LLM
integration
Adding a language model to a product that already exists — without a rewrite, a runaway bill or output nobody can parse.
What we build
Four areas where this work is won or lost.
Feature design
Where a model genuinely helps — drafting, summarising, extracting, classifying — and where plain code is better and cheaper.
Structured output
Responses forced into a schema and validated, so downstream code receives data rather than prose.
Cost and latency
Caching, model routing and prompt budgets, because an unmetered model call is a recurring line on the invoice.
Fallbacks and safety
Provider failover, timeouts and content rules, so an outage or a bad response degrades gracefully.
How we work
A short loop with a written result at every step.
Find the use
We map candidate features against value and error tolerance and pick the one that earns its running cost.
Integrate cleanly
A thin internal interface over the provider, so the model can be swapped without touching product code.
Control and measure
Per-feature cost, latency and quality tracked, with a budget alert before the invoice surprises you.
Stack
Chosen for your product, not for our habit.
OpenAI
LangfuseWant AI in the product
without a rewrite?
Tell us what the product does today. We will point to the feature that earns its model bill.
