Manish Gaud

— SERVICE / BUILD

RAG System Build — production-grade

A production-grade RAG system built to your data, with the isolation and observability that hold up after launch.

Four to six weeks of senior architecture and AI-assisted implementation. Multi-tenant from day one. Retrieval that holds up under load. Observability you can actually debug with.

— Investment
$4,500–6,500
— Timeline
4–6 weeks
Book a discovery call →

— Who it's for

Right fit when…

  • — You have a document corpus and want a chat-with-documents interface for your team or customers.
  • — Off-the-shelf tools (generic vector apps, LangChain demos, ChatGPT plugins) don't isolate per-tenant or per-user the way you need.
  • — You need the system in production — auth, billing, audit logs, the full operational shape — not a notebook.
  • — You want to own the code afterwards, not be locked into a vendor.

— Deliverables

What you get

  • — A working RAG system deployed to your infrastructure (or mine, your choice).
  • — Multi-tenant or per-user data isolation enforced at the database, not the application.
  • — Document ingest pipeline covering PDF, DOCX, TXT, and one optional format (OCR, HTML, or Markdown).
  • — Chat UI with streaming answers and source citations.
  • — Per-tenant usage metering with daily caps and rate limits.
  • — Observability dashboards (Sentry or equivalent) and a runbook for the team that will own the system after.
  • — All source code, in a repository you own.

— Process

How it runs

  1. 01

    Discovery call

    30 minutes. Fit check, scope, what data you're chatting with and who the users are.

  2. 02

    Architecture brief

    Week 1. A written specification covering data model, retrieval strategy, isolation, deployment shape, and observability. You read it before code starts.

  3. 03

    Architecture approval

    You and your team sign off on the architecture brief. No code is written until the architecture is locked.

  4. 04

    Implementation

    Weeks 2–5. Backend, frontend, ingest pipeline, observability, and deployment. Reviewed weekly with you.

  5. 05

    Acceptance week

    Week 6. Your team uses the system, we close gaps together, and I hand off the runbook.

— FAQ

Honest questions

Why is the price range so wide?
It covers the difference between "one format, English only, single tenant" and "multi-format, multilingual, multi-tenant with usage caps." The discovery call narrows it before we agree on scope.
Do you use my LLM provider or yours?
Yours. The architecture is provider-agnostic and supports OpenAI, Anthropic, Bedrock, or local models — your choice, with the trade-offs written down.
What about fine-tuning?
Not in this engagement. Most teams discover that better prompts and better retrieval beat fine-tuning for their problem. If yours genuinely needs it, I'll say so and recommend a path forward.
Will my data leave my infrastructure?
Only what's sent to the LLM provider per your configuration. Data residency, region, and provider choices are yours to make; I implement them.
Can you integrate this with my existing app?
Yes. I prefer to ship the RAG system as a self-contained service with a clean API, then your team wires it into the product surface. That keeps the boundary clean and the system easy to upgrade later.

Building a production RAG system and tired of demos that don't survive the second customer?

Let’s talk.

Book a discovery call →