product-intelligence

name	product-intelligence
description	Automated competitor feature tracking, user feedback clustering, and feature validation for the Product department. Monitors competitor launches, aggregates user feedback signals, clusters by theme, and feeds the product self-improvement loop. Use when tracking competitor features, analyzing user feedback, validating feature hypotheses, or running the product UAOP pipeline.
risk	low
tags	["product","research","competitive-intel","feedback"]
created	2026-03-23T00:00:00.000Z
updated	2026-03-23T00:00:00.000Z

Product Intelligence — Product Department Intelligence Engine

Automated competitor feature tracking and user feedback synthesis. UAOP Stage 1 + Stage 5 for Product.

When to use

• Tracking competitor product launches and feature changes
• Synthesizing user feedback from multiple channels
• Validating feature hypotheses before building
• Quarterly product roadmap planning
• Sprint prioritization — "what should we build next?"

Pipeline

Step 1: Competitor Feature Scan

• Monitor competitor changelogs, release notes, Product Hunt launches
• Track feature additions/removals across LangChain, CrewAI, AutoGen
• Identify feature gaps (they have it, we don't) and advantages (we have it, they don't)

Step 2: User Feedback Aggregation

• Collect signals: support tickets, app reviews, community posts, NPS comments, sales call notes
• Cluster by theme using frequency + sentiment analysis
• Rank by impact: revenue-tied requests > nice-to-haves

Step 3: Feature Validation

• For each proposed feature: hypothesis, success metric, estimated effort (RICE score)
• Cross-reference against competitor data — are we filling a gap or innovating?
• Check ICP alignment — does the target user actually want this?

Step 4: Product Intel Report

• Top feature requests (by cluster frequency)
• Competitor moves (new features, pricing changes, positioning shifts)
• Recommended priorities (RICE-ranked)
• Experiment proposals (hypothesis + metric + sample size)

Step 5: Self-Improvement Loop

• Track feature adoption rates post-launch
• Measure experiment win rates
• Reinforce: features with high adoption → invest more; low adoption → investigate why
• Feed outcomes into next cycle's prioritization

Cadence

WEEKLY:   Competitor changelog scan + feedback triage
MONTHLY:  Full product intel report + RICE re-ranking
QUARTERLY: Roadmap review informed by cumulative intelligence

Agents

Agent	Role
pm-agent (L0)	Owns product strategy, approves priorities
feedback-synthesizer (L4)	Clusters and themes user feedback
trend-researcher (L4)	Tracks competitor movements
experiment-tracker (L4)	Monitors experiment results
requirements-analyst (L4)	Validates feature specs