Catch the regression before your customers do.
We build observability for production AI, drift detection, hallucination tracking, in production evaluation, latency and cost monitoring, and alerting that fires on the things that actually matter.
What we
actually watch.
Latency dashboards aren't enough, and pure model quality dashboards miss the operational picture. We instrument both, so you see the full health of your AI in one place.
Drift Detection
Input drift, output drift, and concept drift, measured continuously and alerted before users feel the regression.
In Production Evaluation
Sample real traffic and score quality against your eval suite continuously, so you know if quality is drifting up or down.
Hallucination Tracking
Citation grounding, factuality checks, and refusal rate metrics, surfacing when the model starts making things up.
Latency & Throughput SLOs
p50/p95/p99 with token level breakdowns, queue depth, and saturation indicators, burnable error budgets for AI services.
Cost Observability
Per request, per customer, per feature cost tracking, so you can see exactly which use cases are profitable and which are leaking money.
Smart Alerting
Composite alerts that combine quality, latency, and cost signals, so on call wakes up for real issues, not noisy drift in a single metric.
From dark stack to queryable history.
Most AI deployments are dark in production. We instrument them so every interaction is observable, queryable, and replayable.
Telemetry Audit
Map what's already being captured, where the gaps are, and what regressions you've missed in the past because the data wasn't there.
Instrumentation
OpenTelemetry traces, structured logs, and quality probes wired into the request path, without inflating latency.
Dashboards & SLOs
Dashboards your on call can actually read. SLOs that map to user pain. Alerts tuned to your burn rates, not vanity metrics.
Drill & Iterate
Game day exercises with synthetic regressions to validate alerting paths, then ongoing tuning as your usage grows.
Questions about
Monitoring & Observability
Datadog covers infra and latency well. AI observability adds quality, drift, and cost tracking, usually as a thin layer that feeds into Datadog and your existing alerting.
OpenTelemetry as the spine. Langfuse, Helicone, or Arize for LLM tracing. Whylogs or Evidently for drift. Prometheus/Grafana for metrics. We integrate with what you already run.
LLM as judge with calibrated rubrics, embedding distance from reference outputs, user feedback signals (thumbs, escalations), and periodic human review of flagged samples.
Yes, most engagements start with a side car instrumentation layer that captures telemetry without touching the core code path.
PII redaction at capture, encrypted storage, role based access. Where regulation requires it, we keep telemetry within your perimeter, see our on premise offering.
Stop experimenting.
Start deploying AI that works.
Book a free discovery call. Tell us what's gone wrong in production lately, we'll diagnose what to instrument first.
info@croncore.com