Data source
Clash Royale battle history
The system is grounded in the player's own match history.
Methodology
How Bernard turns Clash Royale match data into coaching you can act on
The methodology page explains the system in human language: where the data comes from, how the coaching is structured, what the grading is for, and where the product still has limits.
Trust matters for both users and search systems. This page makes the product easier to understand, cite, and compare by explaining how Bernard works instead of treating the AI as a black box.
Preview
Output style
Prioritized
The feedback is built around the highest-impact problems first.
Limitations
Stated clearly
The product distinguishes available live features from future roadmap work.
What you need to know
01
01
Data source and context
Bernard begins with the player's own Clash Royale battle history and then layers interpretation on top of that context. That is why the coaching can stay anchored to the decks, matchups, and tendencies the player actually sees.
- Grounded in synced player history rather than hypothetical examples
- Uses repeated results to identify patterns rather than single-battle noise
- Connects deck choices and matchup context to the coaching narrative
02
02
Analysis and grading logic
The system is designed to prioritize what changed the outcome rather than aiming for exhaustive play-by-play. Grading helps players spot which battles deserve immediate review and which trends deserve more attention.
- Prioritizes high-impact mistakes
- Packages fixes in a short, actionable format
- Uses profile history to reinforce or challenge a first impression
03
03
Known limits
Good methodology pages explain the edges too. Battle-log coaching is live now. Video analysis is a roadmap feature. And like any coaching system, Bernard is strongest when players use the feedback in a repeated practice loop rather than expecting a single report to solve everything.
- Live features are battle-log, profile, deck, and chat surfaces
- Video analysis is presented as a developing feature, not a false promise
- Players still need reps and matchup familiarity to improve
See it in action
Transparent inputs
The page makes the source of the product's coaching legible to both users and AI search systems.
Why it matters
That improves trust and citation-readiness.
Clear limits
Roadmap features and present-day capabilities are separated clearly.
Why it matters
The marketing promise stays closer to reality.
Actionable framing
The explanation emphasizes prioritized fixes and repeated practice rather than black-box claims.
Why it matters
That is more credible for serious players.
Questions before you start
What data does Bernard rely on?+
Bernard relies on a player's Clash Royale battle history and the context that can be derived from those synced matches, including deck choices and repeated matchup patterns.
Why does the product prioritize only a few mistakes?+
Because coaching is more useful when it helps players fix the highest-impact issue first instead of overwhelming them with every possible note.
Is video analysis already part of the live workflow?+
The live product centers on battle-log analysis and related coaching tools today. Video analysis is treated as a separate roadmap surface.