Telemetry Analysis01
Compare speed, gear, and RPM across race sessions.
Turn raw FastF1 data into readable performance signals without forcing users to parse charts alone.
A
Apex Intelligence
Virtual F1 Race Engineer
Race engineer system online
Build faster race calls from telemetry, not guesswork.
Apex Intelligence turns Formula 1 session data into explainable pit-window, tyre-decay, and undercut insight so solo builders can demo real agentic AI with real motorsport context.
Live telemetry channels
04
Race strategy modules
03
Decision window latency
< 5s
Strategy Core
Japanese GP // Race // NOR
Strategy alert
Pit window opens in 4 laps.
Medium compound degradation is crossing the threshold where undercut exposure becomes material.
Tyre delta
+0.31s/lap
Undercut risk
High
Traffic loss
1.8s
Confidence
74%
SECTOR 1 +0.184
SECTOR 2 -0.092
TYRE MEDIUM
DRS ENABLED
PACE DELTA -0.31
PIT WINDOW LAP 18-22
UNDERCUT RISK HIGH
BATTERY DEPLOY PUSH
SECTOR 1 +0.184
SECTOR 2 -0.092
TYRE MEDIUM
DRS ENABLED
PACE DELTA -0.31
PIT WINDOW LAP 18-22
UNDERCUT RISK HIGH
BATTERY DEPLOY PUSH
Core capabilities
A landing page that sells the system, not just the screen.
The first fold should signal what the product does. The next fold should prove why users should believe it.
Tyre Intelligence02
Read tyre decay before the pace cliff hits.
Surface lap-time decay, compound context, and degradation signals as strategy-ready insight.
Pit Strategy03
Spot undercut windows and strategic risk earlier.
Translate telemetry, gaps, and session context into explainable pit-window recommendations.
Race Narrative04
Understand why the recommendation exists.
Show assumptions, fallback behavior, and reasoning instead of hand-wavy AI output.
Strategy story
Translate race data into decisions users can trust.
The strongest version of this homepage makes strategy reasoning visible, explainable, and emotionally compelling at the same time.
Decision flow
Show the thinking, not just the answer.
This section is the bridge between cinematic branding and trust. It should visually communicate how raw race data becomes a strategy recommendation users can believe.
Pace decay
0.31 s/lap
Pit window
L18-L22
Fallback honesty
Structured
01
Ingest race context
Year, event, session, and driver selection become the operating frame before the system makes any claim.
02
Read telemetry signals
Speed, gear, RPM, and pace delta are normalized into a compact snapshot a human can verify quickly.
03
Compare strategic scenarios
The engine checks undercut pressure, tyre decay, and expected traffic loss before escalating a recommendation.
04
Deliver explainable action
Outputs stay grounded in measurable evidence, assumptions, and fallback honesty instead of black-box AI theatre.
Mission control preview
End the story by showing a real operator surface.
A high-impact landing page should end with proof that the product already has substance.
Live Telemetry Query
A compact preview of the real mission-control loop.
Submit a telemetry or strategy prompt to view results.
Operator view
Give users a real cockpit, not a fake product render.
The homepage should end by proving the system already works. Reuse the real telemetry query surface so brand promise and product proof stay tightly connected.
Recommendation preview
ExplainableSuggested call
Box before the undercut window peaks.
Pace decay is compounding while traffic-loss projection remains acceptable within the next stop window.
- Recommendation ties to visible metrics instead of invisible prompting.
- Query flow is close enough to the real dashboard to reduce trust gap.
- Empty, loading, and error states are already grounded in the product surface.
Final call
Turn race data into a mission-control moment users actually want to try.
The MVP only gets one first impression. Make it feel like telemetry, strategy, and AI are finally working as one system.