Weighted average of the three sub-scores. No confidence multiplier. No hidden penalty. The popover on the score chip shows the exact math.

mindberScore = round(
  innovationIndex x 0.40
  + functionalityScore x 0.50
  + activityScore x 0.10
, 1)

When one sub-score is missing, the missing input's weight redistributes proportionally across the remaining two. Two of three inputs labels confidence as medium; all three as high. With fewer than two scored inputs, no Mindber Score is shown (insufficient) and the page renders “Score pending” instead.

Fallback weights when one input is missing

• No Activity - Innovation 44% · Functionality 56%
• No Innovation - Functionality 83% · Activity 17%
• No Functionality - Innovation 80% · Activity 20%

Confidence states

• High - all three sub-scores present. Default 40/50/10 weights apply.
• Medium - two of three sub-scores present. Fallback weights as above.
• Insufficient - fewer than two present. No Mindber Score is shown.

Score band interpretation

Bands are absolute - they do not shift with the category. The category percentile label (below) tells you how the product ranks against its peers separately.

Average of three sub-scores - Novelty, Differentiation, Concept clarity - each 0-10.

innovationIndex = round((novelty + differentiation + conceptClarity) / 3, 1)

• Novelty - how distinct the core concept is from the category baseline. LLM-inferred, grounded in the category's top 3 tools and common features (see below).
• Differentiation - count of features present in this product but missing from >=50% of category peers. Scaled to 0-10: min(10, unique x 1.5). LLM-inferred, verified against the category common-features list.
• Concept clarity - how unambiguous the value proposition reads from the tagline and description alone. LLM-inferred, grounded in the source copy.

Confidence-weighted average of four sub-scores. Feature breadth and Performance reliability are deterministic - computed from source data with no LLM involvement. The other two are LLM-inferred.

functionalityScore = sum(score x baseWeight x confidence) / sum(baseWeight x confidence)

Base weights:breadth 0.25 · depth 0.30 · integrations 0.20 · performance 0.25. Confidence weighting means a sub-score with thin evidence pulls less weight than one backed by real data.

• Feature breadth (deterministic). Count of features from collected source records. Score = min(10, round(log2(count + 1) x 2)). 5 features - 5; 15 - 8; 30+ - 10.
• Feature depth (LLM). Quality of the top features vs the category leader. Cites specific named features.
• Integration surface (LLM with caveat). Public API + webhook + native integration evidence. Where no integration evidence exists, the score defaults to neutral (5) with confidence no higher than 0.4.
• Performance reliability (deterministic). Computed from the product's website engagement score and homepage response time: 0.7 x (livenessScore / 10) + 0.3 x responseTimeScore. Response-time score is 10 for no more than 500 ms, linearly degrading to 1 at 5000 ms or more.

A score in isolation isn't meaningful - a 7.3 in a crowded category is different from a 7.3 in a deep one. We compute per-category distributions (median, p25, p50, p75, p90) and bucket each final score into one of five labels:

• p90 or highertop 10%
• p75 or highertop 25%
• p50 or higherabove median
• p25 or higherbelow median
• below p25bottom 25%

A baseline is only published once a category has at least 10 scored products; below that minimum, percentile is shown as unranked.

• Full baselines (median/p25/p75/p90) refresh weekly. A drift alert fires when a category median moves by more than 1.0 point.
• Percentile labels refresh daily. The underlying raw scores stay fixed until the product is regenerated - only the bucket label updates.
• Product scores are regenerated whenever the source inputs change (name, tagline, description, categories, candidate alternatives) or when the scoring methodology is upgraded.

• Every LLM-written sentence must cite a specific feature, use case, or baseline data point. Marketing adjectives are banned.
• A banned-word safety filter runs on every rationale; offending text is blanked out rather than retried.
• Any sub-score below 4/10, composite confidence below 0.6, or a safety-filter replacement flags the record for admin review before publication.
• Scores compare within category, never absolute. “Below median” is factual; “bad” is not a word we use.