Knowledge architecture
Source inventory, permission model, freshness requirements, content structure, and query patterns translated into a retrieval design.
Enterprise RAG development
RAG systems people can verify and trust.
We build retrieval and knowledge systems that return grounded answers, show their evidence, respect access controls, and improve through measurable evaluation.
Discuss your systemWhat the engagement solves
Build the operating system, not only the demo.
Enterprise RAG quality depends on the entire retrieval path: source ingestion, document structure, permissions, chunking, indexing, query understanding, ranking, context assembly, and answer evaluation. Unleashs diagnoses and engineers that full path so teams can move beyond impressive demos toward dependable knowledge products.
- Increase answer relevance with retrieval tuned to the structure and language of your domain.
- Preserve citations, source freshness, permissions, and lineage across the knowledge lifecycle.
- Replace subjective testing with repeatable retrieval and answer-quality evaluation sets.
What we deliver
A complete production path.
Ingestion and indexing
Reliable parsing, normalization, chunking, metadata, embeddings, hybrid search, and incremental update pipelines.
Retrieval and generation
Query transformation, filters, hybrid ranking, reranking, context assembly, citations, and confidence-aware responses.
Evaluation and monitoring
Golden question sets, recall and ranking metrics, groundedness checks, trace review, and production feedback loops.
- 01
Measure the baseline
Test representative questions and locate whether failures begin in source coverage, retrieval, ranking, or generation.
- 02
Design the retrieval path
Choose parsing, chunking, metadata, search, and reranking patterns around the actual corpus.
- 03
Validate end to end
Evaluate retrieval and answer quality separately, including permissions, citations, latency, and cost.
- 04
Operate and improve
Monitor source freshness and query failures, then turn real usage into controlled evaluation improvements.
Representative technology
WeaviateQdrantPineconePostgreSQLLlamaIndexLangChainOpenSearchMLflow
Frequently asked
What teams ask first.
How do you improve an underperforming RAG system?
We first separate retrieval failures from generation failures, then test source coverage, parsing, chunking, metadata, hybrid search, ranking, and context assembly against representative questions.
Can RAG respect document permissions?
Yes. Access control should be enforced during retrieval with user or group entitlements carried into the index and query filters, then verified with authorization tests.
Which RAG metrics matter?
Useful measures include source coverage, retrieval recall, ranking quality, groundedness, citation correctness, answer relevance, latency, cost, and the rate of safe abstention.
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