Ask one design question. Slice finds relevant games, studies how each one works, and shows what players, patches, and trade-offs add to the answer.
A research trail you can inspect, compare, and carry into your next design decision.
Mappex pulls shipped-game context, market metadata, Steam reviews, playtime signals, and change history into one research frame.
Mappex pulls shipped-game context, market metadata, Steam reviews, playtime signals, and change history into one research frame.
Feedback is decomposed into design-relevant signals: systems, content, player types, sentiment, friction, and recurring praise.
The pipeline connects those signals into tensions, design philosophy, transferable learnings, and questions worth asking next.
Every game gets its own deep pass: implementation patterns, player feedback counts, evidence gaps, traps, and contextual notes.
Validate & synthesize
Results are validated for accuracy and patch currency, then synthesized into a cross-game comparison with failure modes and transfer rules.
Multi-criteria game search
Consistent depth across 10+ games
Player feedback counting
Source & patch validation
Cross-game synthesis
| Manual research | AI with Deep Research | Mappex Slice | |
|---|---|---|---|
Multi-criteria game search | ~ Researcher assembles matches by hand | ~ Suggests matches from its available context | ✓ Criteria-driven candidate set |
Consistent depth across 10+ games | ~ Depth varies with time and method | ✗ Consistency depends on prompt and context | ✓ One research pass per game |
Player feedback counting | ✗ Sample and count by hand | ✗ Usually qualitative mentions | ✓ Mappex feedback analysis |
Source & patch validation | ~ Check updates across sources | ✗ Requires a fresh source pass | ✓ Patch-aware research when requested |
Cross-game synthesis | ~ Synthesize comparisons yourself | ~ Patterns summarized in prose | ✓ Structured options and failure modes |
Use Slice when you know the question but not the answer across games.