Juno PM Prioritization Copilot

01 · The problem

Product signals were everywhere. Prioritization confidence was not.

Product Managers were working across fragmented customer, business, product, and technical signals. Support tickets, customer interviews, Jira issues, Slack discussions, sales feedback, and product documentation all contained useful evidence, but synthesizing them into a defensible prioritization recommendation required significant manual effort. The challenge was not a lack of information — it was turning fragmented information into traceable, evidence-backed product judgment.

02 · What I learned

Trust required more than a smart answer.

The central product insight was that PMs would not trust a recommendation simply because it sounded intelligent. Juno needed to show where evidence came from, how signals related to strategy, and where uncertainty remained. The product had to optimize for both recommendation quality and decision transparency.

03 · The product decision

A grounded copilot, not an autonomous roadmap manager.

I chose a copilot model in which Juno retrieves and synthesizes evidence, compares signals against strategy, scores opportunities, and generates recommendations — but the Product Manager retains authority over prioritization and roadmap decisions.

04 · How Juno works

From fragmented signals to evidence-backed recommendations.

The workflow separates signal collection, retrieval, strategic comparison, and recommendation generation — with the PM as the final reviewer at every cycle.

05 · Data and trust design

Grounded retrieval was a product requirement, not just a technical choice.

06 · Trust and evaluation

I designed evaluation around trust, traceability, and usefulness.