Debug and evaluate RAG applications

Connect your data to LLMs in a safe and systematic way.

Unified OpenTelemetry traces across both ingestion and retrieval pipelines
to help you improve RAG performance.

Run evals and compare experiment runs to methodically identify the best combination of chunking strategy, embedding model, retrieval algorithm & more.

Need help finding a better chunk size?

Detect hallucinations with small, targeted evaluator models

Automated evaluators to benchmark your RAG systems.

Faithfulness evaluators that outperform GPT-4 and run at 1/1000th the cost.

Best of all, they can be fine-tuned for other RAG evals, like answer relevance, and customized with your application data (you own the model).

Trying to evaluate your RAG application performance?

Version control and optimize prompts with AIConfig

Version, evaluate & optimize prompts and model parameters with an open-source framework that manages prompts as YAML configs.

Use AIConfig SDK to decouple model-specific logic from your application code and swap model providers with ease.

Use AIConfig Editor as a universal prompt playground to optimize prompts for any model or modality, including text, image and audio.

API gateway to access models from any provider behind a single API

Containerized microservices that handle model inference, routing, response caching, monitoring and rate limiting.
All behind a unified API gateway.

Provision AI Service Mesh within your organization cloud to provide streamlined access to generative AI with admin controls and safety guardrails.

Manage approved models, track costs, monitor usage, enforce rate limits and more.

Need help deploying a production-ready RAG application?

Getting a generative AI application from prototype to production is hard.

Let us help you.
Our team of engineers and ML researchers can help build, debug and evaluate enterprise-grade RAG applications.