AI INTEGRATION

AI capabilities. Engineered into your applications.

Integrate language models, retrieval and structured outputs with business data and applications. We design permissions, evaluation, approval steps and failure handling around the task, including local or externally hosted models where appropriate.

Define the task, data and quality requirements

We define the task, approved information sources, application interfaces and expected output. Retrieval, structured responses and permission controls are designed around the workflow and the consequence of errors.

Retrieval and source context
Structured outputs and application integration
Approval steps and evaluation workflows

Example project scopes

Integrations may support internal knowledge retrieval, document processing or enquiry classification. Each use case requires representative evaluation, clear review responsibilities and a defined failure path.

An internal assistant retrieves approved material with sources.
A document workflow creates structured drafts for verification.
An enquiry tool classifies work while uncertain cases stay with the team.

Deliverables and handover

Deliverables include the use-case and data-flow design, prototype, evaluation results, implemented in-scope integration and operating guidance. Model hosting, access, cost and monitoring arrangements are agreed explicitly.

Scope: Models can make mistakes. Hosting, data access, failure handling and operating cost remain explicit design choices.