Sprint · GenAI

GenAI POC SprintRAG or chatbot, in production.

A production-ready RAG system or domain-specific chatbot — grounded, evaluated, and deployed. The fastest path from 'we should try GenAI' to 'we have GenAI in production'.

Tell us your pain — proposal in 5 days All sprints

4–6 weeksGenAI

Who it's for

Is this sprint for you?

Companies with private knowledge that customers/employees ask about
Teams who tried demos and want a real deployment
Use cases bounded enough to ship in 4-6 weeks

Deliverables

What you walk away with.

Production RAG application (or chatbot)
Ingestion pipeline for your data sources
Hybrid retrieval on Azure AI Search
Evaluation harness with groundedness metrics
Quality dashboard
Source code in your repo
30-day support window

Timeline

Week-by-week — 4–6 weeks.

01
Week 1 — Discover
Data inventory, success metrics, architecture decision (prompt vs. RAG vs. fine-tune), eval strategy.
02
Weeks 2–3 — Build core
Ingestion pipeline, retrieval logic, prompt engineering, evals, thin pilot UI.
03
Week 4 — Iterate
Eval-driven tuning, content safety, PII handling, observability.
04
Weeks 5–6 — Harden
Production deployment, FinOps optimizations, runbook, knowledge transfer. Optional based on scope.

Stack

What we use.

The Microsoft AI ecosystem we lean on for this sprint.

Primary stackAzure OpenAIAzure AI SearchAzure AI FoundryDocument IntelligencePromptFlow

Scope

What's not included.

We're explicit about scope so you can decide whether you need to scale up — or stop here.

Multi-agent orchestration (see Agent Pilot)
Net-new model fine-tuning (separate engagement)

FAQ

Common questions.

How much data is too much for 4-6 weeks?: Anything below ~10M documents is comfortable. Above that we still ship, but the ingestion pipeline becomes the longer pole — plan for the 6-week scope or split into phases.
Do you ship the UI?: We ship a thin pilot UI sufficient to validate. Production-grade UI/UX is a separate workstream we can bring in or hand off.