# LLM Integration

> A language model doing a defined job inside your systems — measured, governed and costed.

Canonical page: https://sentrydigitalservices.com/services/llm-integration/
Provider: Sentry Digital Services (https://sentrydigitalservices.com/)
Engagement: Managed service · Delivery options: Project · Support · Managed service
Last updated: 2026-10-15

Access to a model is not a capability. We integrate large language models into the products and workflows you already run — grounded in your own data, constrained to a defined job, evaluated against real examples, and costed per transaction before anything goes live.

## What it does

Integrates large language models into existing products, workflows and data — retrieval over your own content, structured outputs, evaluation harnesses, guardrails, fallback and per-call cost control.

## Where it creates value

The judgement layer of the value stream: it puts reading, classifying, drafting and deciding where a person used to sit, so work that queued for attention now completes on arrival.

## Why it cannot be ignored

Most LLM projects fail in the same two places: the model answers from the wrong source, and nobody measured whether it was right. Both are engineering problems with known answers — and both are far cheaper to solve before launch than after a customer finds the error.

## What you get

- **Grounded in your data** — Retrieval over your own documents, records and systems, so an answer points at something real instead of being invented.
- **Structured output, not prose** — The model returns validated fields your software can act on — the difference between a demo and a working integration.
- **Evaluated before it ships** — A test set of real examples, scored on every change, so a new prompt or a new model cannot quietly make things worse.
- **Costed and capped** — Cost per call measured, work routed to the cheapest model that passes, with limits and fallbacks so spend cannot run away.

## Key concepts

- **Retrieval-augmented generation** — Fetching the relevant passages from your own content first, then asking the model to answer using only those. It is what separates an answer traceable to your documentation from a confident guess.
- **Evaluation set** — A fixed collection of real inputs with known-good answers, scored automatically on every change. Without one, nobody can say whether last week's change helped or hurt — which is how AI projects stall.
- **Guardrails** — Explicit limits on what the model may say, do or call, enforced in code around the model rather than asked for in the prompt. When the model is customer-facing or able to trigger an action, a prompt is a request; a guardrail is a rule.

## Next step

Book a free Discovery session: https://sentrydigitalservices.com/contact/ · value@sentrydigitalservices.com · 01738 506283