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Service — Retrieval & RAG
Retrieval-augmented generation systems that ground every answer in your documents, databases, and knowledge — with citations, access control, and measured accuracy.
Retrieval-augmented generation (RAG) connects a language model to your own knowledge: documents, wikis, tickets, product data, policies. Before answering, the system retrieves the most relevant passages and grounds the model's reply in them — which is how you get accurate, current, citable answers instead of confident guesses.
The catch is that naive RAG — chunk the PDFs, embed, top-k search — plateaus fast. Real documents have tables, versions, contradictions, and permissions. Aivetech builds the full retrieval stack: document pipelines that preserve structure, hybrid search (semantic + keyword + metadata filters), reranking, freshness handling, and per-user access control so people only ever see answers from documents they're allowed to read.
Customer support assistants that deflect tickets with cited answers; internal knowledge assistants over policies, SOPs, and engineering docs; sales enablement bots over product and pricing collateral; and compliance assistants over regulations and contracts. A RAG layer also becomes the knowledge backbone for agentic systems and voice agents, so one investment powers every AI surface you run.
Every deployment ships with an evaluation set built from your real questions, scored for retrieval hit-rate, answer faithfulness, and citation correctness. We tune until the numbers hold, then monitor them in production — because "it seems to answer well" is not an engineering standard.
Ingestion for PDFs, Office docs, wikis, tickets, and databases — structure-aware chunking, tables preserved, versioning handled.
Semantic + keyword + metadata search with reranking, tuned on your evaluation set, not vibes.
Per-user access control inherited from source systems, and every answer cites the passages it came from.
FAQ
A RAG (retrieval-augmented generation) chatbot retrieves relevant passages from your own documents and data before generating an answer, so replies are grounded in your knowledge base, current, and citable — rather than limited to what the model memorised in training.
Accuracy depends on the retrieval stack, not just the model. With structure-aware ingestion, hybrid search, reranking, and an evaluation set built from your real questions, well-engineered RAG systems reliably answer with citations and flag what they don't know instead of guessing.
Yes. We inherit access controls from your source systems, filter retrieval per user, and ensure no one ever gets an answer derived from a document they can't open.
PDFs, Word and Excel files, Google Drive, Notion, Confluence, SharePoint, helpdesk tickets, websites, and SQL databases — through ingestion pipelines that keep content fresh as sources change.
Book a scoping call. We'll pick one high-value corpus and ship a cited, permission-aware assistant over it in weeks.
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