Know the moment your model stops behaving.
ResonanceOps is the monitoring layer for AI in production — drift detection, evaluation, and alerting for teams who can't afford to find out from a customer first.
340M+
predictions scored / month
99.98%
monitoring uptime
<90s
median alert time
Powering monitoring for AI teams at
A model doesn't crash when it fails. It just starts being wrong, quietly, in production, for weeks before anyone notices.
Traditional uptime monitoring has nothing to say about that. ResonanceOps was built specifically for the failure modes that only show up in model behavior.
Is your model still the model you shipped?
ResonanceOps watches every feature distribution, prediction, and output against the baseline it learned at launch — not a weekly batch job, a continuous one.
- Statistical drift detection (PSI, KL divergence, population stability)
- Per-feature and per-segment breakdowns, not just a model-wide score
- Baselines that update as your data legitimately shifts
Is it getting better, or just different?
Run evals against production traffic, not just a static test set — accuracy, hallucination rate, toxicity, and any custom scorer your team defines, tracked release over release.
- LLM-graded and rule-based evaluators out of the box
- Custom evaluators in Python, versioned alongside your model
- Score trends tied to specific deploys, not just dates
Who finds out first — you, or your users?
Thresholds you set once route straight to Slack, PagerDuty, or a webhook, with enough context in the alert to start debugging immediately instead of re-pulling the same dashboard.
- Severity-aware routing, not one noisy channel for everything
- Alert payloads include the trace, not just a metric name
- Auto-mute on acknowledged, recurring incidents
Hallucination rate spiked on billing_agent
2m ago
Latency p95 up 40% on fraud_scoring
26m ago
checkout_amount drift resolved
1h ago
Full request tracing
See the whole call, not just the score
Every eval and alert links back to the exact trace that produced it — every span, every tool call, every millisecond — so debugging starts where the problem actually happened.
“We found a fraud model drifting three weeks before it would have shown up in our quarterly loss numbers. That alone paid for a year of this.”
Head of Risk ML
Mesh Analytics
Built for the team that gets paged
ML platform teams
Give every model owner in the org the same monitoring baseline, without becoming the team that manually checks dashboards every morning.
Teams shipping LLM features
Hallucination rate and response quality degrade quietly between model provider updates. Catch it in the eval run, not the support queue.
Fraud and risk models
A silently drifting fraud model is expensive in a way that's hard to notice until the quarterly numbers come in. Don't wait for the quarterly numbers.
Small data science teams
No dedicated MLOps hire yet. ResonanceOps covers the monitoring baseline so the team's time goes into modeling, not building internal tooling.
Plans that scale with prediction volume, not seat count
Start monitoring a single model for free. Add evaluation, alerting, and more volume as your models go into real production traffic.
See full pricingBefore you wire it into production
What kind of models does ResonanceOps monitor?+
Classic ML models (classification, regression, ranking) and LLM-based systems (RAG pipelines, agents, chat). The core primitives — drift, evaluation, alerting — apply to both, with evaluators tuned for each.
Do you need access to our training data?+
No. ResonanceOps monitors what goes into and out of your model in production — inputs, outputs, and optional ground truth when it arrives later. Training data access is never required.
How is this different from a generic observability tool?+
General APM tools tell you a service is slow or down. They don't know what a healthy prediction distribution looks like, or that a 0.71 factual-consistency score is a problem. ResonanceOps is built around model behavior specifically.
Can we self-host?+
Our Scale plan supports a VPC deployment alongside the standard hosted option. Reach out and we'll walk through what that setup looks like for your infrastructure.
Is ResonanceOps a new company?+
Yes — we're an early-stage AI infrastructure startup based in Lalitpur, Nepal. We're working closely with our first cohort of design partners before opening up more broadly.
Start watching your models before they drift on you.
Free up to one model in production. No credit card, no sales call to get started.