Feature · Decide

Prioritize
With Evidence.

Your last sprint planning was 90 minutes of opinions and zero data. Every product team argues about what to build next. MindBacklog replaces opinion with evidence — scoring features with WSJF and RICE, enriched by real customer signals from 6 channels and your MIND product context. Three buckets: Radar → Ideas → Roadmap. Evidence decides what moves forward.

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Scoring Frameworks

The Right Framework.
The Right Data.

Choose the framework that fits your team. MindBacklog enriches each with your actual customer data and product context — not generic estimates.

WSJF

Weighted Shortest Job First

The SAFe-recommended framework for lean teams. Prioritize by maximizing business value relative to effort. MindBacklog's AI auto-estimates business value from actual customer feedback volume and sentiment.

Score = (User Value + Time Criticality + Risk Reduction) ÷ Job Size

RICE

Reach · Impact · Confidence · Effort

The popular framework for balanced prioritization. Each dimension can be manually set or AI-suggested based on linked feedback signals, customer segments, and historical velocity data.

Score = (Reach × Impact × Confidence) ÷ Effort

Custom

Your Own Framework

Define your own scoring dimensions with custom weights. Combine quantitative metrics from your feedback pipeline with qualitative inputs from your team — all in one score.

Score = Σ (Dimension × Custom Weight)

Context-Aware Suggestions

MindBacklog's AI suggests scores based on real product data: how many customers mentioned this, what's the sentiment trend, how does it align with your stated product vision. Override any suggestion with a click.

AI Score = f(feedback signals, context, product vision, trends)

Why Context Matters

Generic Scoring
vs. Context-Aware

Most tools let you fill in numbers. MindBacklog fills them in for you — with data from your actual product.

Generic Prioritization

✕ Scores based on gut estimates and team opinions
✕ No link between scoring inputs and customer data
✕ HiPPO effect — highest paid person's opinion wins
✕ Static scores that don't update with new feedback
✕ Features scored in isolation from product strategy

MindBacklog's Approach

✦ AI suggests scores from linked feedback signals
✦ Real customer evidence behind every dimension
✦ Data-driven — removes opinion bias from prioritization
✦ Living scores that evolve as new signals arrive
✦ MIND engine aligns scoring with product vision

AI-Powered Features

Intelligence at
Every Step

Beyond scoring — AI helps you discover, describe, and decide across your entire Ideas bucket.

📝

AI Descriptions

Generate comprehensive feature descriptions from linked feedback signals with one click.

📊

Auto T-Shirt Sizing

AI estimates effort (XS/S/M/L/XL) based on feature complexity and your team's historical velocity.

🔗

Signal Linking

Automatically matches new feedback to existing ideas — building evidence behind each feature request.

📈

Trend Detection

Surface emerging themes from your feedback pipeline. Know what your users want before requests pile up in Radar.

🏷️

Smart Tags

AI tags ideas by product area, complexity, and customer segment — making filtering and analysis instant.

⚡

Batch Scoring

Score or re-score your entire Ideas bucket at once. AI applies updated context across all features simultaneously.

PrioritizeWith Evidence.

The Right Framework.The Right Data.