LLM integration
Language models, integrated like production software
Calling a language model API is easy; running one in a business process is not. The difficulty is everything around the call: grounding answers in your data, constraining what the model may say, handling failure, keeping latency tolerable and cost predictable, and being able to see what happened afterwards. That surrounding engineering is what we build.
This engagement suits you if
- You have an app and want AI features inside it
- A prototype works but is too slow, too expensive or too unpredictable for production
- You need privacy and data-handling decisions made deliberately
The work
What the engagement covers
Model selection
Chosen against your accuracy, latency, privacy and cost constraints — not by hype.
Retrieval layer
Your documents and records prepared so the model answers from them.
Guardrails
Constraints on what it can say and do, and defined failure behaviour.
Cost and latency control
Caching, routing and prompt design so it stays affordable at real volume.
Observability
Logging and traces so you can see behaviour and debug it later.
We run language-model features in production products: transcription and summarisation of calls, AI readings on reports, and instant multilingual replies to live customers.
How it runs
The sequence we follow
- 01
Find the job
We look for one repetitive, text-heavy job with a clear right answer. Not a strategy deck — a job.
- 02
Design the agent
What it decides, what data it can read, which tools it can call, where it must stop and ask a person.
- 03
Build and connect
The agent, grounded in your own data, wired into the systems it needs — CRM, WhatsApp, sheets, accounting.
- 04
Deploy with a human in the loop
It goes live handling a slice of the work, with a person reviewing what it does before the loop widens.
- 05
Operate and improve
We watch traces of real behaviour and tighten it. An agent is a system you run, not a project you finish.
FAQ
Common questions
Which models do you use?
It depends on the constraint that matters most for your case — accuracy, latency, cost, or keeping data in a particular jurisdiction. We choose per project and tell you the trade-off you are making.
Will our data be used to train someone's model?
That is a configuration and contract question we settle before anything is connected. If data residency or non-training guarantees matter to you, say so at the start and it shapes the choice.
How do you keep running costs predictable?
By treating cost as a design constraint: caching repeated work, using smaller models where they suffice, and keeping prompts tight. A feature that costs more than the work it saves is a failed feature.
Other engagements
Interested in llm integration?
Tell us what you're trying to solve. First conversation is free and useful either way.