Freelance Engagement — AI Assistant & RAG

Dustin Weaver

AI Assistant & RAG Integration for SaaS Teams

Ship a production-grade AI chat or search feature on your existing product — without hiring an ML team.

Most “AI features” are a thin wrapper

A chat API bolted onto a product isn’t the same as AI grounded in your own data. It hallucinates, ignores context, and erodes trust in the feature fast. Building it right takes retrieval engineering most product teams don’t have in-house — and hiring for it is slow and expensive.

Two ways to work together

01 — Audit

AI Assistant Audit & Roadmap

$1,500–2,500/ 3–5 days

  • Review of your product, data sources, and any AI already attempted
  • Architecture plan: vector store, ingestion & sync pipeline, retrieval chain design, personalization approach
  • Delivered as a written roadmap, plus a walkthrough call

Best for: teams who want AI in the product but aren’t sure how to build it — or want a second opinion before committing engineering time.

02 — Sprint

AI Assistant MVP Sprint

$8,000–15,000/ 2–3 weeks

  • Document/data ingestion & sync pipeline
  • Vector store and retrieval chain(s), tuned to your data
  • Chat interface integrated into your existing UI
  • Optional: cross-session memory & personalization

Best for: teams ready to ship a real AI feature fast, without a multi-month ML hiring process.

Why me

How it works

  1. 1

    Intro call

    30 minutes — your product, your data, your goals.

  2. 2

    Fixed-price proposal

    Scoped to exactly what you need. No surprises later.

  3. 3

    Build

    Audit or sprint, with regular check-ins.

  4. 4

    Handoff

    A working feature and documentation — or a roadmap your team can run with.