Your entire AI stack. One Quality Index.

Industry-standard observability protocol for traces, metrics, and logs.

Universal coverage — if your tool emits OTel, we ingest it automatically.

LLM tracing, dataset management, and evaluation by LangChain.

Sync eval scores and datasets directly into your Quality Index.

Code hosting, pull requests, and CI/CD workflows.

Link deploys to quality changes and auto-generate remediation PRs.

Open-source LLM observability with tracing and prompt management.

Forward traces and user feedback to build a complete quality picture.

CLI-first eval framework for testing LLM outputs in CI/CD.

Run evals on every PR and feed pass/fail results into the Task Quality pillar.

Automated evaluation metrics purpose-built for RAG pipelines.

Measure faithfulness, relevance, and context precision per retrieval.

Experiment tracking and eval platform for LLM applications.

Sync experiment results so every iteration is tracked in the Quality Index.

Unit-test style evaluation framework for LLM outputs.

Run 14+ metrics (hallucination, bias, toxicity) and report as pillar scores.

Open-source observability platform for LLM traces and embeddings.

Visualize embedding drift and annotation scores alongside your index.

Programmable guardrails with 50+ validators for LLM I/O.

Per-validator pass/fail rates feed directly into your Safety score.

Real-time prompt injection and data leakage detection.

Every blocked attack attempt raises your Safety score automatically.

NVIDIA's toolkit for controllable and safe LLM conversations.

Track rail activations and topical guardrail compliance over time.

Unified API gateway supporting 100+ LLM providers.

One integration captures cost, latency, and errors across all your models.

ML experiment tracking, model registry, and dataset versioning.

Sync run metrics so model upgrades are reflected in your Quality Index.

Open-source platform for ML lifecycle management.

Track experiment runs and model versions alongside quality trends.