ChatGPT integration for business: AI inside your software
ChatGPT integration for business is not about giving everyone an account; it means building AI directly into the software you already use: your website, your business application, your customer portal. From Bastia, we connect language models to your applications through APIs, in a tested and controlled way.
Integrating AI into your software: what it involves
ChatGPT is OpenAI's consumer app. Behind it, as behind other providers' assistants, sits an LLM: a large language model trained on vast amounts of text that can understand a request and write a response.
These models are available through an API, a technical entry point that lets one piece of software send a request to the model and receive its reply, with no chat window involved. That is what makes LLM integration possible: a button that summarises a file, a field that fills itself in, requests sorted automatically, a search box that understands questions.
Using the API rather than the consumer app changes a great deal: you choose the model, you control what it receives and what it returns, and you keep costs and data-processing terms in check.
AI integration examples by sector
- 01
Retail
Product descriptions written from technical specifications, catalogue translation, a site search that understands how customers actually phrase things.
- 02
Tourism and hospitality
Replies to guest reviews drafted for approval, translated descriptions, summaries of the enquiries in your booking system.
- 03
Trades
A clear description of works written from quick notes, right inside your quoting software or job app.
- 04
Professional practices
File summaries, standard letters, incoming documents classified, all within the practice's management system.
- 05
Local authorities
Administrative text rewritten in plain language, residents' requests sorted, drafting help for officers.
- 06
Startups and software publishers
An AI feature added to your product, with an architecture that lets you switch model provider without rewriting everything.
Putting an LLM API to work in a reliable application
Plugging in an LLM API is easy; getting behaviour that is reliable, predictable and affordable is much harder. We treat AI like any other software component: precise instructions, structured outputs the program can check, automated tests and monitoring after go-live.
Hameau Vivant, one of our projects, is a good illustration. In this persistent social simulation, an LLM continuously drives the villagers' minds and decisions. Keeping such a system running over time takes exactly what a business needs: tightly framed outputs, memory held in an ordinary database, a controlled number of model calls and consistent behaviour from one day to the next.
Finally, we design every integration so that it does not depend on a single provider: if another model becomes better suited, cheaper or a better fit for your constraints, it should be able to take over.
Why integrate AI instead of using ChatGPT on the side
- 01
AI where the work happens
No more copying and pasting between a chat window and your software: the feature is there in the right place, at the right moment.
- 02
Consistent results
Instructions are set once; everyone gets results in the same format and to the same standard.
- 03
Control over data
You decide what is sent to the model, and the provider is chosen to meet your confidentiality requirements.
- 04
Clear costs
Usage is measured and can be capped, instead of individual subscriptions that are hard to keep track of.
The stages of a ChatGPT integration project
- 01
Understand
We review your software, the tasks to improve, the data involved, your budget and timeline, whether you are in Bastia, elsewhere in Corsica or further afield.
- 02
Design
We choose the model and provider, define what the feature receives and returns and how it fits into the interface. Mock-ups are approved by you before development.
- 03
Build
We develop the integration and test it against a representative set of examples, edge cases included. You follow progress and try the feature in real conditions.
- 04
Launch and support
Go-live, hosting, maintenance, monitoring of quality and costs. When models change, we check that your feature still behaves as intended.
Data, security and dependence on a provider
- 01
Data sent
Every call sends text to the provider. We keep personal data to the strict minimum and check where and on what terms it is processed, in line with GDPR.
- 02
Hijacking attempts
Text typed by a user can contain hidden instructions designed to subvert the model. The AI is therefore never given more permissions than it needs.
- 03
Model changes
Providers update or retire their models. Automated tests let us check that a change does not degrade your feature.
- 04
Transparency
The EU AI Act sets transparency obligations for certain uses, such as telling users they are dealing with an AI. We build these in from the design stage.
What determines the cost of an LLM integration
- 01
Your existing software
Is the code accessible, documented, designed to accept connections? Recent, open software makes integration simpler.
- 02
Feature complexity
Rewording text takes less work than a feature that combines several data sources and triggers actions.
- 03
Volume and model
Usage is billed by the amount of text processed, and the most powerful models cost more per use.
- 04
Confidentiality and follow-up
Data-processing requirements, possible use of a local AI, maintenance and long-term monitoring.
Technologies and methods
- LLM APIs
- OpenAI
- Open models
- Structured outputs
- Automated testing
- TypeScript
- Node.js
Frequently asked questions
Can ChatGPT be integrated into our existing software?
Usually, yes, if we can work on its code or it offers connection points. We look at this in our first conversation. For closed software, a companion tool connected to your data is sometimes an option.
Do we have to use OpenAI?
No. Several providers offer high-quality language models, including European ones, and some open models can run on your own servers. We choose according to your quality, cost and confidentiality needs.
Will our data be used to train the models?
Many business offerings accessed through an API commit not to use your data for training, but terms vary between providers and change over time. We check them for you before making a choice.
How much does day-to-day API usage cost?
It is billed by usage, according to the amount of text processed and the model chosen. We estimate this cost from your real volumes before development, then set up monitoring and spending caps.
Can we use AI without sending our data outside?
Yes, with a local AI: an open model installed on a server on your premises or with a host you control. It is a different trade-off between quality, cost and confidentiality, and we will set it out for you plainly.