A focused approach that uses AI to draft stories, criteria, and maps—then human review to finalize a testable, traceable backlog.
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AI‑Assisted Requirements & Stories turns intent into shippable, testable specs. We mine research, tickets, analytics, and stakeholder input to generate epics, user stories, acceptance criteria, and non‑functional requirements. Output is consistent, de‑duplicated, and tied to outcomes your business and engineering teams agree on.
Through short workshops and document mining, AI drafts story maps, edge cases, and state coverage. Product and engineering refine language, add constraints, and link dependencies. Traceability matrices connect stories to designs, data, and KPIs, and we select a thin slice that proves value fast.
A complete requirements pack: PRD, prioritized backlog, story map, acceptance tests, definitions of Ready/Done, event schema, and UX/content specs. Estimates and sequencing align to team capacity. Everything is version‑controlled and ready for handoff to Jira or Azure DevOps.
Policy‑as‑code checks, approvals, and audit trails keep requirements reliable. We document privacy, accessibility, and security constraints; link stories to evaluation metrics; and add change‑management steps—so scope stays controlled and decisions remain explainable.
A focused approach that uses AI to draft stories, criteria, and maps—then human review to finalize a testable, traceable backlog.
No. AI accelerates drafting; product and engineering own decisions, priorities, and acceptance of quality.
Jira, Azure DevOps, Linear, Trello—plus Figma and analytics tools for linking designs, data, and events to each story.
Within two weeks for a requirements pack and prioritized backlog; 8–12 weeks to launch a production thin slice.
Acceptance test pass rates, defect escape rate, story cycle time, and KPI deltas tied to the features shipped.
Change control with impact analysis, approvals, and version history—so trade‑offs are transparent and auditable.
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