Retriever deployment requires explicit scheduling and data-placement decisions

The customer problem is a document assistant whose data boundaries or infrastructure costs remain unclear. Include records owners, platform IT, security and finance. Ask which content can leave the network, what demand must be served, and which modalities matter. Offer a bounded placement and readiness assessment using a representative approved document collection. The value hypothesis is a workable service boundary with predictable capacity needs. Avoid promising lower costs from self-hosting or inferring staff savings from hardware fit. This source supports technical discovery, not a validated financial case.

Design a minimal pipeline, then trace every endpoint, storage dependency and identity handoff. Require approved test documents and an isolated environment with a reproducible manifest. Validate scheduling under the intended cluster policy, restart behavior and denial of prohibited external traffic. Compare extraction quality with a human-reviewed baseline before adding answer generation. A useful proof of value measures completed documents, latency and recovery while maintaining access boundaries. Do not treat an apparently healthy container as evidence that the entire workflow functions.

Platform operations should own capacity and recovery; the records owner should approve content and outputs. Implement staged artifact promotion, ingestion monitoring and a rollback runbook. Dependencies include storage, maintenance capacity and a defined release process. Train support staff to distinguish extraction errors from scheduling failures. Review data placement before onboarding users and after optional services change. Proposed acceptance criteria are successful replay of the agreed corpus, no prohibited egress and recovery within locally agreed limits. Risks include hidden dependencies and an unrepresentative test collection.