What should we decide before ordering?
What decision is the AI allowed to influence?
Custom AI integrations applies this principle to source system, documented API and aI service boundary. Drafting a reply, finding a policy paragraph and switching a light carry different risks. We classify whether the output is advice, a suggestion, a reversible command or an action with material effect. The delivery timeline surveys source system before design approval, then tests aI service boundary during commissioning. For this service, the scope records how target-system command connects to observed and reviewed result, then verifies that relationship during commissioning or recovery testing.
Custom AI integrations applies this principle to interface implementation, authentication and logging and customer workflow owner. Retrieval, tools and larger models may reduce some errors, but none turns generated text into verified truth. We design citations, confidence cues and human review around the actual consequence. Medical, legal, financial, employment, security and life-safety decisions require qualified owners outside the model. For this service, the scope records how source and target vendors connects to interface implementation, then verifies that relationship during commissioning or recovery testing.
Where may data travel and remain?
Custom AI integrations applies this principle to aPI support, data preparation and identity method. It does not mean every connected feature is offline or that privacy appears automatically. Model downloads, telemetry, speech services, connectors, backups and support tools can cross that boundary. For this service, the scope records how model consequence connects to maintenance owner, then verifies that relationship during commissioning or recovery testing.
Custom AI integrations applies this principle to interface timeout, limit retries and preserve source state. A knowledge assistant must not reveal a document merely because it indexed the words. Service accounts receive the narrow access needed for their task, secrets stay out of prompts and logs, and retention is set deliberately. The customer decides the lawful basis, staff policy and approved datasets. For this service, the scope records how use manual fallback connects to reconcile after recovery, then verifies that relationship during commissioning or recovery testing.
How is accuracy tested after launch?
Custom AI integrations applies this principle to valid request, invalid payload and expired credential. Acceptance testing uses representative inputs, awkward phrasing, missing information, denied requests and known edge cases. The team records the expected outcome, model response, source citation, tool call and reviewer decision. Maintenance repeats the valid request and invalid payload checks after material changes, updates or reported faults. For this service, the scope records how rate limit connects to target unavailable, then verifies that relationship during commissioning or recovery testing.
Custom AI integrations applies this principle to interface implementation, authentication and logging and customer workflow owner. Version records and a small repeatable test set make drift visible after an update. There is always a non-AI route for important work: manual control, ordinary search, a queue for staff review or a disabled automation. We prefer a narrow assistant whose limits are obvious to a broad agent with vague authority. A lower-complexity alternative remains valid when source and target vendors can be handled by the documented manual or existing-system route. For this service, the scope records how source and target vendors connects to interface implementation, then verifies that relationship during commissioning or recovery testing.
