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Moving an AI proof of concept into an existing product

2026-10-13
1 min read
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A proof of concept often runs on prepared data with a single operator. Product integration introduces multiple users and ongoing failure handling.

Identify missing production work

Review authentication, permissions and data lifecycle. Inventory external services and recurring costs. The proposed rollout needs an owner for each dependency.

Test ordinary failures

Simulate unavailable services, invalid outputs and revoked access. Keep the existing product usable where the workflow allows. Monitor outcomes without recording unnecessary confidential content.

Require an operating handover

Ask for configuration, recovery and evaluation procedures. A second operator follows them in a suitable environment. Compare proposals on this complete delivery. The demonstration remains useful evidence of feasibility, while production acceptance verifies the conditions that prepared examples did not cover.

Related reading : Semantic search versus filters: combine retrieval with business constraints. Discuss your project scope with Sky Vaults.

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