Short answer: you integrate ChatGPT, Claude or Gemini by calling the provider's API from your own software, so the model can read the context it needs (a customer record, a document, an order) and return an answer or take an action inside your CRM, ERP, website, app or WhatsApp. The four common patterns are assistants grounded in your documents (RAG), AI inside existing screens, tool-using agents and MCP servers. The engineering work is mostly in data access, permissions, testing and cost control, not in the model itself.
Why companies integrate instead of just using ChatGPT
Copying data into a chat window does not scale: it is slow, nothing is logged, and staff paste information that should not leave the company. An integration puts the AI where the work already happens, gives it only the data each user is allowed to see, and records what it did.
The four integration patterns
- Assistant grounded in your documents (RAG): policies, manuals, contracts and past tickets are indexed, and the model answers from them with sources. Best for support, HR and internal knowledge. See enterprise RAG.
- AI inside the screens you already use: a "summarise this account", "draft a reply" or "extract invoice fields" button inside your CRM, ERP or helpdesk. Small to build and quick to show value.
- Tool-using agents: the model can call your functions (look up an order, create a task, book a meeting), with human approval for anything that changes data. See agentic AI systems.
- MCP servers: one standard connection that lets Claude, ChatGPT and your own agents use your systems with your permissions. See what an MCP server is.
Where businesses use it first
- Sales: lead research, call summaries and follow-up drafts inside the CRM.
- Support: replies drafted from real order data and your knowledge base, on the website or WhatsApp.
- Operations and finance: reading invoices, purchase orders and contracts into the ERP. See document AI.
- HR: a policy assistant that answers from the handbook, with sources.
- Management: plain-language questions over reports and dashboards.
Data privacy and security
- Send the minimum: strip fields the model does not need, and mask personal data where you can.
- Per-user access: the AI sees only what the signed-in user may see.
- Provider terms: major providers say business API data is not used for training by default; confirm the current terms and retention settings for your industry.
- Private models: where data must not leave your servers, an open-source model can run on your own infrastructure. See LLM development.
- Logs and review: record prompts, answers and actions, and review a sample every week.
What it costs to run
The main running cost is model usage, billed per token. It stays predictable when you cache repeated answers, use smaller models for simple steps, keep prompts short and set limits per user. Hosting is usually a modest addition to your existing cloud.
Mistakes to avoid
- Building on one provider in a way that makes switching models expensive.
- Launching without a test set of real questions and expected answers.
- Letting the AI change records without approval at the start.
- Measuring "it feels useful" instead of time saved or tickets closed.
A practical launch plan
- Pick one workflow with a clear metric, such as first-reply time or invoices processed per day.
- Collect 50 to 100 real examples and the answers you expect.
- Build the smallest integration that serves that workflow, behind a feature flag.
- Test against the examples, fix the gaps, then roll out to a few users.
- Measure for two to four weeks, then expand to the next workflow.
A focused integration typically takes 2–4 weeks; one that spans several systems with agents and approvals takes 4–6 weeks.
Work with Nexariza AI
We connect ChatGPT, Claude, Gemini and open-source models to CRMs, ERPs, websites, mobile apps and WhatsApp, with per-user permissions, evaluation and cost controls, and you own the code. Read about AI integration, or get a free scope within 24 hours.
Frequently asked questions
Can ChatGPT be integrated with our own software?
Yes. Through the OpenAI API (and the equivalent APIs from Anthropic for Claude and Google for Gemini) your own software can send text, documents or images to the model and use the answer inside your CRM, ERP, website, mobile app or WhatsApp. Your users never need a ChatGPT account.
Is our data safe if we use the ChatGPT or Claude API?
The major providers state that data sent through their business APIs is not used to train their models by default, and they offer data-retention controls. You still decide what is sent: remove what the model does not need, keep access per user, and log every call. Check each provider's current terms for your industry.
What is the difference between ChatGPT and the OpenAI API?
ChatGPT is the chat app people use directly. The OpenAI API is the same family of models offered as a service your software can call, billed by usage. Integration means using the API, or a protocol such as MCP, so the AI works inside your own systems and workflows.
How much does it cost to run a ChatGPT integration?
Running costs are mostly model usage, billed per token by the provider, plus modest hosting. Costs stay predictable with caching, smaller models for simple steps and limits per user. The build itself is usually a fixed-scope project of two to six weeks.
Should we use ChatGPT, Claude or Gemini?
It depends on the task, language, cost and where your data must stay. A well-designed integration keeps the model behind one interface, so you can test several, pick the best for each job, and switch later without rebuilding.