AI Integration for Web Products
Model APIs wired into real products — where they help, and honestly where they don't.
Most products do not need their own model — they need a capable hosted model wired into their actual workflow: drafting content, answering support questions from your own data, structuring messy input, or automating a step your team does by hand today.
The engineering is in the surrounding system: streaming responses that feel instant, retrieval over your own documents so answers are grounded in your data, guardrails and cost limits so a viral day does not become a four-figure API bill, and interfaces that treat the model as fallible — showing uncertainty instead of pretending confidence.
I integrate providers like Anthropic's Claude and OpenAI into Node.js and Next.js products, with the boring-but-critical pieces — caching, rate limiting, prompt versioning, usage logging — included from the start.
What you get
- LLM integration for content generation, summarization and extraction
- Retrieval-augmented answers over your own documentation or data
- Streaming chat interfaces in React/Next.js
- Cost controls: token budgets, caching, per-user rate limits
- Structured outputs with schema validation for automated workflows
- Fallback and degradation behavior when the provider has an outage
Tools I work with
How we work
Find the real task
We pick one workflow where AI measurably helps — and define what 'helps' means in numbers before any prompt is written.
Ground it
Answers are anchored to your content with retrieval, so the system cites your data instead of improvising.
Harden it
Validation on every structured output, budgets on every request, and a defined behavior for 'the model cannot answer this'.
Measure
Usage, latency and cost dashboards from day one, so the feature's value is a fact rather than a feeling.
Common questions
Which AI provider should we use?
It depends on the task — drafting quality, cost, latency and context length all trade off differently. I am provider-agnostic: Claude for much of the current generation work, OpenAI where it fits better, and the architecture keeps swapping providers cheap.
Can AI answer questions from our own documents?
Yes — that is retrieval-augmented generation: your content is chunked, embedded and retrieved per question, so answers are grounded in your material with references. Accuracy depends heavily on document quality, which we assess honestly first.
How do you keep API costs under control?
Caching repeated queries, capping response length, cheap models for easy tasks and strong models only where they earn it, per-user quotas, and a live cost view so nobody gets surprised by the invoice.