AI Integrations
AI assistants become far more useful the moment they can see and act on your real systems. We build the connections that make that possible, with the permissions, limits and logging you would expect from any other piece of software touching company data.
Who it is for
This service is for organisations that already use AI assistants, or plan to, and want them working with company systems rather than copy and paste.
- Teams whose people already use AI assistants and keep pasting data in and out of them by hand.
- Companies with internal tools or databases that have an API but no AI access.
- Software and data teams that want a well-built MCP server for their product or platform.
- Agencies that need AI tools to work across many client accounts and data sources.
Problems it solves
- People export data to spreadsheets just so they can paste it into an AI assistant.
- Each team has connected AI to tools in its own way, with no view of what has access to what.
- Off-the-shelf connectors do not cover your internal systems or your specific fields.
- Security teams block AI tools because nobody can show which data they touch.
- Answers drift because the assistant works from stale exports instead of live data.
What we build
- Business system integrations
- Connections between AI assistants or agents and your CRM, helpdesk, ERP, databases and data warehouse, scoped to the records and actions each use case actually needs.
- Workspace integrations
- Access to email, calendars, documents and drives in Google Workspace or Microsoft 365, using your existing identity provider and sharing rules.
- Custom MCP servers
- Model Context Protocol servers that expose your internal APIs and data as well-described tools, so any compatible assistant can use them safely, with read-only and write scopes kept separate.
- Access, audit and documentation
- Least-privilege credentials, rate limits, request logging and clear documentation, so your IT and security teams can review and own the integration after hand-over.
How an engagement runs
The same five phases as every project, adapted to this service.How we work
Inventory
We list the systems involved, how they authenticate, and which data and actions each use case requires.
Design
We agree tool definitions, permission scopes and where approvals are needed before any write action.
Build
We build and test the connectors against staging or sandbox environments, then against production with read-only access first.
Roll out
We release to a pilot group, monitor usage and errors, and widen access once it behaves as expected.
Hand over or maintain
Your team gets the code, documentation and runbooks, or we keep maintaining the integrations as APIs change.
Good to know
Read before write
Integrations start read-only. Write actions are added one at a time, each with its own scope and, where it matters, a confirmation step.
Not tied to one assistant
Because MCP is an open protocol, the same connector works with several assistants and model providers.
Related use cases
Want your AI tools to see your real data?
Tell us which systems matter most. We will propose the smallest integration that delivers something useful.
Start a conversation