A chat box is the easy part. We integrate GPT and Claude so they call your APIs, act on your systems, stream instantly, stay within budget, and fail gracefully — the engineering that turns a model into a feature people rely on.
Frontier model, wired into your product
Function calling · your APIs
behind one provider-agnostic layer
manual work an integration can erase
+ routing to control token spend
so a provider outage isn't yours
What an LLM integration really takes
The gap between a weekend prototype and a production integration is all of this — and it's exactly where we live.
The model triggers your real APIs under your permission rules — looking things up, taking actions, returning structured results.
Token-streaming UX, multi-step chains and agentic flows that feel instant and stay debuggable.
Caching, model routing and token budgets that keep spend predictable and well under what naïve calls would cost.
Input/output filtering, PII redaction, provider fallbacks and validation so the feature is safe and resilient.
How we integrate
A clear path from 'we want AI in the product' to a guardrailed, cost-controlled feature in users' hands.
We find where an LLM removes real work in your product, and what tools it needs to call to do it.
Provider-agnostic integration with function calling into your APIs, streaming UX, and structured outputs.
Validation, PII handling, caching and routing — so it's safe to ship and cheap to run at scale.
Launch with monitoring on quality, latency and spend, plus fallbacks for when a provider has a bad day.
An integration, in production
A brand agency was losing its best hours to status meetings and manual prep. We integrated LLM summarisation into their existing stack — Slack, Notion, Asana — so updates wrote themselves and the team sold the hours it got back.
Field & Form
Brand & design agency · ~40 people · EU
Reclaimed per week
Billable hours
Status meetings left
Project visibility
“We thought meetings were how we stayed aligned. Turns out they were how we lost money. We're more aligned now than we were with the meetings — and we're billing 31% more of our week.”
Straight answers
No — we integrate OpenAI GPT, Anthropic Claude, and open-source models behind a provider-agnostic layer. That means we pick the best model per task and you're never stranded if pricing, limits or capabilities change.
Cost control is part of the build: prompt and context trimming, response caching, model routing (cheap model first, escalate only when needed), token budgets, and dashboards that tie spend to usage. You get the capability without an unbounded invoice.
Function calling lets the model trigger your real APIs — look up an order, create a ticket, send an email — instead of just chatting. It's what turns an LLM from a text box into something that actually does work inside your product, safely and with your permission rules.
Retries, timeouts, fallbacks to a secondary provider, output validation, and graceful degradation so a model hiccup never takes down your feature. We treat the LLM like any other unreliable dependency and engineer around it.
Yes — most integrations slot into your current stack via APIs and SDKs. We read from the tools you already use and add the AI layer on top, rather than asking you to migrate.
Tell us what you want it to do. We'll come back within 24 hours with an integration plan, a cost model, and a timeline.
2000+ vetted engineers · 3 global hubs · 98% client retention
for project discussion
Once you fill out this form, our sales representatives will contact you within 24 hours.
We guarantee to get back to you within a business day.