We build production-grade RAG systems that deliver accurate, citation-backed answers from your proprietary data — not generic LLM guesses. Advanced retrieval, hybrid search, and eval-driven iteration.
Core Capabilities
Context-aware chunking, metadata extraction, and hierarchical indexing that preserves document structure — far beyond naive text splitting.
Combine semantic vector search with BM25 keyword retrieval and re-ranking models to surface the most relevant context for every query.
Route different query types to the right retrieval strategy — exact lookups, semantic search, or structured database queries — automatically.
We measure retrieval precision, answer faithfulness, and relevance using RAGAS and custom evals before and after every change.
Every answer cites its source. Hallucination guardrails and confidence scoring ensure your users receive only grounded, verifiable responses.
Vector stores, document pipelines, and API layers designed to handle millions of documents and thousands of concurrent queries without degradation.
Technology Stack
We select the right tools for your scale, infrastructure, and retrieval requirements — no one-size-fits-all stack.
Our Approach
We audit your data sources, document types, and query patterns to design the right retrieval architecture before writing a line of code.
We build a working baseline RAG system and run it through an evaluation suite to establish a quality benchmark.
We improve chunking, retrieval, and prompting in data-driven iterations — every change is measured against the eval benchmark.
We deploy to your cloud infrastructure with monitoring, alerting, and documentation. Your team owns the system end-to-end.
Let's scope your RAG project. Fixed pricing, no hourly billing, real engineers.
End-to-end LLM, RAG, and computer vision systems for production.
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