We build the evidence layer for AI systems
We built Shyena because we kept seeing the same failure mode: an AI system ships with a clean dashboard full of green checkmarks, and the regression reaches customers anyway. The tools weren't lying, exactly — they were scoring whatever output they happened to collect, even when the underlying run broke down partway through. Nothing crashed loudly, so nothing looked wrong.
Testing tools built for scripted, deterministic software don't hold up against AI that reasons and responds differently on every run. Two runs of the same test can take different but equally valid paths to the same correct outcome — and a script that hardcodes one path calls the other a failure. That's not a bug in the AI. It's a testing model built for the wrong kind of software, and it's the same structural gap whether the system is a conversational agent, a voice bot, or a RAG pipeline.
So we're building an evaluation platform for AI systems generally — starting with conversational and voice AI, where we could prove the model against real, live agents: agentic personas that pursue a goal instead of replaying a script, evaluation that combines LLM judgment with deterministic hard-fact checks, and a gate that treats a broken execution as disqualifying before any quality score gets the chance to look better than it should. The goal isn't a prettier dashboard. It's a verdict you can actually trust before a release goes out, not after a customer tells you it was wrong — for whatever kind of AI system you're shipping.
What we believe
Principles, not features
Evidence over vibes
A verdict has to be explainable. Every score is traceable back to the turn, the assertion, or the judge call that produced it — never a black box you're asked to trust.
No false confidence
A broken run should never look like a passing one. If a conversation didn't complete, the verdict says so — regardless of how well the partial transcript scored.
Built for how agents actually behave
LLM-driven agents don't follow a script, and testing them shouldn't assume they do. Personas pursue goals; the executor improvises the rest.
We're early — building this in close collaboration with the first teams putting it to real use. If that's you, we'd rather show you a real verdict against your own agent than talk in the abstract.
Talk to usSee it evaluate your own agent
Bring one real scenario. We'll run it against your live conversational AI agent and walk through every judged turn with you.