LENS is LLM observability for AI systems. It ingests traces from every agent, runs an LLM-as-judge evaluation on each answer, and surfaces the weak ones on a dashboard — so bad responses get caught and fixed, not shipped.
Most teams ship AI answers they have never measured. LENS closes that gap.
Every response gets a score from an LLM-as-judge, so you know how good your agents actually are instead of hoping.
Low-quality responses are caught the moment they happen and surfaced for review — not discovered later by a customer.
Weak answers collect in one place, so every fix feeds back into agents that keep getting better over time.
LENS ingests traces from every agent, runs an LLM-as-judge evaluation on each answer, and surfaces low-quality responses on a dashboard so they can be fixed.
Instead of guessing whether your agents are good, you get a scored, searchable record of every answer — and a clear list of the ones that need work.
From raw agent trace to a reviewed fix — no human scoring in the loop.
Every agent sends its traces to an ingest endpoint as it answers, in real time.
A judge queue evaluates each answer for quality with an LLM-as-judge — automatically, at scale.
Low-scoring answers surface on a dashboard for review and fixing, closing the loop.
LENS turns AI quality from a gut feeling into a metric you can act on.
Score every response automatically, so quality holds up across thousands of answers — not just the handful a human happens to read.
Bad answers get flagged the moment they happen and reviewed internally — before they ever reach the people you serve.
Each flagged answer becomes a fix that feeds back into your agents, so the whole system gets measurably better over time.
Get every AI answer scored before it reaches a human — and a live dashboard of the ones that need work. Let's set it up on your stack.