See inside your AI in production — quality drift, cost and latency — so problems surface as evidence on a dashboard, not weeks later from a user.
Traditional monitoring tells you whether a service is up. It says nothing about whether your AI is still giving good answers. A model can return a perfectly valid HTTP 200 while quietly degrading — drifting off-topic, getting slower, growing more expensive, or hallucinating more than it did last month — and none of that shows up on an ordinary dashboard.
AI systems fail in ways classic software does not. Output quality drifts as the world changes, costs balloon when a prompt quietly grows, latency creeps up under real traffic, and a bad model update can regress quality across the board without throwing a single error. Without visibility into these, you are flying blind on the parts that matter most.
Observability closes that gap. By tracing every model call — inputs, outputs, latency, cost, and quality signals — you can see problems as they emerge instead of hearing about them from a frustrated user weeks later. It is the difference between running AI on hope and running it on evidence.
We instrument your AI stack to capture what actually matters: every call's inputs and outputs, latency, token cost, and quality signals such as drift and error patterns. We set this up to respect privacy, capturing what you need for debugging without hoarding sensitive content.
Then we build the dashboards and alerts around your real failure modes, so you are warned when quality drifts, cost spikes or latency degrades — before it reaches your users. Where useful, we add automated evaluation on live traffic so quality is measured continuously, not just at release.
See when answers start degrading, before users complain.
Track token spend per feature and catch runaway prompts.
Find where time goes and keep responses fast.
Trace any call end to end for debugging.
Score real traffic continuously, not just at release.
Get warned when a model update lowers quality.
We identify the failure modes that actually hurt you.
We locate the calls and signals worth capturing.
We design privacy-respecting instrumentation and metrics.
We instrument the stack and build dashboards and alerts.
We validate that alerts fire on real problems, not noise.
We hand over the setup with a runbook for on-call.
We build on open observability standards so you keep ownership of your data and avoid lock-in to a single vendor.
It tells you the service is up, not whether answers are still good. AI needs quality, cost and drift signals on top of uptime.
We design capture to respect privacy — enough to debug, without hoarding sensitive content, and with redaction where needed.
No. We build on open standards like OpenTelemetry so your telemetry stays yours and portable.
Yes, with evaluation-as-monitoring on live traffic, complemented by human review where judgement is needed.
Compact, efficient models that run at the edge — low latency, full privacy.
Privacy-safe training data at scale — augment, balance, and unblock ML.
Benchmark, stress-test and adversarially probe models before they ship.
Book a 30-minute call. We will tell you honestly whether we can help.
Book a call