Workflows · Guide
Before buying private AI, draw the data route
A deployment label does not explain where documents, prompts, logs, and backups travel.
Replace the label with a map
An illustrative business asks for private AI because it wants control over company documents. Start by identifying the concern: administrator access, external processing, retention, or cross-customer exposure. These are different requirements. A product hosted on a private server may still call external services, while a cloud design may offer controls that need to be examined individually.
Trace one approved document
Map upload, extraction, embedding, retrieval, model processing, output storage, logs, and backup. Record the operator and retention expectation at each stage. Use a sanitized sample for the workshop. Ask whether a support engineer can see content and whether troubleshooting changes the path. A diagram should reflect actual configured behavior rather than a vendor’s generic architecture picture.
Workflow at a glance
Turn concerns into tests and terms
For each concern, identify a technical check or a contractual question. Test access revocation, retrieval permissions, and deletion behavior within the agreed scope. Where backups retain copies, explain the policy rather than suggesting deletion is instantaneous everywhere. This is a requirements exercise, not legal advice or a certification of compliance.
Choose the lightest design that meets the need
Compare the operating effort of candidate deployments, including updates, monitoring, and recovery. A stricter isolation requirement may justify extra cost; a vague preference should first become a concrete risk statement. The outcome of the workshop should be an agreed data route and responsibility list that can guide implementation and vendor review.
A practical next step
Map a sanitized document with both the business team and supplier. Ask where copies remain after the active record is deleted and list uncertain answers for follow-up. The map is a requirements tool, not legal certification. Resolve different assumptions about external processing, logs, and backups before a deployment label becomes a purchase decision.
Sources and further reading
- Generative AI lifecycle — Generative AI LensGenerative AI lifecycle — Generative AI LensAmazon Web Services · Documentation ·
- Document AI overviewDocument AI overviewGoogle Cloud · Documentation ·
Editorial approach
AI-assisted original writing, checked against linked public sources. Practical recommendations and illustrative scenarios are editorial analysis, not reported client results. Source dates and product details may differ from this collection’s preparation date.