LLM IntegrationServices
Add summarising, extraction, drafting or classification to the software you already run, built as an engineered feature with tests, limits and logs.
LLM Integration Services: what the work involves
A quick demo that calls a language model is easy. A feature that runs inside a live product is not. Outputs arrive in unpredictable shapes, providers time out, a long input blows the budget, a customer's private text ends up in a log, and nobody notices that quality changed after a model update. Teams then freeze the project because it feels unreliable.
We treat the model as one unreliable dependency among others. Each call goes through a thin service that owns the prompt, validates the output against a schema, retries within limits, and falls back to a simpler path when needed. Structured output formats keep the response machine-readable. We add tracing for every request, a cost meter per feature, and an evaluation set that runs before each prompt or model change. Provider choice stays swappable between OpenAI, Anthropic, Google and open models behind one interface, so pricing changes or outages do not strand you.
Core features
Schema-checked outputs
Responses are forced into a defined structure and validated, so downstream code never has to guess what came back.
Provider abstraction
One internal interface fronts several model vendors, making it practical to switch or route by task.
Prompt versioning
Prompts live in version control with change notes, so you can see what changed when behaviour shifted.
Evaluation harness
A fixed set of real examples is scored before each release, catching regressions that eyeballing would miss.
Tracing and cost metering
Every call records latency, tokens and outcome, giving you cost per feature and a trail for debugging.
Graceful fallbacks
On timeout, refusal or invalid output, the feature degrades to a simpler behaviour instead of showing an error.
What we get right before launch
Customer data sent to third parties
Text leaving your system needs a policy. We redact personal fields where possible, choose provider terms that exclude training, and keep region requirements in mind.
Unbounded cost
A loop or a very long input can produce a surprising bill. Input limits, rate limits, caching and per-tenant budgets are part of the design from the start.
Prompt injection
Text from users or documents can try to override your instructions. We separate trusted and untrusted content, restrict tool permissions, and never let model output run unchecked actions.
Tools and technology
- OpenAI
- Anthropic Claude
- Google Gemini
- Llama
- Mistral
- LangChain
- Python
- Node.js
- FastAPI
Common questions, answered
Can you integrate with a legacy system?
Usually yes, through its database, API, file exports or a small adapter service. We keep the model layer separate so the older system needs minimal change, and we assess the connection during scoping.
Which model should we use?
It depends on the task, language and sensitivity of data. We compare a few candidates on your own examples, then pick the cheapest one that meets the quality bar, keeping the choice reversible.
Can the model run on our own servers?
For stricter privacy needs, open models such as Llama or Mistral can be self-hosted. They need suitable hardware and often trail the largest hosted models on hard tasks, so we test before committing.
How do you test something non-deterministic?
We build an evaluation set from real examples, define what an acceptable output looks like, and score every change against it. Rare variation is expected, so we track patterns across many runs.
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Ready to start your LLM Integration Services project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.