KD-016 · Workflow

Recommendation Flow

Show how bounded context becomes a reviewable recommendation rather than an opaque automated action.

Evidence level: Verified
Executive audience: Operations leader, revenue leader, product owner
Publication status: Published after human review

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Recommendation Flow. Show how bounded context becomes a reviewable recommendation rather than an opaque automated action. Business problem: Teams have information but no consistent, inspectable path from context to the next best action. Claim boundary: Code-backed RachelOS implementation pattern. It does not establish revenue, adoption, conversion, healthcare deployment, or a result outside the demonstrated implementation.

flowchart LR
  F[Reviewed facts] --> S[Current state]
  S --> Q[Priority and decision rule]
  Q --> R[Recommendation]
  R --> H[Human accepts, changes, or skips]
  H --> O[Outcome recorded]

Why this matters

Teams have information but no consistent, inspectable path from context to the next best action.

Executive decision

Expose the inputs, decision rule, recommendation, and approval path so the operator can accept, change, or skip it.

Claim boundary

Code-backed RachelOS implementation pattern. It does not establish revenue, adoption, conversion, healthcare deployment, or a result outside the demonstrated implementation.

Evidence summary

rachelos:ev-rachelos-canonical-queue

One ranked, freshness-classified list of who needs attention now and why, recomputed on every signal, replaces reconstruction across four tools.

Boundary: Code-backed priority layer. No volume or conversion metric implied.

rachelos:ev-rachelos-human-approved-ai

AI drafts and recommendations wait in a dedicated operator queue for human review before anything is sent.

Boundary: Enforced in code, not promised. No send-rate metric.