Enterprise RAG development that gives answers you can trust.
Retrieval-augmented generation (RAG) lets an AI assistant answer from your own documents instead of guessing. Techcream builds enterprise RAG systems that ingest your PDFs, contracts, tickets and wikis, retrieve the right passages with hybrid search, answer with citations, and respect who is allowed to see what.
Every company is sitting on a knowledge goldmine
Your team already has the answers — buried in thousands of documents, tickets and chats. Generic chatbots hallucinate because they don't know your business. A well-built RAG system grounds every answer in your sources, so support, sales, legal and ops teams find the right answer in seconds.
From scattered documents to cited answers
Hybrid retrieval finds the right passages; permissions decide who can see them.
Your knowledge
- PDFs & scans
- Contracts
- Tickets
- Wikis / Notion
Ingest
- OCR & parsing
- Chunking
- Embeddings
- Sync jobs
Hybrid retrieval
- Keyword search
- Vector search
- Re-ranking
- Access control
Answer
- Grounded LLM
- Citations
- Confidence
Where you work
- Slack / Teams
- Helpdesk
- Web app / API
Everything needed to go live.
Ingestion pipeline
PDFs, Word, scans (OCR), tables, Notion, Confluence, Google Drive, Zendesk, databases — kept in sync.
Hybrid retrieval
Keyword + semantic search with re-ranking, tuned on your real questions.
Answers with citations
Every answer links to the exact source passage so users can verify it.
Access control
Role- and document-level permissions so people only see what they're allowed to.
Evaluation & monitoring
Accuracy test sets, feedback loops and dashboards for answer quality and cost.
Where your team works
Slack, Teams, web app, your helpdesk or an API — not another tool to learn.
From first call to production.
- 1
Audit your knowledge
Inventory sources, formats, freshness and permissions.
- 2
Build the pipeline
Chunking, embeddings and hybrid search tuned to your content.
- 3
Evaluate on real questions
Measure accuracy against questions your team actually asks.
- 4
Launch & improve
Roll out, collect feedback, and keep raising answer quality.
Where it pays off first.
- ✓Support assistants that draft cited replies from past tickets and docs
- ✓Sales assistants that answer RFPs and security questionnaires
- ✓Legal and compliance search across contracts and policies
- ✓Employee helpdesk for HR, IT and SOPs
- ✓Customer-facing product assistants on your website
We've built this before.
Askspot
Platform for agencies to create AI chatbots trained on their clients' own data.
Blurbb.ai
Collects and summarises content from many sources into concise output.
Custom LMS
Learning platform with resource management and progress tracking for a tutoring business.
Why teams choose Techcream for RAG Systems.
Accuracy first
We measure retrieval and answer quality on your data before launch — not after complaints.
Private by default
Deploy in your cloud or on-prem with private models when data can't leave — see Private AI Stack.
Agentic when needed
When one search isn't enough, agents plan multi-step lookups and cross-check sources.
Built to maintain
Sync jobs, monitoring and clear ownership so it stays accurate as documents change.
RAG Systems, explained.
RAG is a technique where an AI model first retrieves relevant passages from your own data, then generates an answer grounded in those passages — reducing hallucinations and letting the answer cite its sources.
Hybrid RAG combines keyword search and semantic (vector) search, often with a re-ranker. It finds exact terms like product codes and also understands meaning, which improves accuracy on business documents.
For knowledge that changes, usually yes: RAG updates as documents change and can cite sources. Fine-tuning is better for style or narrow tasks. Many production systems use both.
Yes. We can deploy entirely within your cloud account or on-premise, using private or open-source models so documents never leave your environment.
Let's scope your RAG Systems project.
Tell us about the workflow or problem. We'll reply within 24 hours with initial thoughts and a time to talk — no sales pitch.
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