AI where your work already happens. Not in another tab.
AI only creates value when it can reach the right data and act inside the right systems. We integrate AI capabilities with your existing enterprise applications, data platforms and identity, so assistants and agents work with real context, respect existing controls and fit into the tools your people already use.
You're in the right place if…
- Enterprises with core systems such as ERP, CRM or custom line-of-business applications that AI needs to work with
- Product companies adding AI to an established platform without destabilising it
- IT teams asked to connect several AI pilots to production data safely
What gets in the way.
Data locked in silos
The context AI needs is spread across applications, databases and file stores with different access models.
Legacy interfaces
Older systems may lack modern APIs, which makes safe, reliable integration harder.
Identity and access
AI features must honour the same authentication, authorisation and audit rules as the rest of the estate.
Fragile point-to-point builds
Each pilot wiring itself to systems directly creates a tangle that is costly to secure and maintain.
Enterprise AI Integration, end to end.
Integration architecture
A shared approach for how AI services reach data and systems, instead of one-off connections per pilot.
APIs and connectors
Building or wrapping APIs around enterprise applications so AI can read and act through controlled interfaces.
Data pipelines for AI
Preparing and syncing enterprise data for retrieval, search and analytics use.
Identity and access integration
Single sign-on, role-based access and audit logging carried through to AI features.
Embedding AI in existing applications
Adding assistants, summaries and automation directly into the applications your teams use every day.
A delivery approach you can plan around.
Map the landscape
Inventory the systems, data and access controls involved, and the constraints around each.
Design the integration layer
Agree interfaces, data flows and security boundaries, reusing what already exists where possible.
Build and harden
Implement connectors and pipelines with error handling, rate limits, logging and tests.
Roll out and support
Release in stages, monitor usage and reliability, and extend the layer to the next use case.
Ways to work with us.
Integration assessment
A review of your systems and data to plan how AI can connect to them safely.
Integration project
A scoped build of the connectors, pipelines and controls for one or more AI use cases.
Technologies we work with
- REST and GraphQL APIs
- Event streaming and message queues
- Model Context Protocol (MCP)
- Single sign-on and role-based access control
- AWS and Azure integration services
Enterprise AI Integration: common questions.
Can you integrate AI with legacy systems?
Usually, yes. Where a system has no usable API we can build a controlled interface around it, or work from a synchronised copy of the data. The right option depends on the system and how it is used.
Do we need a new data platform first?
Not always. Many use cases can start with targeted pipelines from existing sources. If a broader data foundation is needed, our data engineering team can plan it with you.
Will AI features follow our existing access rules?
That is a design requirement for us. AI features authenticate through your identity provider and only reach data the user or service is permitted to access.
Let's talk about your enterprise ai integration needs.
Someone from our Data & AI practice will reply within one business day to talk through the use case.