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TensorPeak Labs

Knowledge engineering

RAG & Knowledge Systems

Custom RAG development that makes trusted company knowledge searchable, permission-aware, cited, and useful inside products and workflows.

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What this solves

From promising capability to an operating product.

A useful knowledge system does more than place documents in a vector database. It connects trustworthy retrieval, permission-aware data, grounded generation, and measurable quality.

  • Give teams faster access to relevant information
  • Return evidence and citations with generated answers
  • Control access across sensitive knowledge sources

Capabilities

The parts required to make it work.

01

Data ingestion, cleaning, and document processing

02

Hybrid search, embeddings, re-ranking, Pinecone, pgvector, Weaviate, and Qdrant

03

Citation, permission, and freshness strategies

04

Retrieval and answer-quality evaluation

Delivery approach

A clear sequence from uncertainty to operation.

  1. 01

    Audit

    Understand the sources, formats, permissions, update cycles, and user questions.

  2. 02

    Retrieve

    Design chunking, metadata, hybrid search, and ranking around real queries.

  3. 03

    Answer

    Generate grounded responses with citations and safe fallback behavior.

  4. 04

    Evaluate

    Continuously test retrieval coverage, faithfulness, and usefulness.

FAQ

Questions about this service.

Will RAG stop every hallucination?+

No system can promise that. Strong retrieval, citations, evaluations, and fallback behavior can materially reduce unsupported answers and make them easier to detect.

Can permissions from our source systems be preserved?+

Yes. Access control should be designed into ingestion and retrieval rather than added after launch.