YourDocuments.Searchable.Instantly.

The Document Problem Most AI Tools Don't Solve

Your business runs on documents — contracts, technical manuals, compliance policies, financial reports, supplier agreements, customer correspondence. They're stored across SharePoint, email, file servers, and shared drives. Nobody can find what they need quickly. Institutional knowledge is buried in PDFs that nobody searches. New staff read the wrong version. Compliance queries take a day to answer.

Generic AI tools like ChatGPT give confident answers about your documents — based on nothing, because they haven't read them. Enterprise search tools index documents but don't understand them. They return the document, not the answer.

Retrieval-Augmented Generation (RAG) is the architecture that closes this gap. Your documents are chunked, embedded, and stored in a vector database. When a question comes in, the system retrieves the most relevant passages from your actual content and uses an LLM to synthesise an accurate, cited answer. The LLM doesn't guess — it reads your documents and tells you what's there.

What We Build

Document Q&A Systems

Ask questions of your internal document library in plain English. The system searches across thousands of documents, retrieves the relevant passages, and returns an accurate answer with source citations. Runs privately — your documents stay in your environment.

Contract and Agreement Intelligence

Extract key terms, obligations, dates, and clauses from contracts at scale. Flag non-standard terms, compare against templates, and answer questions like "which supplier contracts expire in Q3?" or "which agreements contain a termination for convenience clause?"

Compliance and Policy Q&A

Give staff a reliable way to query policies, procedures, and compliance requirements. Instead of hunting through document libraries or asking a colleague, they get an accurate answer with the source policy section — in seconds.

Technical Manual Search

For engineering, manufacturing, or field service businesses: make technical documentation searchable by symptom, component, or procedure. Engineers find the relevant section in seconds, with the exact text and page reference.

Intelligent Data Extraction Pipelines

Extract structured data from unstructured documents at scale — invoices, delivery notes, application forms, survey responses. Output to your database, ERP, or spreadsheet. Handles variation in layout and format.

How We Build It

We don't use off-the-shelf RAG wrappers and call it a day. Each system is engineered for the specific document types, query patterns, and accuracy requirements of your use case.

• Document ingestion — PDF, Word, Excel, email, HTML, SharePoint, and custom formats
• Chunking strategy — paragraph, semantic, sliding window, or hybrid depending on document type
• Embedding model selection — OpenAI, Azure OpenAI, or on-premises models for data sovereignty
• Vector store selection — Pinecone, Weaviate, Azure AI Search, or Chroma
• Retrieval optimisation — hybrid search, re-ranking, metadata filtering
• LLM integration — GPT-4o, Claude, or Azure OpenAI for enterprise compliance
• Answer quality testing — we run evaluation sets against real queries before deployment
• Integration with your systems — SharePoint, email, Slack, ERPNext, or custom frontend

Pricing

RAG Pilot — £5,000–£10,000

One document type, one use case, deployed to a test environment. Answers the question: does RAG work for this document collection and these query patterns? Delivered in two to three weeks with full evaluation results.

Production RAG System — £15,000–£35,000

Full production deployment. Multiple document types, complete ingestion pipeline, retrieval optimisation, user interface or API endpoint, monitoring, and documentation. Deployed to your cloud environment with handover.

RAG Support Retainer — From £1,500/month

Post-deployment maintenance: document library updates, model updates, prompt tuning, performance monitoring, and new document type onboarding. Essential as your document library grows and query patterns evolve.

Why Techseria

• We build for accuracy — RAG is only useful if it answers correctly; we optimise for precision, not just recall
• Privacy-first — your documents never leave your environment; we deploy within your cloud or on-premises
• Full-stack delivery — document ingestion, vector infrastructure, LLM integration, and frontend in one engagement
• Microsoft Solution Partner — experienced with Azure OpenAI and Azure AI Search for enterprise compliance
• Production track record — we have deployed RAG systems for legal, finance, manufacturing, and SaaS companies
• Honest scoping — if RAG isn't the right solution for your problem, we'll tell you what is