Openlayer

What Openlayer does

Openlayer is an AI Governance Control Plane for Regulated Enterprises, combining policy enforcement, continuous evaluation, and compliance automation in one platform. It helps enterprises prove to regulators and boards that AI policies are enforced in production, generating audit evidence as a byproduct. The platform supports both traditional ML and GenAI systems, handling tasks from data-quality detection to automating comprehensive model evaluations with full traceability across RAG, agents, and complex multi-step workflows. Openlayer aims to bridge the gap between documenting governance and tracing model actions after the fact, enabling safe, reliable, and compliant AI.

Products

• LLM ObservabilityProvides full visibility into LLM pipelines, tracing system behavior, monitoring cost and latency, and catching failures.
• LLM EvaluationOffers rigorous evaluation for GenAI and LLMs, allowing users to test every prompt, trace every response, and validate every output.
• ML ObservabilityContinuously tracks performance, drift, and live issues with automated observability for ML systems.
• ML EvaluationEvaluates ML models with 100+ customizable tests, version comparisons, and automated CI/CD validation to catch regressions.
• Data Quality MonitoringMonitors pipelines for anomalies and schema drift to prevent downstream model failures, ensuring clean and trustworthy data.
• AI Governance & ComplianceAutomates compliance by mapping models to global frameworks like the EU AI Act and NIST, validating, and providing audit-ready evidence.
• AI MonitoringMonitors AI systems, from ML pipelines to LLMs, for accuracy, reliability, and safety without slowing down development.
• AI ObservabilityProvides end-to-end observability for AI systems in production, showing what models are doing and why.
• AI Model EvaluationEvaluates AI models against real-world scenarios, edge cases, and evolving data, going beyond accuracy.
• AI Agent EvaluationTests and validates agentic systems before production, assessing reliability, security, and behavior across dynamic workflows to catch risks like hallucinations, bias, and prompt injection.
• LLM Agent EvaluationHelps test and debug LLM agents with precision and visibility, addressing issues from agent hallucinations to planning failures.
• LLM MonitoringMonitors large language model systems in real time, detecting failures, tracing outputs, and managing costs across applications.
• ML TestingTests every aspect of ML systems, from data validation to behavioral testing, before and after deployment.
• Model MonitoringMonitors performance, detects drift, and catches production issues before they impact users, offering a modern monitoring layer for ML systems.
• AI Agent ObservabilityProvides observability purpose-built for agentic systems, allowing users to understand every action, chain, and decision, detect risks, and maintain control.
• GenAI TestingProvides tools to evaluate generative AI systems across edge cases, hallucinations, prompt quality, and more before they reach users.
• LLM Experiment TrackingGives GenAI teams visibility into prompt iterations, model settings, and evaluation results in one place.
• ML Experiment TrackingHelps ML teams log, compare, and analyze experiments to move faster and ship smarter.
• Model ValidationEnsures models are accurate, fair, and reliable before they go live.
• AI Data ValidationValidates datasets before training or inference, catching errors, drift, and anomalies early.

Key capabilities

• 100+ automated tests for agentic systems
• Real-time guardrails (prompt injection, PII leakage, hallucinations)
• Offline evaluation from prototype to production
• Real-time observability and monitoring of AI systems
• Automated data quality checks (schema changes, drift, anomalies)
• Automated compliance with standards (ISO/IEC 42001, OWASP, NIST, EU AI Act)
• LLM observability with full visibility into pipelines
• Rigorous LLM evaluation with customizable tests and version comparisons
• ML observability for continuous performance, drift, and issue tracking
• ML evaluation with 100+ customizable tests and CI/CD validation
• AI governance tools for automated compliance and audit-ready evidence
• AI monitoring for accuracy, reliability, and safety
• End-to-end AI observability in production
• AI model evaluation against real-world scenarios and edge cases
• AI agent evaluation for reliability, security, and behavior
• LLM agent evaluation for debugging hallucinations and planning failures
• LLM monitoring for real-time failure detection, tracing, and cost management
• ML testing for data validation and behavioral testing
• Model monitoring for performance, drift, and production issues
• AI agent observability for understanding actions, chains, and decisions
• GenAI testing for hallucinations, prompt quality, and edge cases
• LLM experiment tracking for prompt iterations and evaluation results
• ML experiment tracking for logging, comparing, and analyzing experiments
• Model validation for accuracy, fairness, and reliability
• AI data validation for errors, drift, and anomalies
• LLM-as-a-judge testing
• Integration with LLM providers, Git-based workflows, SDKs, and APIs
• Explainability tools for understanding model behavior
• Enterprise-grade security (SOC 2, GDPR, role-based access, SSO, air-gapped deployments, encryption, automated backups, multi-region hosting)

Use cases

• Accelerating evaluation and observability of agentic systems
• Preventing prompt injection, PII leakage, and hallucinations
• Transitioning AI systems from prototype to production safely
• Catching and fixing AI issues in production within minutes
• Detecting bad data before it reaches models
• Aligning AI systems with regulatory standards and generating audit evidence
• Enhancing development and operational efficiency for AI teams
• Tracing system behavior, monitoring cost and latency in LLM pipelines
• Testing performance across prompts, data types, and model settings for GenAI apps
• Debugging prompt injection issues in GenAI systems
• Validating AI models before they go live
• Building AI copilots
• Building summarization tools
• Building customer support agents
• Orchestrating agents in GenAI workflows
• Building retrieval-augmented generation (RAG) systems
• Fine-tuning internal copilots
• Testing and debugging LLM agents
• Monitoring LLM systems in real time
• Testing every aspect of ML systems before and after deployment
• Monitoring performance, detecting drift, and catching production issues in ML models
• Understanding AI agent actions and decisions
• Evaluating generative AI systems across edge cases and prompt quality
• Tracking LLM and ML experiments
• Validating datasets before training or inference

AI approach

Openlayer provides an AI Governance Control Plane for regulated enterprises, combining policy enforcement, continuous evaluation, and compliance automation. It supports both traditional ML and GenAI systems, offering tools for data quality detection, automated model evaluations, and full traceability across RAG, agents, and complex multi-step workflows. The platform offers 100+ automated tests and real-time guardrails to prevent issues like prompt injection, PII leakage, and hallucinations.

Tech named: ML, LLM, GenAI, RAG, agents, scikit-learn

What it says sets it apart

• Combines policy enforcement, continuous evaluation, and compliance automation in one platform
• Closes the loop between documenting governance and tracing model actions after the fact
• Designed to support both traditional ML and GenAI systems
• Provides full traceability across RAG, agents, and complex multi-step workflows
• Offers 100+ automated tests and real-time guardrails
• Seamlessly integrates into existing workflows with SDKs and APIs
• Goes beyond accuracy in AI model evaluation, considering real-world scenarios and edge cases
• Built for modern GenAI operations, offering visibility for dynamic LLM workflows
• Offers a modern monitoring layer designed for real-world ML systems
• Provides observability purpose-built for agentic systems
• Supports a wide range of integrations with LLM providers, frameworks, and data platforms
• Enterprise-grade security with SOC 2, GDPR compliance, and secure deployment options