Integrate at the seam
Introduce the model where a handoff already exists — a queue, a review step, a form — rather than rebuilding a working process around it. The existing seam already has error handling and an owner.
Services
Custom LLM Integrations
Connecting language models to the systems you already run, with the evaluation and guardrails to keep them honest.
What this covers
Putting a model inside systems that already work, without destabilising them.
Most organisations do not need a new AI product. They need a language model connected to the systems they already depend on — the ticketing queue, the document store, the internal search, the workflow that someone currently performs by reading and re-typing. The value is in the join, and so is the risk.
A model introduced into a working system brings failure modes that system was never designed for. It is non-deterministic, so the same input can produce different output tomorrow. It fails fluently, producing well-formed answers that are wrong in ways a schema check will not catch. And it is easily influenced by text it consumes, which matters enormously the moment that text comes from outside your organisation.
The integration work is therefore mostly boundary work: validating what goes in and what comes out, deciding where a human must remain in the loop, keeping the model’s reach proportionate to the consequences of a mistake, and versioning every prompt, tool definition, and model identifier so that a change in behaviour can be traced to a change someone made.
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A representative image for Custom LLM Integrations — a real system diagram, an architecture whiteboard, or the team at work. Not a stock photo.
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The shape of it
How the pieces fit together.
How we approach it
Four stages, in order.
- 01
Map
Find the seam: where the handoff already happens, who owns it, and what a wrong answer would cost there.
- 02
Connect
Join the model to the real systems behind an interface, with authentication, rate limiting, and failure handling in place.
- 03
Guard
Add input and output validation, scope the tools, and decide explicitly where a human stays in the loop.
- 04
Watch
Log inputs, outputs, and tool calls; evaluate continuously, so silent drift shows up as a signal rather than a complaint.
Principles
What we hold to, and why.
Guardrails at the boundary
Validate what enters the context and what leaves it. Treat retrieved and user-supplied text as data, never as instructions, and check output against a schema before anything downstream acts on it.
Humans in the loop where it counts
Automate the reversible; keep review on anything that spends money, contacts a customer, or deletes something. The right amount of autonomy is a function of what a mistake costs, not of what the model can do.
Version everything the model sees
Prompts, tool definitions, retrieval settings, and the model identifier itself. When behaviour changes, the first question is what changed — and a provider’s silent model update is a change like any other.
Is this you?
Signals that this is the work you need.
- A model needs to reach data that lives behind an existing permission model.
- Model output is written straight into a system of record with nothing checking it.
- External or user-supplied text reaches the model and is treated as instruction.
- Behaviour changed and nobody can tell whether the provider updated the model.
- Staff have started pasting internal data into a consumer chatbot to get work done.
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A short walkthrough for Custom LLM Integrations — an engineer explaining the approach, or a screen recording of the system being discussed.
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Tell us what you're building.
We'll tell you straight whether this is the right thing to spend on.