SERVICE 05
AI System & Integration Services
AI system development and AI integration for web products: RAG pipelines, agents, MCP servers, and LLM features wired into your existing stack. One developer, production-ready delivery.
$2,000 to $12,000+
Typical project range
2 to 8 weeks
Typical delivery window
1 developer
You talk to who builds it
00 / OVERVIEW
Why this service exists
I build AI systems and handle AI integration for products where the model is not a demo: it is a feature users depend on, with evals, cost controls, and a path to production.
One developer owns architecture, prompt design, backend pipelines, and the web layer that surfaces the output. No hand-off chain, no "AI team" that never ships.
AI integration projects typically run $2,000 to $12,000+ for a first production feature, in 2 to 8 weeks depending on complexity. Wrapper-tier features land faster; RAG and agent systems take longer.
01 / SCOPE
What AI integration covers
AI integration
Adding LLM features to an existing web app or backend
RAG systems
Retrieval over your docs, catalogs, or records: grounded answers, not hallucinated guesses
Agent workflows
Multi-step AI that calls tools, queries APIs, and completes tasks
Structured extraction
OCR + LLM pipelines that return typed data, not free-form text
MCP servers
Expose your product inside ChatGPT, Claude, and other AI assistants
Eval and monitoring
Test suites, quality thresholds, and cost tracking from day one
02 / LEVELS
AI system maturity levels
Level
Description
Typical timeline
Level
Wrapper
Description
Well-designed prompt + UI around an API model
Typical timeline
1 to 2 weeks
Level
RAG app
Description
Your data retrieved and fed to the model as context
Typical timeline
2 to 4 weeks
Level
Agent system
Description
AI that takes actions across tools and APIs
Typical timeline
4 to 8 weeks
Level
MCP / platform
Description
Your product reachable from external AI clients
Typical timeline
2 to 6 weeks
Not every product needs the same depth. I scope to the rung that matches your validation question. The goal is always the smallest AI system that proves the feature works for real users.
03 / DELIVERABLES
What you get
Feasibility read
Honest assessment before you commit: including "don't build this yet"
AI integration design
Model choice, prompt strategy, retrieval architecture, failure modes
Backend pipeline
FastAPI services with structured outputs, retries, and logging
Cost controls
Rate limits, caching, model routing, per-user budgets where needed
Eval suite
Representative test cases you can rerun after every change
Web UI
React frontend for the AI feature: chat, forms, or inline actions
Handover
Code, docs, and a walkthrough of how to extend the system
04 / APPROACH
Why AI integration is its own discipline
Bolting a chat box onto an API call is easy. Shipping an AI system users trust is not.
Three things make AI products different from normal web development: Probabilistic output (you need a quality threshold and an eval loop, not a pass/fail checkbox). Per-call economics (inference costs money every time the feature runs; pricing and abuse control are MVP concerns). Workflow fit (the model can be accurate and still fail if the output lands in the wrong place in the user's day).
I have written about this stack in React + FastAPI for AI web applications and what is an MCP server.
05 / PROCESS
My process
01
Discovery
What should the AI do, for whom, and what does "good enough" look like?
02
Day-one feasibility test
Run 20 to 30 real examples against API models before writing product code.
03
Written scope
Pipeline design, eval plan, fixed price, timeline.
04
Build, eval, ship
Backend pipeline, frontend, deployment, handover.
If the AI cannot clear your quality bar in a day of API testing, you have saved yourself the build. That honesty is part of the service.
06 / PROOF
Recent AI builds
AI meeting notes SaaS: transcription, summarization, structured outputs
OCR + AI extraction pipeline feeding an editable design canvas: 4 weeks, $2K
Rule-based automation with AI fallback for complex workflow scenarios
Single upload-and-extract flow validating model accuracy before billing
More context in my portfolio.
07 / FIT
Who this is for
GOOD FIT
- You want to add AI to an existing product (custom software development + AI integration)
- You are building an AI-native product and need a production pipeline, not a Lovable demo
- You need RAG over proprietary data with evals and cost controls
- You want an MCP server so your product works inside AI assistants
NOT A FIT
- You need custom model training at MVP stage (almost never: API models first)
- You want "AI everywhere" without a specific workflow to validate
- You have not validated that users want the feature at all (MVP development first)
08 / NEXT STEP
Start here
I reply within 1 to 5 business days: including when a simpler integration or no build is the honest answer.
09 / FAQ
FAQ
01
Do I need to train my own model?
Almost never at this stage. Existing API models (Claude, GPT, Gemini) plus good prompting and RAG cover most production AI features. Fine-tuning and custom training are rare exceptions: I will tell you if you are one.
02
Can you add AI to our existing app?
Yes. AI integration into an existing React, Python, or Node codebase is a common engagement: new endpoints, background jobs, and UI components wired into what you already run.
03
How do you control inference costs?
Rate limits, caching, model routing (cheaper models for simple tasks), per-user budgets, and human-in-the-loop flows that reduce retry loops.
04
What stack do you use for AI systems?
FastAPI + PydanticAI or LangChain on the backend, React on the frontend, PostgreSQL for app data, vector stores (pgvector, Pinecone, etc.) when RAG requires them. Structured outputs are the default: typed, validated responses instead of free-form prose.
05
How is this different from hiring an AI agency?
You work directly with the engineer who designs the pipeline, writes the evals, and ships the integration. No account managers, no slide decks, no junior team learning on your API budget.
Ready to scope
Ready to scope an AI feature? Tell me what you want the AI to do.
Email me with what the AI should do and 2 to 3 real examples, or book a 30-min discovery call for a feasibility read.