AI That Knows Your Enterprise Data
We build RAG systems that retrieve accurate, grounded answers from your enterprise knowledge base — documents, databases, images, and more — with the reliability production demands.
Planner · Retriever · Reasoner · Validator
Four layers designed for retrieval accuracy that enterprise use cases actually need.
Planner
Understands the query intent, identifies the right retrieval strategy, and coordinates the search across document types and data sources.
Retriever
Executes hybrid search — dense vector similarity plus sparse keyword matching — with re-ranking to surface the most relevant content.
Reasoner
Synthesizes retrieved content into accurate, grounded answers. Handles multi-hop reasoning across multiple document sources.
Validator
Checks that answers are grounded in retrieved sources, flags low-confidence responses, and routes ambiguous cases for review.
What Every RAG System Includes
Vector Database Integration
Pinecone, Weaviate, Qdrant, pgvector — we integrate with your existing stack or help you choose the right database for your use case.
Hybrid Search
Combine dense vector search with sparse keyword matching and re-ranking to maximize retrieval precision across diverse document types.
Document Ingestion
Automated pipelines that ingest, chunk, embed, and index PDFs, Office documents, emails, web content, and structured data.
Multimodal Reasoning
Process and reason over text, tables, charts, images, and mixed-format documents together — not just the text layer.
Common Questions
What is RAG and why does my enterprise need it?
RAG (Retrieval-Augmented Generation) lets AI systems answer questions grounded in your actual enterprise data — documents, databases, policies, and internal knowledge — rather than relying on generic training data. This is how you build AI that knows your business.
What file types and data sources do you support?
We ingest PDFs, Word documents, Excel files, HTML, emails, images, slide decks, databases, and structured data feeds. Our multimodal pipelines can process text, tables, charts, and images from the same document together.
How accurate is the retrieval?
Accuracy depends on data quality and system design. We use hybrid search (dense vector + sparse keyword), re-ranking, and a Validator agent layer to significantly improve precision over naive RAG implementations. We measure and report retrieval accuracy metrics throughout the engagement.
What vector databases do you work with?
We work with Pinecone, Weaviate, Qdrant, pgvector, and Chroma. We select the right database based on your scale, query patterns, and infrastructure preferences. We are not tied to any vendor.
Ready to Build Your RAG System?
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