Know how yourAI behavesin production.
Openlayer traces every production request and continuously runs the same evaluations used before deployment, so quality, cost, safety, and compliance remain measurable after launch.
See Openlayer in your stack
Production is where AI becomes unpredictable.
In development, teams test against known datasets and controlled scenarios. In production, AI encounters new users, changing inputs, unexpected tool paths, and behavior no pre-launch test can fully anticipate.
Traditional monitoring can tell you whether a system is available. It cannot tell you whether an answer is accurate, an agent made the right decision, or performance is quietly deteriorating across thousands of interactions.
EU AI Act
ISO/IEC 42001
NIST AI RMF
OSFI E-23
<1hr
to detect a failed production evaluation
175+
automated tests for quality, safety, performance, and compliance
6x
faster deployment after standardizing on Openlayer
Up is not the sameas working.
An AI system can be online and still hallucinate, leak PII, drift, or quietly burn budget. Traditional application and security monitoring can capture uptime, errors, and network activity, but not whether an AI response was correct, safe, or compliant.
When something goes wrong, engineers are left reconstructing the incident across disconnected logs, model calls, prompts, tool actions, and user sessions.
Behavioral failures are invisible to standard monitoring.
Application monitoring can confirm that a request succeeded without detecting that the answer was inaccurate, unsafe, biased, or off-policy.
No complete record of each AI interaction.
Prompts, responses, latency, token usage, cost, evaluations, tool calls, and user feedback live in separate systems, or are not captured at all.
Pre-launch tests stop at deployment.
A system may pass every evaluation before launch, then encounter new inputs, model updates, changing data, and unexpected agent behavior in production.
One standard from development through production.
Openlayer connects every production trace to the evaluations and policies used before deployment. Teams can continuously verify that each system still meets its requirements, see what changed, and investigate failures without rebuilding tests in another tool.
A complete record of production AI behavior
01
Continuous testing on production
Your test suite keeps running after launch, on a rolling schedule of hourly, daily, or weekly. When a test fails, you know within the hour, not at the next audit
02
Complete traces for every AI interaction
Capture inputs, outputs, model calls, tool actions, metadata, latency, token usage, cost, evaluations, and user feedback in one queryable trace.
03
One live feed across every environment
Stream AI interactions from local development, staging, canary, and production into one consistent observability layer.
04
Drift detection
Statistical drift detection on inputs and outputs, so you catch behavior changes before your users or your metrics do.
05
Latency and throughput monitoring
Track end-to-end latency, time-to-first-token, and throughput with P90/P95/P99 percentiles and configurable SLA alerts.
06
Human feedback and annotation
Capture thumbs-up/down feedback and custom labels, then connect every annotation to the exact session, trace, and model response that produced it.
07
Session and cohort analysis
Enrich traces with user and session IDs to analyze performance by segment, geography, or product line.
08
Customizable dashboards
Build dashboards around the quality, cost, latency, safety, and compliance metrics that matter to each team and AI system.
09
Alerting and notifications
Configurable alerts via Slack, Teams, email, PagerDuty, and SMS, scoped to critical-only to reduce alert fatigue.
10
Automated remediation
When tests fail, escalate to on-call, quarantine a trace batch for review, or block the CI/CD pipeline.
Trusted by regulated leaders: Sun Life and Gallagher (insurance); Rogers, KPN, and Comcast (telecom and media).
Backed by Y Combinator and Race Capital. SOC 2 Type II.
Founded by ex-Apple/Siri ML engineers.
Jericho Security: 6x deployment frequency and +53% throughput after standardizing on Openlayer.
Named in the 2026 Gartner Market Guide for AI Evaluation and Observability Platforms.
Endorsed by Guillermo Rauch (Vercel CEO) and Max Mullen (Instacart founder).
