System
A layered system. Ingestion at the bottom feeds the synthesis layers in the middle. The decision audit log closes the loop back into the priority engine — so the system learns from every override.
Decision Audit Log
Every decision logged with rationale + predicted outcome. Quarterly retrospective auto-surfaces predicted-vs-actual.
Roadmap Drafting + Audience-Tuned Render
Pre-fills quarterly plans, simulates sequence tradeoffs, renders the same plan in four audience modes (Exec, Eng, Sales, CS).
Stakeholder Alignment Copilot
Surfaces conflicts, drafts alignment summaries, predicts escalation risk. Compresses the coordination loop.
Dynamic Priority Engine
Continuous synthesis, gated publication. Flags significant re-prioritization without auto-resequencing the public plan.
Opportunity Synthesis Engine
Semantic clustering of duplicate requests, summarization of customer pain, theme extraction. Replaces hours of manual tagging.
Context Graph
Continuous ingestion across Slack, Jira, CRM, support tickets, customer calls, analytics. Builds an organizational memory layer.
Why this shape
Most prioritization tools live in layer 5 alone. They are systems of record — they store the prioritized list after the PM has already done the thinking. This system inverts that. Layers 1-3 do the synthesis. The PM operates in 4-5. Layer 6 makes the system learn.
The build in this case study covers layers 4-5 with realistic mocked data. Layers 1-3 (ingestion, synthesis, engine) are the architectural precondition; their existence is assumed in the brief.