LLM integration

LLM Integration for Your Existing Systems

The gap between a model demo and something your team will actually rely on is retrieval, evaluation, and cost control. That gap is the work.

Retrieval over your data

Your documents, your catalogue, your records, indexed and retrieved so answers are grounded in your material instead of the model’s general knowledge.

Your language, not generic English

Systems that work in the terminology, the dialect, and the language your customers and staff actually use.

Evaluation before rollout

A test set drawn from your real cases, so a change to a prompt or a model can be measured instead of guessed at.

Cost control

Metering, caching, and per-user limits designed in from the start, so cost scales predictably with usage.

In practice

Things we have actually built.

  • An assistant that handles Lebanese Arabic by voice and text, with per-customer usage limits tied to loyalty tier.
  • A retrieval loop that pulls a specialist’s own past corrections as few-shot examples, so the system converges on their judgement.
  • Vision extraction from photographs into structured, searchable records.

Questions

Common questions.

Will our data be used to train someone else’s model?

Not with the configurations we deploy. We use providers and settings where your data is not retained for training, and we tell you exactly where each piece of data goes.

Can it work in Arabic?

Yes, including dialect rather than only Modern Standard Arabic, and including right-to-left interfaces. We have shipped this.

How do you keep the running cost predictable?

Metering per user, caching repeated work, choosing a smaller model where a large one adds nothing, and setting hard limits. Cost is a design constraint from day one, not something discovered in the first invoice.

Try it free for
three months.

We build one working AI integration into the business you already run, at our cost. You use it free for up to three months. No contract, cancel anytime, and you only pay if you keep it.