Quick answer: Most projects do not train a new model: they configure instructions and retrieve authorised content from a knowledge base or through RAG. Documents need ownership, version and scope.
How to train an AI agent with company data
Most projects do not train a new model: they configure instructions and retrieve authorised content from a knowledge base or through RAG. Documents need ownership, version and scope.
Software, architecture and practical choices
Separate rules from sources, remove duplicates, protect sensitive data and build test questions with expected answers, no-answer cases and outdated content.
Named software is provided as a technical example, not as a partnership or integration guarantee. Features, plans and availability may change; verify documentation, APIs, licences and security requirements.
Controls and metrics
Apply least privilege, log sources and actions, minimise personal data and keep a human escalation path. Measure time released, quality, errors, rework, adoption and process outcomes: message volume alone does not prove value.
Apply least privilege, log sources and actions, minimise personal data and keep a human escalation path. Measure time released, quality, errors, rework, adoption and process outcomes: message volume alone does not prove value.
How to start safely
Choose one frequent, measurable process. Document inputs, authorised sources, outputs, exceptions, ownership and approvals. Begin in observation mode, move to drafts and enable reversible actions only after testing real cases.
- Most projects do not train a new model: they configure instructions and retrieve authorised content from a knowledge base or through RAG. Documents need ownership, version and scope.
- Separate rules from sources, remove duplicates, protect sensitive data and build test questions with expected answers, no-answer cases and outdated content.
- Apply least privilege, log sources and actions, minimise personal data and keep a human escalation path. Measure time released, quality, errors, rework, adoption and process outcomes: message volume alone does not prove value.
Frequently asked questions
What is the main point?
Separate rules from sources, remove duplicates, protect sensitive data and build test questions with expected answers, no-answer cases and outdated content.
What should be tested?
Choose one frequent, measurable process. Document inputs, authorised sources, outputs, exceptions, ownership and approvals. Begin in observation mode, move to drafts and enable reversible actions only after testing real cases.
Is the result guaranteed?
Apply least privilege, log sources and actions, minimise personal data and keep a human escalation path. Measure time released, quality, errors, rework, adoption and process outcomes: message volume alone does not prove value.