Build AI Agents That Actually Understand Your Data
RAG isn't enough. Your agents need structured metadata, precise filtering, and intelligent retrieval to complete complex tasks. Ship agentic workflows in days, not months.
Know Your Agents Will Work Before You Build Them
Stop discovering retrieval problems after weeks of development. Synthetic benchmarking lets you test embedding models and chunking strategies on your actual data, ensuring optimal accuracy before you commit to a pipeline.
- Test before you build.
- Evaluate different embedding models and chunking strategies on YOUR data before writing a single line of code. Know your retrieval accuracy upfront.
- Data-driven optimization.
- Compare NDCG scores, relevancy metrics, and latency across configurations. Make informed decisions based on real performance data, not guesswork.
- Ship with confidence.
- Avoid weeks of rework from poor retrieval. Our synthetic benchmarks ensure your RAG pipeline delivers accurate results from day one.
RAG Pipelines
End-to-End AI Data Infrastructure
Stop stitching together disparate tools. Vectorize RAG pipelines handle document ingestion, processing, vectorization, and retrieval, all optimized for how AI agents actually work.
- Complete SDLC for AI.
- From ingestion to evaluation, manage your entire RAG lifecycle in one platform. No more juggling multiple tools or building custom infrastructure.
- Iterative refinement.
- Test, tune, and optimize your pipelines with built-in evaluation tools. See exactly how changes impact retrieval accuracy before deploying.
- Production-ready APIs.
- Deploy with confidence using battle-tested retrieval endpoints. Automatic query rewriting, re-ranking, and metadata filtering built in.
Vectorize Iris
Turn Complex Documents Into Structured Knowledge
Multi-column layouts. Embedded tables. Confusing formatting. Iris cuts through it all to produce structured Markdown with precision, and it's available inside every RAG pipeline.
Complex document intelligence
Product manuals, research reports, technical docs. Vectorize Iris handles dense layouts, embedded diagrams, and multi-column text.
Smart chunking for agents
Preserve semantic structure. Keep related content grouped so agents get full context, not fragments.
Schema-based extraction
Extract structured metadata using your own schema. Enable agents to filter, reason, and retrieve with precision.
Fine-tuned for retrieval
Optimize chunking and metadata for how your agents actually retrieve. Build with real usage in mind, not guesses.
Your data tells a story.
Now you can hear it.
Agentic RAG-as-a-service has deep research on private data built right in. This transforms your entire knowledge base into comprehensive intelligence reports in minutes. Uncovering insights that would take analysts weeks to find.
Report ready in 2 minutes 34 seconds
📊 Executive Summary
📈 Market Position Analysis
🎯 Competitive Landscape
💡 Strategic Recommendations
Template-Driven
Create custom report structures that ensure consistency across all your research. From sales reports to competitive analysis.
Workflow Ready
Integrate with n8n to trigger reports on schedules, distribute to stakeholders, and incorporate into your business processes.
Hybrid Intelligence
Combine your private data with web research for complete context. All while keeping your confidential information secure.
Bring structure to your unstructured data.
Agentic RAG only works if your data is retrieval-ready. Vectorize gives you the tools to extract structure, preserve context, and build reliable pipelines. Without the complexity.