Application AI endpoints
Provide a defined interface for a task such as summarisation or classification, including input limits, structured output expectations and useful error responses.
Automation & integration
AI integration & API development
Bring AI capabilities into your existing products, platforms and data environment.
Built around a specific business need.
An AI feature becomes useful when it can work with the systems people already rely on. That often means more than adding a model call: information must move in the correct format, permissions must remain intact and a failure must not leave the business unsure what happened.
Kent Consultancy would design the integration boundary between AI capabilities and your existing applications. The work could expose a limited tool to an assistant, add an AI-backed endpoint to a product or connect generated outputs to an internal workflow. We would make the interface explicit so application teams can understand its behaviour, test it and operate it over time.
Who this is for
- Product teams introducing AI features into existing applications.
- Organisations connecting assistants to internal data and business systems.
- Technical teams needing maintainable interfaces around model-based capabilities.
Where this can make a practical difference.
Restricted agent tools
Expose narrow business operations to an agent without granting broad system access, enforcing authorisation and validation at the service boundary.
Workflow connectivity
Move reviewed AI outputs into existing operational systems, preserving identifiers and confirming whether the destination accepted the intended update.
How we approach the work.
Specify the contract
Define callers, permitted purposes, request and response structures, error states and acceptance criteria. Clarify which system owns each piece of data and each business decision.
Design authentication and data flow
Select an appropriate identity and credential approach, enforce access in the integration layer and minimise information sent to external services. Agree retention and logging boundaries.
Implement validation and control
Validate requests before processing and check model outputs before downstream use. Apply timeouts, usage limits and explicit approval requirements for operations that change consequential records.
Handle reliability risks
Design retries around the semantics of each operation. Where supported, use idempotency or duplicate detection to avoid repeated actions, and report uncertain outcomes rather than guessing that an update failed.
Document and hand over
Test representative success and failure paths, provide interface documentation and agree versioning and change ownership. Include monitoring that distinguishes model, integration and downstream-system failures.
The sequence is tailored to the agreed scope, dependencies and risk. It describes our delivery approach, not a fixed timeline or performance guarantee.
What the engagement can deliver.
- An integration architecture and data-flow description.
- API contracts and documented permission requirements.
- Implemented endpoints or connectors within the agreed scope.
- Validation, error-handling and operational monitoring.
- Test evidence and technical handover documentation.
The proposal identifies the deliverables, acceptance evidence, responsibilities and support arrangements included in your engagement.
Measure what matters to this workflow.
Valid response rate
Requests returning outputs that satisfy the agreed interface contract.
Integration latency
Time spent across the complete request path under defined conditions.
Failure diagnosability
Incidents with enough permitted evidence to identify the failing stage.
These are measures to discuss and baseline. Targets are agreed for the specific system and data; no universal improvement percentage is assumed.
What to prepare for discovery.
- Documentation or technical access for the systems being connected.
- Available engineering contacts and an appropriate test environment.
- Agreed data categories, access rules and operational ownership.
Questions worth asking.
Can any existing system be connected?
That depends on its available interfaces, access arrangements and operating constraints. Discovery should establish feasibility before committing to a particular integration design.
Does model output count as trusted application data?
No. It should be validated against the expected structure and business rules. Consequential content may also require human review before it enters an authoritative record.
What happens if the model provider changes?
An explicit integration contract can reduce the effect on callers, but behaviour still needs re-evaluation. Model substitution should be treated as a tested change, not assumed to be equivalent.