, or new use cases.
How teams currently catch this — and how they don't
Little to no testing
scattered manual checks, not systematic coverage — gaps you find out about when a customer hits them
looks free — until a rollback, a bad headline, or a compliance question you can't answer
no coverage against the scenarios, risk or user types you didn't think to test, no early warning, no evidence trail
Build it in-house
some coverage — but only against the scenarios, risks and user types you thought to build a taxonomy for
a dedicated team of AI and compliance specialists, plus ongoing upkeep every time a model, prompt, or feature changes
independence — you're still grading your own homework. And depth — testing isn't your core product, so coverage lags behind the risks you didn't think to look for
Luku
continuous evaluation across 100+ risk patterns and thousands of test dialogues — coverage you didn't have to build yourself, and evidence your sales, marketing, and compliance teams can actually use
scoped to the depth of testing you configure — no hiring, no upkeep
you're granting an outside evaluator access to your agent — which is exactly the independence that makes the findings credible in the first place