Agentic Evaluation & Observability Platform

Enterprise AI Agent Evaluation, Trace-Level Observability, and Continuous Monitoring

Turn Messy Agent Behavior into Measurable Performance

Trace-Level Visualization & Failure Analysis

Segmenting traces and error patterns so you see utility and performance related failures.

Custom, Reusable Rubrics

Human expert- and LLM-judge–based custom rubrics with graded templates for correctness, safety, UX, and compliance.

Human-Reviewed Synthetic Data at Scale

Capture your domain-specific definition of “good” and reuse it across test sets, judges, and alerts.

Auto-Evaluators (LLM-as-a-Judge)

Measure rubrics via LLM-as-a-judge or code-based evaluators for consistent, automated scoring.

Continuous Performance Monitoring

Continuously evaluate production rollouts for regressions in quality, safety, or latency.

Tailored to Your Role.

Product

  • Know where agents fail and why, without reading thousands of logs.
  • Align stakeholders around clear rubrics for ‘ship / no-ship’ decisions.
  • Prove impact of changes with evaluation-driven metrics.

Engineering

  • Integrate tracing and evaluation with your existing stack.
  • Catch regressions in staging and production before customers see them.
  • Automate evaluation via APIs and CI pipelines.

Risk & Compliance

  • Codify safety and compliance expectations as rubrics.
  • Generate auditable evaluation reports for internal and external stakeholders.
  • Continuously monitor for policy violations in live traffic.

Why Innodata vs Typical LLM Observability Tools

Most LLM observability platforms were built for model metrics and application logging, not for regulated agentic workflows that must withstand regulatory, legal, and board scrutiny.

Area Typical LLM Observability Tools Innodata Agentic Evaluation & Observability Platform
Primary Focus Token-level metrics, latency, generic quality scores Agent-level reasoning, tool use, and workflow completion across full lifecycles
Configuration Burden Heavy user configuration of metrics, dashboards, and checks Pre-configured evaluation system, guided workflows, and expert-supported rubrics
Compliance & Audit Limited or generic audit support Audit-grade logging with GDPR, HIPAA, SOX experience and exportable reports
Business KPI Linkage Technical metrics loosely connected to business goals Rubrics co-designed with your teams to mirror operational and financial KPIs
Agent Orchestration Coverage Limited multi-step and tool-using agent evaluation Deep support for tool-chaining, orchestration quality, and multi-agent workflows
Vendor / Model Neutrality Often tied to a particular stack or ecosystem Fully model and vendor agnostic across frameworks and AI vendors
Services & Advisory Product only, minimal evaluation design support Embedded advisory on rubric design, failure analysis, and governance orchestration

Choose Your Deployment Method

Managed Evaluation Services

Platform-Powered, Expert-Operated

Platform-powered evaluation delivered with white-glove support. Innodata experts manage rubric design, failure analysis, and ongoing optimization using the same infrastructure.

Best for organizations that require accelerated rollout, operate in highly regulated environments, or lack internal evaluation capacity.

Expert-Guided Platform Enablement

Structured Setup and Custom Configuration

Innodata works alongside your team to onboard priority use cases, design domain-specific rubrics, and configure evaluation workflows. You operate the platform. We ensure it is production-ready.

Best for organizations that require custom setup but plan to manage evaluation internally.

Platform License

Self-Managed Evaluation Infrastructure

Full platform access for your AI and data science teams to configure rubrics, run evaluations, monitor agents, and manage governance workflows internally. 

Best for enterprises with established AI Centers of Excellence seeking full operational control.

Start with an Evaluation Strategy Session

In a 30-minute consultation, our experts will review your agent roadmap, evaluation approach, and production blockers.

We’ll identify where structured evaluation, KPI-aligned rubrics, and trace-level observability can accelerate deployment — and outline how a tailored walkthrough would look for your environment.

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