From agent logs to the four numbers leadership asks for.
Your agents produce thousands of traces a day. Nobody reads JSON. We turn them into what the business needs to know: what it costs, what it delivers, how good it is and where it fails. ROI, throughput and quality, with a weekly rhythm to act on them.
Business signal
Turn traces, evals and agent logs into dashboards executives read.
What we do
Metrics defined with you
ROI, throughput, quality score, deflection rate, time saved. Written down, agreed, and tied to the traces that produce them.
Evals that score outputs
Automatic scoring with LLM-as-judge plus sampled human review, calibrated every month so the number means something.
Dashboards people read
Per team and per feature, in your BI tool or ours, with a weekly summary that lands in the inbox.
Alerts on what matters
A quality drop, a cost spike or a new failure mode shows up as a message, not as a complaint two weeks later.
How it works
- 1
Define the questions
What does leadership need to decide, and which number would change the decision?
- 2
Instrument
Traces, scores and business events wired to the same identifiers so they can be joined.
- 3
Build the dashboards
One page per audience: executives, product, engineering. No raw logs anywhere.
- 4
Review weekly
A short ritual: what moved, why, what we change. We run it with you until it sticks.
What you get
- Metric definitions document
- Scoring pipeline with LLM-as-judge and human samples
- Dashboards per audience
- Weekly report template and alert rules
Good fit if
- Agents already in production or about to be
- Someone who has to justify the AI budget
- Traces or logs you can access
Not a fit if
- No agents yet (start with Build tools)
- A one-off report instead of an ongoing signal
Stack we use
Questions
Can you use our BI tool?
Yes. We push scores and events to your warehouse and build on Metabase, Grafana, Looker or whatever you run.
How do you measure quality?
Automatic evals plus a sample of human reviews each week, compared monthly so the automatic score stays honest.
What about personal data in traces?
PII is redacted before storage, and the dashboards show aggregates, never individual conversations.