What this covers
A server-side setup for calling AI models, with input checks, usage limits, error logging and a planned fallback.
Tools
Model APIs, JSON Schema, Streaming, Usage budgets. The exact stack depends on what you already use.
Where it helps
- Pulling structured data into a product form
- Streaming answers into an existing screen
- Comparing model settings for one task
We agree what “done” means before work starts.
What you get
- A server-side connection to the AI provider
- Input checks, usage limits, error logging and a fallback plan
- Source code, setup notes and an agreed definition of done
The final list depends on the agreed scope. The estimate and schedule follow from it.
Data and security
Provider keys stay on the server, output is checked before use, rate limits are enforced, and private prompts stay out of normal logs.
How I work on it
Agree what goes in and comes out, test providers on real examples, integrate, simulate limits and outages, and document the running cost.
What helps me get started
- A short description of the process and who uses it.
- Sample data, any API documents, and what access is available.
- How much it will be used, who reviews the work, and your release priorities.
A short outline is enough for a first call. Please send sample data, not real records or passwords.
Related services
Read the planning guideQuestions
Should we use several AI providers?
Usually not. Each provider behaves differently and needs its own testing. Add a backup only if you really need the extra reliability.
How do you price the work?
I look at the workflow, the systems involved, the access needed and what “done” means, then send an estimate. If there are big unknowns, we start with a short discovery or a prototype.