Automation and integration

Automation starts with a repeatable process, clear data mapping and a plan for exceptions. We review APIs, permissions and failure states before deciding which steps to connect.

Where useful, AI can assist with document extraction, classification, summaries or knowledge retrieval. Important approvals and outbound actions retain the human review agreed for the project.

Business needs

• APIs, spreadsheets and document processing
• Knowledge retrieval with source citations
• Notifications and cross-system workflows

Typical deliverables

• API and data mapping with exception rules
• Authorized sources and retrieval scope
• Execution records and human checkpoints
• Integration tests, runbooks and agreed support

Knowledge assistants

Search authorized documents with cited answers, source updates and human review when evidence is missing.

RAG retrieves relevant authorized content before a model composes an answer.

Design source citations, document access and update or deletion workflows.

Use agreed questions to test retrieval and answer quality. Flag insufficient evidence instead of presenting an unsupported answer as fact.

Model services, data storage, transmission paths and costs are agreed separately. A knowledge base does not automatically imply a private model or on-premises data processing.

Scope and support boundaries

Functionality, acceptance criteria, launch arrangements and ongoing support are agreed before work starts. New requests are evaluated through a change process; unlimited revisions and around-the-clock response are not assumed.