Buried Knowledge

The Document Problem Most AI Tools Don't Solve

Traditional search tools return documents, not answers. Generic AI tools make up answers because they haven't read your files.

Buried PDF Information

Your business runs on contracts, technical manuals, compliance policies, and reports. They are stored across SharePoint, email, and shared drives. Nobody can find what they need quickly, and knowledge is buried in PDFs.

Hallucinating Demos

Generic AI tools give confident answers based on nothing, because they lack access to your actual files. Search tools index files but don't synthesize answers, returning documents instead of cited answers.

The RAG Solution

Vector Ingestion

Documents are chunked, converted to vector embeddings, and indexed securely.

Synthesized, Cited Answers

The system retrieves the most relevant passages from your actual content and uses an LLM to synthesize cited, accurate answers without guessing.

Applications

Document intelligence systems we build

We engineer RAG pipelines for specific data sources, query behaviors, and sovereignty demands.

  • Document Q&A Systems

    Ask questions of your library in plain English. Runs privately in your secure cloud environment.

    Q&ASovereignty
  • Contract Intelligence

    Extract terms, obligs, dates, and non-standard clauses. Compare agreements against templates.

    ContractsCompliance
  • Compliance & Policy Search

    Give staff a way to query compliance procedures. Returns the exact policy section in seconds.

    PoliciesSOPs
  • Technical Manual Search

    Make technical guides searchable by symptoms or components, returning exact page numbers.

    Field OpsManufacturing
  • Data Extraction Pipelines

    Extract structured data from unstructured invoices or forms. Output to databases or ERPs.

    ExtractionPipelines
Our Methodology

How We Build It

We don't use off-the-shelf wrappers. We build custom RAG pipelines optimized for precision.

  • Multi-Source Ingestion

    Connect PDF, Word, Excel, email, HTML, SharePoint, and custom databases.

  • Chunking Strategy

    Semantic, paragraph, or sliding window partitioning matching your document layout.

  • Sovereign Embeddings

    Azure OpenAI, OpenAI, or local open-source models for data compliance.

  • Retrieval Optimisation

    Hybrid vector search, metadata filtering, and semantic re-ranking (Cohere).

Pricing

RAG Implementation Pricing

Transparent, milestone-based tiers matching the size of your document corpus.

RAG Pilot

One document type, one use case, deployed in a sandbox. Answers whether RAG works for your queries.

£5,000–£10,000/month
Start RAG Pilot
What's included
  • 1 document type
  • Sandbox environment UI
  • Accuracy evaluation
  • Delivered in 2-3 weeks
Popular

Production RAG System

Full production deployment. Multiple document sources, ingestion pipeline, re-ranking, API/UI, and monitoring logs.

£15,000–£35,000/month
Deploy Production RAG
What's included
  • Multiple document sources
  • Automated ingestion pipeline
  • Access controls (RBAC)
  • Custom UI/API integration

RAG Support Retainer

Post-deployment maintenance: document updates, model upgrades, prompt tuning, and performance audits.

£1,500/mo/month
Select Retainer
What's included
  • Prompt & embedding tuning
  • Document library sync support
  • Model performance audits
  • SLA support hours

Make your enterprise documents smart

Stop searching. Start finding. Contact our RAG architects to scope a secure sandbox pilot.

Techseria

Engineering the enterprise of tomorrow — from strategy through operations.

UK Address

Techseria (UK) LTD 71-75 Shelton Street, Covent Garden, London, WC2H 9JQ

India Address

Techseria Private Limited G-1209, Titanium City Center, 100 Feet Shyamal Road, Satellite, Ahmedabad – 380015

© 2026 Techseria Technologies, Inc. All rights reserved.