Welcome to Vecta

Vecta is the essential MLOps platform for building reliable, production-ready RAG (Retrieval-Augmented Generation) systems. We help AI teams evaluate, benchmark, and optimize their retrieval systems with confidence.

What is RAG Evaluation?

RAG evaluation is the process of measuring how well your retrieval-augmented generation system performs. This includes:

Retrieval Quality: How accurately your system finds relevant information
Generation Quality: How well your LLM produces answers from retrieved context
End-to-End Performance: Overall system reliability and user satisfaction

Why Choose Vecta?

🎯 Precision at Scale

Measure retrieval quality with granular metrics (chunk, page, document level) and track performance across thousands of queries.

🔧 Universal Compatibility

Works with 50+ integrations including all major vector databases, LLM providers, and RAG frameworks.

📊 Actionable Insights

Transform raw metrics into clear recommendations with interactive dashboards and automated alerts.

🔒 Enterprise Security

SOC 2 certified with end-to-end encryption, RBAC, and compliance with GDPR & CCPA.

How Vecta Works

Connect Your Data: Link your documents, vector databases, or existing RAG pipelines
Generate Benchmarks: Our AI creates comprehensive test datasets tailored to your domain
Run Evaluations: Test retrieval, generation, or full RAG pipeline performance
Monitor & Optimize: Get real-time insights and recommendations for improvement

Getting Started

Ready to start evaluating your RAG system? Choose your path:

API Quickstart → - Get started in minutes with our hosted platform
Local Quickstart → - Self-hosted evaluation with full control

Key Concepts

Before diving in, familiarize yourself with these core concepts:

Evaluations - Different approaches to testing RAG systems
Benchmarks - Creating and managing ground truth datasets
Metrics - Understanding precision, recall, F1, and more

Need Help?

📖 Browse our complete documentation
💬 Contact our support team
📅 Book a demo with our team
🐛 Found a bug? Report it

Welcome to the future of RAG evaluation! 🚀