For the work between systems
You may need a new service, a connection to an enterprise application, or backend support for an AI capability. The starting point is the current architecture and a specific gap in how the systems work together.
This work can stand on its own. A conventional backend integration may be the right first step before an AI feature is considered.
What we can deliver
- Custom backend services and API endpoints for an agreed use case.
- Connectors between existing applications, data sources and internal tools.
- Data preparation and service interfaces around AI and retrieval components.
- Validation, access controls, error handling and operational visibility within the agreed scope.
Work with the architecture you have
We map the relevant interfaces, ownership boundaries and failure cases before choosing the implementation. The aim is a service your team can understand and operate within its existing environment.
A bounded first delivery can establish the interface and prove the data flow. Further scope is based on what that first integration reveals, rather than an assumed platform rewrite.
Agree what ready means
Acceptance criteria can cover API behaviour, invalid inputs, permissions, upstream failures and the expected operating workload. Deployment and handover responsibilities should be explicit from the start.
For AI-related services, the backend also needs to handle unavailable context, model errors and uncertain outputs. Those behaviours belong in the integration design, not only in the prompt.