// Service //
Your existing systems, now with language model capabilities — drafting, summarising, extracting, classifying, and generating, built into the tools your team already uses.
// What this is //
LLM integration is not about replacing your existing systems. It is about adding a layer of AI capability to the workflows and tools your team already depends on.
Your CRM can now draft follow-up emails based on deal history. Your support system can classify and summarise tickets before a human reads them. Your internal dashboard can answer natural language questions about your data. None of this requires rebuilding your stack from scratch.
This is distinct from building a standalone AI product. LLM integration means embedding AI behaviour into workflows that already exist — extending what your current tools do rather than replacing them.
// The integration layer //
The integration layer sits between your existing system and the language model. It receives inputs from your system, constructs a prompt, calls the model, handles the response, and returns formatted output to your application. In production, that layer requires considerably more than a direct API call.
// Common targets //
We work across a range of integration targets. The four most common are below. If your use case is different, that is not a problem — the integration principles are consistent across systems.
// Production reality //
Most LLM demonstrations work on clean, controlled inputs. Production environments do not provide clean, controlled inputs. The gap between a demo that impresses and an integration that runs reliably is significant, and it is where most LLM projects break down. We have written more on why LLM integrations often fail between demo and production.
The same task needs to work across varied real-world inputs, not just the test cases you thought of during development. Prompt engineering for production means designing for variance, edge cases, malformed inputs, and adversarial behaviour — not just the happy path. We treat prompts as versioned, documented artefacts, not strings embedded in code.
Token budgets are real. What you include in context determines both cost and quality. Including too much inflates cost and degrades output. Including too little loses relevant information. Managing context — deciding what goes in, in what order, and at what truncation threshold — is one of the core engineering decisions in any LLM integration. See also our RAG systems service if you need the model to access proprietary knowledge bases.
LLM calls are slow relative to standard API calls. For user-facing features, perceived latency matters. We implement streaming responses where appropriate, cache results for frequent queries, and use async processing patterns where real-time output is not required. We also set honest latency expectations with stakeholders before launch.
Without rate limits and cost monitoring, production LLM integrations generate surprise invoices. We build cost guardrails in by default: per-user rate limiting, per-request token caps, total spend alerting, and logging of token consumption by feature. Cost modelling at expected production volume is part of our process before any integration goes live.
Model providers experience outages. Responses occasionally fail validation. Latency occasionally spikes. Your integration needs defined behaviour for each of these cases — whether that is a cached response, a default message, a human escalation trigger, or a silent retry. Silent failure in a production workflow is not acceptable.
Model providers update, retrain, and deprecate models on their own schedule. Prompts that work with one model version do not always produce equivalent output after an update. We build version pinning and change-testing into our integrations so you are not surprised when a provider deprecates a model endpoint. We have written about what production-ready LLM integration requires in more detail.
// Our process //
Typical build time is three to eight weeks depending on the complexity of the target system and the number of integration points. The stages below are consistent across engagements.
We establish which system we are integrating with, which specific workflow the AI will participate in, what the model should produce, and what it should not do. Clear scope prevents scope creep and sets a basis for quality measurement.
We design the prompt structure before writing integration code. This includes system instructions, input templates, context ordering, output format specification, and edge case handling. Prompts are documented and versioned from the start.
We build the integration layer — input processing, prompt construction, model API calls, response parsing, error handling, fallback logic, caching, and logging. We connect to your existing system via the mechanism that suits your architecture: webhook, API endpoint, background worker, or direct database integration.
Before launch, we test at expected production volume and model the actual cost at that volume. There should be no financial surprises after go-live. We also validate output quality across a representative sample of real inputs, not just synthetic test cases.
We deploy to your environment and configure monitoring: latency dashboards, token spend alerts, error rate tracking, and output quality sampling. You have visibility into the integration from day one.
Your team receives full documentation, the prompt library, runbooks for common operational scenarios, and a handoff session. We do not leave you dependent on us to keep the lights on.
// Model selection //
We work with Anthropic (Claude), OpenAI (GPT-4o, o3), Google (Gemini), and open-source or self-hosted models including locally deployed options for data-sensitive environments.
Model selection is driven by the actual requirements of your use case: the nature of the task, your latency requirements, your cost targets, and your data privacy constraints. We recommend the right model for what you are building, not the one with the best marketing at the time of the engagement.
For some integrations, the right answer is a small, fast model running locally. For others, it is a frontier model via a managed API. We will tell you which, and why, before any build begins.
// What you receive //
// Related services //
LLM integration sits alongside other services we provide. Depending on your requirements, one of the following may be relevant alongside or instead of a direct integration.
// Get started //
We start every engagement with a scoping conversation — no commitment required. Tell us which system you want to integrate with and what you need the AI to do. We will tell you whether the approach is feasible, what it is likely to cost, and how long it will take.