Prove everyAI change is betterbefore you ship it.
Evaluate LLMs, agents, RAG applications, and traditional ML systems across quality, safety, performance, cost, and compliance.
See evaluation in action
Evaluation has become an enterprise control.
Testing AI is no longer only an engineering exercise. Emerging regulations, risk frameworks, and enterprise policies increasingly require organizations to demonstrate how systems were evaluated, which standards they met, and who approved them for use.
Openlayer preserves every dataset, test, result, version, and decision, so teams can improve performance and produce the evidence required for governance, customer reviews, and audits.
EU AI Act
ISO/IEC 42001
NIST AI RMF
OSFI E-23
10x
faster iteration through systematic testing, not trial-and-error prompt changes.
100%
of evals automatically tied to policies required by compliance
6x
Faster deployment after standardizing on Openlayer
You can’t tell if a changehelped or hurt.
Teams change a prompt, swap a model, update a dataset, or modify an agent workflow without a repeatable way to measure the result. A change may improve quality while quietly increasing cost, latency, bias, or failure rates.
Evaluation is often spread across homegrown harnesses, notebooks, prompt registries, and static documents. Results are difficult to compare, reproduce, or connect to what ultimately shipped.
No repeatable way to compare AI changes.
Teams cannot reliably determine whether a new prompt, model, dataset, or architecture improved the metrics that matter without degrading something else.
Evaluation results are scattered and difficult to reproduce.
Test configurations, datasets, results, and model versions live across notebooks and documents with no reliable system of record.
Evidence has to be reconstructed after the fact.
When governance, customers, or auditors ask how a system was tested, engineers spend days finding results and proving which version they belong to.
Evaluation engineering can trust. Evidence governance can use.
Openlayer gives engineering teams the depth to evaluate LLMs, agents, RAG applications, and traditional ML. Compare versions, configure custom metrics, and measure performance across datasets and cohorts.
Every result stays connected to its system version, configuration, policy, and approval. Engineering gets rigorous evaluation. Governance gets continuously updated evidence.
Evaluation for every type of AI system
01
175+ out-of-the-box evals
Evaluate quality, safety, security, performance, cost, fairness, and compliance with configurable tests ready to run across your AI systems.
02
LLM-as-judge evaluations
Use model-based evaluators to score relevance, faithfulness, completeness, safety, tone, and other subjective qualities at scale. Choose the judge model, define the rubric, and preserve both the score and written rationale.
03
RAG-specific metrics
Measure retrieval and response quality using context precision, context recall, relevance, utilization, faithfulness, answer correctness, and groundedness.
04
Fairness and bias testing
Identify performance disparities across demographic, geographic, customer, and patient cohorts using configurable subpopulation analysis.
05
Version tracking and comparison
Track every prompt, model, dataset, and architecture change, then compare evaluation results against previous versions with complete history and reproducibility.
06
Agentic metrics
Measure whether agents complete assigned tasks, select and use tools correctly, follow required steps, and recover successfully when workflows fail.
07
Advanced configuration
Configure dataset versions, time windows, cohort filters, test thresholds, sampling rules, and pass or fail criteria. Drill into any failed segment for deeper analysis.
08
Custom metrics
Define business-specific metrics in code or natural language, including custom rubrics, deterministic checks, and adversarial tests.
09
Traditional ML support
Evaluate classification and regression models in the same workspace using metrics including accuracy, precision, recall, F1, ROC AUC, MAE, RMSE, and R-squared.
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).
