Bring useful AI features into the software your team already uses. I develop custom integrations for Laravel applications, websites, CRMs and internal systems, connecting model APIs to your existing data, permissions and business rules.
AI Integration Services
An AI integration needs more than an API call. It must fit the user interface, respect access controls and behave predictably when the provider returns an unexpected result.
Add summarisation, categorisation, extraction or assisted writing to existing screens. Reuse your authentication, policies and background jobs so staff can work within a familiar application.
Retrieve only the records needed for a specific task and validate proposed changes before saving them. Keep account boundaries and user permissions in place across every integration.
Connect a suitable provider through server-side requests, with protected credentials and defined output formats. Select the approach around the task, data requirements and expected usage rather than a model name alone.
Queue work that should not block the interface, show progress and make failures visible. Timeouts, usage limits and controlled retries help keep the feature usable when external services are slow.
Delivery and controls
I inspect the existing code and APIs before proposing changes. The aim is a focused feature that follows your project conventions and can be maintained by the next developer.
Check the application stack, authentication, integration points and available data. Identify any provider or platform constraints before defining the first release.
Treat model responses as untrusted input. Check required fields, allowed values and permissions in application code before using a response to update records or trigger work.
Keep API keys on the server, limit usage by user or workflow and avoid sending unnecessary data. Agree logging and retention so diagnostics do not expose sensitive content.
Make provider configuration, error handling and manual alternatives clear. Account for changes to external APIs and agree who will monitor failed jobs and ongoing usage.
Example project
An illustrative feature for an existing Laravel application: staff need a concise account summary before responding to a customer.
Use the signed-in user’s permissions to collect relevant account notes. Exclude unrelated or restricted records.
Generate a draft summary with links back to the records used. Show when it was generated and flag missing context.
Let staff check the original records and edit the draft. Keep any customer-facing action separate and explicit.
Tell me what happens today, which systems are involved and what a useful improvement would look like.
AI Integration Services FAQ
Yes. I review the application first, then build the feature using its existing conventions, permissions and integrations where practical. Older applications may need supporting maintenance before a new feature is introduced.
Potentially, through supported APIs or custom server-side functionality. The exact approach depends on the platform, its permissions and the data the feature needs. I verify those capabilities during scoping.
Secret provider credentials should stay on the server. Browser requests should use your application’s authenticated endpoints, with appropriate authorisation, validation and usage limits.
The feature should show a clear status and offer an agreed fallback. Depending on the task, that might mean a controlled retry, a queued job or continuing manually without the generated result.
Where practical, I keep provider-specific calls focused so a future change is manageable. A replacement still needs evaluation because models differ in supported features, response formats, costs and behaviour.
