RAG development services: answers from your own documents

Why the first RAG prototype gives vague answers, and what fixes it

A RAG prototype gives vague answers when the parsing and chunking hand the model the wrong context. Slides lose their structure, tables turn into word soup, and chunks cut mid-thought, so retrieval returns passages that look relevant and are not. The model answers confidently anyway. The fix lives in the ingestion pipeline.

We convert each file type in a background job built for it, chunk along the document's own structure (headings, slides, table rows), and store the source location with every chunk so the answer can show where it came from. Conversions run outside the request, so a large upload never freezes the person who dropped it in.

Then we write down the questions your team actually asks, with the answers they expect, and re-run that set every time the pipeline changes. It is the only way to know a chunking tweak improved things instead of breaking the answers nobody checked.

Multi-tenant RAG: keeping each team's documents separate

Multi-tenant RAG means every workspace, team or customer retrieves only its own documents. We enforce that with row-level security in the database (Supabase RLS on the platform we built), so isolation holds even if application code has a bug, rather than relying on one shared index filtered in the app layer.

Tenancy also shapes the cost controls. Prompt caching works on the stable part of each workspace's prompt, and the context budget per request is split between conversation history, retrieved passages and the answer, so one workspace's long documents cannot run up the bill for everyone.

When the assistant proposes a change to the knowledge base itself, a person approves it first. On the enterprise platform that approval step is what let the client trust AI edits in a system their whole company reads.

What RAG development costs and how long it takes

RAG development at Tecifo starts with a discovery sprint from $2,500, which audits your sources and produces the written plan, and a fixed-scope build from $6,000. An illustrative document search and chat product is planned in one to two weeks, built over six to eight, and launched with one team's documents before the rest are added.

The enterprise knowledge platform ran to 1,145 hours over six months, because it started as a live proof of concept and grew into multi-tenant workspaces, background conversion, streaming chat and the approval workflow. A single-team assistant over a few hundred PDFs is a far smaller build. File types, volumes, tenancy and whether the assistant can write back are what move the estimate, and the plan prices each.

When RAG is the wrong tool

RAG is the wrong tool when your documents fit in a single prompt, when the questions are really database queries, or when the content changes faster than you can re-index it. A long-context prompt, a SQL tool or a plain search index each beat RAG in those cases, and cost less to run.

The audit in the discovery sprint settles this before any build. If your knowledge is a few dozen pages, we will say so and put it in the prompt with caching turned on.