Ground AI in Your Organization's Knowledge
Retrieval Augmented Generation
We build RAG systems that connect LLMs to your data - vector stores, embedding pipelines, knowledge bases, and hybrid search - so AI answers are accurate, current, and sourced.
AI That Knows Your Business
RAG connects language models to your proprietary data - documents, databases, wikis, APIs - so every response is grounded in facts, not hallucinations.
RAG Capabilities
Vector Stores
Purpose-built vector databases with optimized indexing for fast, accurate similarity search across millions of documents.
Embedding Pipelines
Automated document ingestion, chunking, embedding, and indexing pipelines that keep your knowledge base current.
Knowledge Bases
Structured knowledge graphs and document stores that organize your institutional knowledge for AI retrieval.
Hybrid Search
Combine semantic vector search with keyword search and metadata filtering for the most relevant results.
Our RAG Approach
Data Audit
We map your knowledge sources - documents, databases, APIs, wikis - and assess data quality and coverage.
Pipeline Design
Design chunking strategies, embedding models, and retrieval architecture optimized for your use case.
Build & Evaluate
Implement the full RAG pipeline with evaluation metrics: relevance, accuracy, and latency benchmarks.
Deploy & Iterate
Production deployment with monitoring, feedback loops, and continuous improvement of retrieval quality.
0%
Answer accuracy with citations
< 0s
Average response time
0Zero
Hallucination rate (with guardrails)
0M+
Documents indexed per client