Our LLM integration services connect production language models to the APIs, databases, and tools you're already running — no rip-and-replace, no six-month platform migration before you see any value.
Serving UK and EU clients: GDPR, EU AI Act, data residency, and VPC / on-premise deployment options are covered on our Trust & Safety page.
LLM integration is the work of connecting a language model to your real systems — your product's database, your internal APIs, your auth, your existing UI — so it can act on real data instead of answering in a vacuum. It's the layer that turns "we tried ChatGPT and it was fine" into a feature your product actually ships.
Done right, it's additive: the model sits alongside your current stack, calling into it through the same interfaces your engineers already use, which means your team can maintain and extend it without needing to understand a new platform first.
We've seen inference costs blow up 40x when none of this is in place from day one. Every integration we ship includes it by default.
Per-request limits and prompt design that keep context lean, so cost scales with actual usage, not with how verbose a prompt happened to get.
Responses to common or repeated requests are cached instead of re-generated, cutting both latency and spend on the queries that show up most.
Simple requests go to smaller, cheaper models; the larger model is reserved for requests that actually need its reasoning. See our Trust & Safety framework for how this is monitored in production.
The questions we get asked most about LLM integration — answered straight, no sales pitch.
A single integrated workflow — one feature, connected to your existing data and one or two systems — typically ships in weeks, not months. Timeline scales with how many systems the integration needs to touch and how clean the existing APIs are, which we scope upfront rather than discovering mid-project.
No — that's the premise of LLM integration as opposed to a greenfield AI product. We connect to your existing APIs, databases, and auth rather than asking you to replace them. A rebuild is occasionally the right call, but it's the exception, not the default.
Token budgeting per request, response caching for repeated queries, and routing simpler requests to smaller/cheaper models while reserving the larger model for requests that actually need it. We've seen inference costs blow up 40x at scale when none of this is in place from the start.
Yes — LLM integration is often the connective layer underneath an agent or a RAG system. See our AI agent development and RAG development work for those specific builds.
Book a 30-minute call — we'll tell you honestly what it would take to integrate AI into what you're already running.