AI Integration Developer (Claude, GPT & Gemini)
Real AI features built into your product — Claude, GPT, or Gemini integrated with validated inputs, structured outputs, and a plan for what happens when the model gets it wrong.
AI Integration Developer (Claude, GPT & Gemini)
- req/s, zero deadlocks
- 15Kreq/s, zero deadlocks
- double-charges in production
- 0double-charges in production
- production systems shipped
- 10+production systems shipped
- years building for clients
- 7+years building for clients
Signs you need this now.
Bolting an AI API call into an existing workflow is easy; making it reliable in production is a different problem. Unvalidated inputs, unstructured outputs, and no fallback plan turn an impressive demo into a support burden the moment real users hit it with something unexpected.
An AI feature works in the demo but breaks on real input
Edge cases the demo never hit, malformed input, unexpected user phrasing, missing context, cause the feature to fail or hallucinate in production. Nobody planned for what happens when the model gets it wrong.
No clear sense of what the AI feature actually costs to run
Token usage was never modeled against real traffic, and the bill is a surprise. Cost control has to be designed in, not discovered after the fact.
The AI feature is a black box nobody on the team can debug
When it produces a bad output, there is no logging, no way to see what prompt actually ran, and no way to reproduce the failure. That makes every bug a fresh investigation instead of a quick fix.
What you get.
Structured input validation before the model ever sees it
Malformed or unexpected input caught and handled before it reaches the API, not after it produces a bad result.
Structured output parsing and validation
Responses validated against an expected schema, with a defined fallback when the model does not comply.
The right model for the task, not the biggest one by default
Claude, GPT, or Gemini chosen (and tiered, where it makes sense) based on the actual task, cost, and latency requirements.
Cost modeling against your real traffic
Token usage estimated against realistic volume before launch, so the bill is not a surprise.
Logging and observability on every AI call
What was sent and what came back is traceable, so a bad output is a quick investigation, not a mystery.
A defined fallback for when the model fails
A real plan, not a generic error message, for what the user experiences when the AI feature cannot complete the request.
Four steps, no mystery.
Quick scoping call
A short call (or async over WhatsApp) to understand what you're working with and what "done" actually looks like for you.
Fixed scope, no surprises
A clear written plan of what's included and how long it takes, before any work starts.
The actual work
Progress you can see, not a black box. You get updates as milestones land, not just a status report at the end.
Handover
Everything documented and handed over cleanly, with a walkthrough so your team isn't stuck waiting on me for routine changes.
You might also need.
Frequently asked.
It depends on the task, cost sensitivity, and latency requirements — we recommend after understanding your specific use case rather than defaulting to whichever model is most talked about.
Tell me what you're dealing with.
Send a message and get a real reply within 24 hours, not an automated sequence.
Or WhatsApp directly, same link as above