AI Integration Services

Which AI feature should you add to your existing app first?

Pick the task where people already spend time on repetitive text work and where a wrong answer is cheap to catch before it matters. Six kinds of feature cover most briefs to add AI to software that is already live:

• Search: find products, tickets or documents by meaning rather than exact words, often backed by retrieval over your own content.
• Summaries: a ticket thread, call transcript or long CRM history reduced to five lines.
• Drafting: first replies, product descriptions or follow-up emails that a person approves.
• Classification: routing tickets, scoring leads or flagging risky orders.
• Extraction: pulling fields from invoices, forms and emails into your ERP, a job for NLP as much as for chat models.
• Assistants: a chat panel that answers from your data and, as an AI agent, takes permitted actions.

Why route AI calls through a model gateway instead of calling OpenAI directly?

Calling one provider's API straight from your app is fine for a prototype. It becomes a problem when prices change, a better model appears, the provider has an outage or a customer asks where their data went. A gateway is a small service between your app and every provider: your code calls one internal endpoint, and the gateway picks the model, applies the prompt version, masks personal data, counts tokens and logs the result.

The practical gain: moving from OpenAI to Claude, Gemini or a self-hosted Llama model, or splitting traffic between them, becomes a configuration change tested against your evaluation set. Without a gateway, every model change means editing and redeploying application code in several places.

How does AI in your CRM or ERP read data without exposing it?

The integration gets its own service account with the narrowest scopes the system allows, read-only unless the feature must write back. Where the platform supports it, queries run as the signed-in user, so a sales rep's assistant never sees records their role cannot open.

Before any text leaves your infrastructure, the gateway masks names, emails, phone numbers and card or ID numbers, and restores them in the answer where needed. Provider accounts are opened in your company's name, and we review each provider's current data-use terms with you before go-live. Where regulation or a customer contract rules out an outside provider, the same gateway points at an open model in your own cloud. We build to the controls GDPR or HIPAA require; certification stays with your own audit.

What does an AI integration cost to run each month, and how do you cap it?

Running cost is separate from the build. Hosted models bill per token, so the monthly bill depends on how many users trigger the feature, how much text each call sends and which model answers. Long CRM histories pasted into every prompt are the usual reason a first invoice surprises a team.

We keep it predictable four ways: send only the fields the task needs, cache repeated answers in Redis, route simple jobs such as tagging to a smaller model and save the larger one for drafting, and set a hard monthly cap per feature with alerts well before it is reached. Before you commit, we estimate the monthly running cost in writing from your real volumes.

What happens when the AI provider is slow, down or wrong?

Treat the model as an outside dependency that will sometimes fail. Every call has a timeout. When a provider times out, returns a rate-limit error or goes down, the gateway retries once and then moves to the next provider on the route. If all of them fail, your app shows the screen it showed before the integration, so work carries on without AI.

Wrong answers need a different guard. Output is checked against a schema before your app uses it: a ticket category must be one of your categories, an extracted amount must be a number. Anything that writes to a customer record or sends a message waits for a person's approval until the evaluation scores show it can run on its own.

How do you evaluate an AI API integration company before you sign?

Ask how they will test the feature on your data, not for a chatbot demo. A credible AI API integration company answers these in writing. If you are still deciding what to build at all, start with AI consulting instead.

• Which examples from your own system go into the evaluation set, and what score counts as ready?
• Can you switch model providers without touching application code?
• Whose name are the provider accounts and API keys in?
• How are personal data, rate limits and monthly cost caps handled?
• What is handed over - code, prompts, evaluation sets, runbooks - and can your team change a prompt without calling them?