AI System & Integration Services

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 senior 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.

Short answer: AI integration projects typically run $2,000 to $12,000+ for a first production feature, in 2–8 weeks depending on complexity. Wrapper-tier features land faster; RAG and agent systems take longer. Full scoping guide: AI application MVP.

What AI integration covers

AI system maturity levels

Not every product needs the same depth. I scope to the rung that matches your validation question:

The full maturity ladder and scoping rules are in the AI application MVP guide. The goal is always the smallest AI system that proves the feature works for real users.

What you get

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:

1. Probabilistic output — you need a quality threshold and an eval loop, not a pass/fail checkbox.
2. Per-call economics — inference costs money every time the feature runs; pricing and abuse control are MVP concerns.
3. 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.

My process

1. Discovery — what should the AI do, for whom, and what does “good enough” look like?
2. Day-one feasibility test — run 20–30 real examples against API models before writing product code.
3. Written scope — pipeline design, eval plan, fixed price, timeline.
4. 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.

Proof: recent AI builds

• Uninote: AI meeting notes SaaS — transcription, summarization, structured outputs
• Flaz: OCR + AI extraction pipeline feeding an editable design canvas — 4 weeks, $2K
• OpsGuide: rule-based automation with AI fallback for complex workflow scenarios
• Document extraction tool: single upload-and-extract flow validating model accuracy before billing

More context in my portfolio and the AI application MVP guide.

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)

Start here

1. Email me with what you want the AI to do and 2–3 real examples of input/output.
2. Or **book a call for a free feasibility read.

I reply within 1–5 business days — including when a simpler integration or no build is the honest answer.

Related

• AI application MVP guide: scoping, model choice, cost control
• Custom software development: full web apps with or without AI
• MVP development services: fixed-scope builds when AI is the core bet
• React + FastAPI for AI web apps: stack rationale

FAQ

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.

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.

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. Ballpark planning numbers are in the AI application MVP guide.

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.

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.