ChatGPT, Claude and Gemini inside your software: LLM integration
A language model is useful where your staff already work: in the ERP, the CRM, the ticket system or your own web application. Sortia Labs connects GPT from OpenAI, Claude from Anthropic, Gemini from Google or open models to your software through their APIs, with output checks, logging and clear cost control.
- LLM integration
- ChatGPT API
- Claude API
- Gemini API
- Azure OpenAI
- AI in existing software
What a clean LLM integration includes
Connected to your systems
The model gets the data and functions it needs for its task through REST APIs, database access or webhooks. For your team, the AI shows up as an extra function in the interface they already know.
Switchable provider
A thin layer separates your application from the model provider. That way you can move from GPT to Claude, Gemini or a local model when another one fits better or becomes cheaper, without rewriting the application.
Access through your cloud
If your company already uses AWS or Azure, we connect models through AWS Bedrock or Azure OpenAI. Billing, region and access rights then run through your existing cloud account.
Structured answers
Instead of free text the model returns defined fields such as category, amount or draft reply, which your program processes directly. Inputs and outputs are validated before anything is written to your database.
Costs under control
Every call costs money. We measure usage per function, use smaller models for simple jobs and large ones where they make a difference. Daily or monthly limits keep spending predictable.
How we connect a language model
- 1
Task and yardstick
Which task should the model solve, with which data, and how do we recognise a good answer? We write these criteria down before a model is chosen.
- 2
Compare models
We test several models on your real examples and compare quality, speed and cost. You decide on measured results rather than marketing claims.
- 3
Build and approval
Connection to your application, output checks, an approval workflow and training for your team. The AI makes suggestions, and your staff decide what is accepted.
- 4
Operation
Monitoring, model updates and cost reports. When a provider retires a model or a better one appears, we switch and check the result against the same examples as at the start.
Software we build and run ourselves.
Questions about LLM integration
Is a ChatGPT integration the same as the ChatGPT app?
No. We use the OpenAI API, in other words the models behind ChatGPT, and call them from your software. Your staff do not need their own ChatGPT accounts, and the provider's API terms apply, which differ from those of the consumer app.
What happens to our data at the model provider?
That depends on provider and contract. We check the current terms before choosing and agree a suitable setup with you: enterprise access through Azure OpenAI or AWS Bedrock, a data centre in the EU, or a local model when data must not leave the building.
Which model fits: GPT, Claude or Gemini?
It depends on the task. The models differ on long documents, structured output, languages, speed and price, and the picture changes with every new version. That is why we test with your examples and keep the application open for a later switch.
What running costs should we expect?
Costs depend on the volume of text processed and on the chosen model. In the prototype we measure usage on your real cases, so you have a monthly projection before rollout. Smaller models for simple subtasks can lower costs noticeably.
Read on.
Which part of your software should get AI?
Tell us the application and the task. We suggest a suitable model and show in a prototype what it does with your data.