AI Development

AI integration services

You don’t need a new AI product. Usually the most useful thing AI can do is sit inside the tools your team already opens every morning.

Discuss Your Project

Adoption is the reason integration works so well. A standalone AI tool means another tab, another login and another habit for people to form. A “summarise this case” button inside your existing helpdesk gets used on the first day. It also has the context, because it sits right next to the customer’s record.

The technical work is mostly about safely connecting things. The model should only see data that the current user is allowed to see, so access checks happen in your application before anything reaches the model. Personal data should be minimised or masked where possible. Calls need timeouts and fallbacks, because your core product shouldn’t break when an AI provider has a bad afternoon.

Connecting through MCP and tool calling

Models can call functions in your software through tool calling, and the Model Context Protocol (MCP) is an open standard for exposing tools and data to AI applications in a consistent way. It’s useful for staff assistants, but every tool a model can reach is also a risk, so we start with read-only access and require human approval for anything that changes data. If you don’t have an existing system to build on, AI-powered web applications may be a better starting point.

What integration needs to handle

Permission boundaries

A model must never reveal data that the person asking couldn’t otherwise see.

Provider outages and limits

Your application needs to carry on working, with fallbacks and rate limit handling.

Cost creep

Without limits and caching, usage bills can grow faster than the value delivered.

Integration work

Use case review

Finding the few places where AI will save the most time in your existing workflows.

AI gateway

A central service for provider access, logging, rate limits, caching and switching models.

CRM and helpdesk features

Summaries, suggested replies and classification inside the tools you use.

Tool calling and MCP

Controlled access for models to your systems, with approval steps where needed.

Privacy controls

Masking of personal data, audit logs and appropriate provider settings.

Where it fits

  • Case summaries and suggested replies in helpdesks
  • CRM notes generated from calls
  • Natural language search over your own records

Technology

  • Claude
  • OpenAI
  • Azure OpenAI
  • AWS Bedrock
  • Model Context Protocol
  • Salesforce API
  • HubSpot API
  • Zendesk API
  • Laravel

Sectors we think about here

AI Integration questions

Can you add AI to our existing software?
Usually, yes, if there’s an API or we have access to the code. Older systems may need an integration layer first.
Is it safe to send our data to an AI provider?
That depends on the data, the provider and the contract. Enterprise and API plans offer stronger commitments, and some can run inside your own cloud. We’d look at your specific case and at what can be masked.
What is MCP?
The Model Context Protocol is an open standard for connecting AI applications to tools and data sources, so that integrations don’t need to be rewritten for every model or client.

Related services

Interested in AI Integration Services?

Outline what you need, and we will reply by email with questions or next steps.

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