Turn AI language models into real features your business actually uses, with LLM integration that ships, not just demos.

LLM integration is the work of connecting a large language model, such as OpenAI's GPT or Anthropic's Claude, into your software, data and workflows so it does useful work inside your business. Rather than staff copying and pasting into a public chatbot, the model is wired directly into your systems: drafting text, searching your documents, summarising long material, pulling structured data out of messy text, or powering an assistant inside the app you already run.

In practice, good LLM integration is far less about the model and more about the engineering around it: choosing the right model for each job, grounding it in your own data so answers are accurate, keeping token costs under control, adding guardrails and evaluation, and monitoring it after launch. That discipline is exactly what Avaib brings, drawing on more than 600 projects delivered since 2004.

It depends on the job. GPT and Claude each have real strengths and quite different pricing, and for some tasks a smaller open-source model you run yourself is cheaper and keeps data fully in-house. Because we have no product to resell, we pick the right model per use case rather than marrying you to one provider.

We also keep the integration model-agnostic behind a gateway, so moving between GPT, Claude or an open-source model later is a configuration change rather than a rebuild. You are never locked into one vendor's pricing or terms.

This is the most important part of a trustworthy integration, and the reason we use retrieval-augmented generation (RAG). Instead of relying on what a model happens to know, we connect it to your own documents and records and have it answer from those, with references back to the source. That keeps answers specific to your business and dramatically reduces the confident, made-up responses that create risk.

For anything high-stakes we go further, adding guardrails on what the model will attempt and a human-review step so a person approves before a risky action is taken.

Token spend can climb quietly if nobody designs for it, which is why we treat cost as an engineering problem from the start. We route each job to the cheapest model that does it well, cache repeated work so you are not paying twice for the same answer, and set hard spend limits and alerts.

That way the value stays tied to real business outcomes rather than a surprise bill, and you get clear visibility of what the integration is costing and why.

Yes, and that is the core of what makes an integration valuable rather than a novelty. We connect the model to your documents, database, CRM, helpdesk or internal tools through their APIs, and ground it in your content with RAG so it works from your real information.

Because we are a software company first, if a ready-made connector does not exist, our engineers build the bridge, so the integration fits the tools you already run instead of forcing you onto something new.

Responsible handling is part of every engagement, not an afterthought. We advise on where your data goes, keep sensitive information in-house where that is required, and can run open-source models in your own environment for the strictest cases. We factor in the privacy, security and audit requirements your sector expects.

For regulated industries like healthcare and finance we go further, designing the integration around the specific data-handling and record-keeping obligations you have to meet.

It depends on scope. A focused feature like document search or a drafting tool is a small, fixed engagement, while integrating LLMs across several workflows and systems is larger. Avaib's model is senior engineering at a cost-effective rate, typically around USD 25 per hour, which is why businesses in Australia, the US, Canada and the Middle East work with us instead of paying big-firm prices. One Canadian client cut their build investment by roughly 75% against local quotes and still received the same high-quality solution, backed by a 30-day warranty.

There are two costs to plan for: the build, and the ongoing token spend to run the model, and we help you keep both predictable. Tell us what you want the model to do, and we'll send a free, no-obligation written estimate with a clear scope and a ballpark price.

An LLM feature is a product to maintain, not a project to finish and forget. We stay on to monitor accuracy and cost, catch drift as your data and the models change, and keep tuning it so it stays dependable.

You get one accountable team for the whole journey, from the first version through to ongoing improvement, with a 30-day post-launch warranty on the work we deliver.