6-12 Week Sprint
AI Integration Sprint
Real AI features. Shipped to production.
Add real AI features to an existing product. Not chatbots. Tools, agents, and workflows that move metrics.
Fixed price, set once we scope it over email. A simpler build costs less, a more complex one more.
Your competitors are shipping AI features and you need to ship too. But you have seen what happens when a team bolts on a chatbot and calls it AI: nothing moves, users do not care, and the AI part becomes a maintenance burden. The companies winning with AI right now are not adding chatbots. They are rebuilding workflows where the model takes the boring work, an agent takes the multi-step work, and the user gets a feature that actually changes how their day goes. That is a different engineering problem, and most teams have not shipped it before.
In six to twelve weeks you walk away with production AI features wired into your existing product, on a secure, model-agnostic foundation that every later feature plugs into. The flagship is usually an assistant that knows the data in your product: staff ask questions in plain English and get instant, cited answers drawn from the full record. Around it: RAG that searches and cites your data, tool-calling agents that take actions across your stack, and document intelligence that pulls structured information out of PDFs, emails, and forms. All of it instrumented with evals, behind feature flags, with cost monitoring and prompt versioning. For regulated data it is built HIPAA-safe: sensitive fields are de-identified before the model ever sees them and re-hydrated only on display, with every request audit-logged.
I built Fitly AI with the same patterns: 52 AI tools behind a ten-layer orchestration engine inside a single full-stack product, all in production, all built with Claude Code and Codex from day one. That is the playbook I bring into your codebase. Read the case study for the full breakdown, or see a HIPAA-safe assistant I scoped for a client.
How it breaks down
What happens
Use cases, data sources, success metrics
Discovery over email and a shared doc: the workflows AI will touch, the data the model needs to read or write, and the metrics that will tell us it is working. We agree on what ships first and what can wait.
Model selection, prompt design, eval harness
Pick the right model for each feature (Claude, GPT, Gemini, or local). Design the prompts, retrieval, and tool schemas. Stand up an eval harness so we can measure quality and catch regressions before users do.
Features shipped behind flags
Implement the AI features against the design. Agents do the parallel implementation, I make every architecture, prompt, and product call. Everything ships behind feature flags so you can roll forward safely.
Evals, guardrails, monitoring, cost controls
Run the eval suite against real production traffic. Add guardrails on sensitive flows. Wire up cost dashboards and per-feature spend monitoring. Deploy to all users and watch the metrics that actually matter.
Deliverables
What you walk away with
A data-aware assistant
An assistant that knows the data in your product: staff ask questions in plain English and get instant answers drawn from the full record, with every answer cited back to its source. The digging through screens and reports becomes a single question.
RAG over your private data
Search and Q&A across your docs, customer records, support tickets, or knowledge base. Citations on every answer, hallucination guardrails, and a vector layer that fits your stack (pgvector, Pinecone, or whatever your infrastructure already has).
Tool-calling agents that take actions
Multi-step agents that read records, update systems, send messages, file tickets, draft replies, and close loops between your app and the outside world. Human-in-the-loop where it matters, fully autonomous where it does not.
Document intelligence
Pull structured data out of PDFs, invoices, emails, contracts, and forms. Map it into your existing schema. Replace the hours your operations team currently spends on manual data entry with a pipeline that runs in seconds.
Also included
- Eval harness so you can measure quality and catch regressions on every prompt change
- Prompt management and versioning so you can iterate without re-deploying
- Cost monitoring and per-feature spend dashboards
- Feature flags so you can roll back any AI feature instantly if a model update changes behavior
- Logged outputs and review queues for sensitive or high-stakes flows
- A secure, model-agnostic foundation (de-identify, retrieve, orchestrate, route, audit) built once and reused by every later feature
- HIPAA-safe architecture for regulated data: sensitive fields de-identified before the model, re-hydrated on display, every request audit-logged
- Documentation and a live handoff demo over Zoom so your team can keep building
Who it's for
This sprint is built for
Founders with a working product who need to ship the AI features competitors are racing to launch
Product teams who keep prototyping AI demos that never make it past the proof-of-concept stage
Companies sitting on valuable internal data who want a RAG system, AI search, or copilot over it
Operations teams losing hours to document extraction, data entry, and inbox triage that an agent could handle
SaaS companies adding AI as a tier or upsell, who need it to actually work for paying customers
Sound like you? Email me what you're building and I'll come back with a written scope.
Email me about this sprintWhy this is fast
Agentic AI is the leverage
I shipped 52 AI tools behind a ten-layer orchestration engine inside Fitly AI, all in production. Pattern reuse compresses the build dramatically. What I shipped in months on Fitly I can compress to weeks for you because the architecture, the prompt patterns, and the eval frameworks are already proven on a real product.
What I do vs. what agents do: I design the AI architecture, write the prompts, build the eval harness, and decide what ships when. Agents handle the parallel implementation and test scaffolding. Every prompt and every feature goes through my review and QA before it goes to production.
AI stack used in this sprint
To get started quickly
What I need from you
- Walkthrough of your existing product and the workflows you want AI to touch (a recorded video works great)
- Access to the data the AI will read or write (or representative sample data)
- A decision on which model providers you want to use, or I can recommend
- API keys for Claude, OpenAI, or other providers you plan to use
- Kickoff email thread to align on the first feature to ship
After the sprint
Two paths from here
Hand it off
Your team gets the code, the eval suite, the prompt library, and the runbooks. They can iterate, add features, and swap models without me in the loop. No retainer, no obligation.
Keep me on
Stay on monthly as your AI engineering lead. Because the foundation is built once, each next feature (intelligent documentation, compliance and risk scanning, plain-English analytics, workflow copilots) ships faster than the one before. I keep shipping them, monitor production behavior, swap in newer models as they release, and tune prompts and evals as usage scales.
Pairs well with
Where this goes next
Ready to scope this sprint?
Email me what you are working on and I will come back with a written scope. No meetings required; a Zoom is there if you want one.
Get in Touch