Everything between a deployed model and knowing it's healthy.
ResonanceOps isn't a single dashboard bolted onto your pipeline. It's five connected capabilities that share one trace format, so moving from an alert to the exact request that caused it takes one click, not a cross-tool investigation.
Every call, fully traced
Instrument your model or agent once with the ResonanceOps SDK, and every inference — every span, tool call, and retrieval step — is captured automatically. No sampling guesswork: you decide what's retained and for how long.
Traces are the backbone everything else links back to. A drift alert, a failed eval, an anomalous latency spike — each one points straight to the trace that produced it.
Catch distribution shift before it costs you
ResonanceOps continuously compares live feature and prediction distributions against a learned baseline using PSI, KL divergence, and population stability metrics — not a cron job that runs once a day and hopes nothing changed in between.
Segment drift by any dimension you already track — region, customer tier, device — so you find out which slice of traffic is actually the problem, not just that 'something' drifted.
Score quality continuously, not once at launch
Run evaluators against a sample of real production traffic on a schedule you control. Built-in evaluators cover relevance, hallucination rate, toxicity, and factual consistency; custom evaluators are plain Python, versioned with your model.
Every eval run is tied to a specific model version, so a quality regression shows up as a specific, attributable diff — not a vague downward trend nobody can explain.
Routed to the right place, with the right context
Set thresholds once per metric, per model. When one trips, the alert goes to Slack, PagerDuty, email, or a generic webhook — carrying the trace, the metric history, and the segment it happened in, not just a bare number.
Recurring, acknowledged issues auto-mute so your on-call channel stays something people actually read, instead of something they learn to ignore.
Hallucination rate spiked on billing_agent
2m ago
Latency p95 up 40% on fraud_scoring
26m ago
checkout_amount drift resolved
1h ago
One view per model, not twelve tabs
Latency, throughput, drift score, and error rate for a given model live on a single dashboard your whole team can look at without translating between four different tools first.
Share a dashboard with a read-only link for stakeholders who need visibility but shouldn't be poking at thresholds.
Latency p95
184ms
-12%
Requests / min
9.2k
+4%
Drift score
0.34
+70%
Error rate
0.6%
+0.1%
From first request to first alert
Instrument
Add the ResonanceOps SDK to your inference or agent code — a few lines, no infrastructure change required on your end.
Baseline
We learn the normal shape of your model's inputs and outputs over the first stretch of traffic, so drift detection has something real to compare against.
Monitor and evaluate
Drift checks run continuously. Eval runs execute on the schedule you set, against a live sample of production traffic.
Get alerted
When a threshold trips, the right channel gets a message with enough context to start debugging — not just a reason to open five dashboards.
Fits into the stack you already run
Instrument with the SDK or a generic OpenTelemetry exporter, route alerts wherever your team already looks, and export raw event data to your own warehouse whenever you want it.
See it running against your own model.
Book a walkthrough and we'll show ResonanceOps instrumented against a workload close to yours.