Turning racing telemetry into actionable coaching advice with AI
A driving analytics app that reads an uploaded racing lap, lines it up against a reference lap, and turns the gaps between them into driving and setup advice a coach can actually use — faster, and for more drivers at once.
Service
AI feature development
Industry
Motorsport, sim racing & driver coaching
Product
Driving & telemetry analytics web application
Users
Racing coaches, venue managers, engineers & drivers
Core functionality
Lap comparison, telemetry analysis & AI-generated recommendations
The challenge
Racing telemetry is full of useful information about how a driver performed, but pulling that information out takes time and a fair amount of specialist knowledge.
To give useful feedback, a coach might need to dig through several data channels, spot the differences that actually matter between two laps, figure out where time got lost, and then translate all of that into advice the driver can understand.
That’s manageable for one driver. It gets a lot harder when a coach or venue manager is trying to do it for dozens of customers.
A few problems kept coming up:
telemetry analysis took a lot of manual work
coaches had to review every lap on its own
the important differences were scattered across several data channels
raw telemetry was hard for customers to make sense of
consistent advice depended on which coach was available, and how experienced they were
managers could only support so many customers in a given session
engineering observations and driving advice had to be worked out separately
The client needed something that could speed up the analysis without replacing the coach’s judgment.
The AI opportunity
The application already had access to structured racing data — the driver’s lap replay and vehicle telemetry. That opened up a chance to compare a customer’s lap against a chosen reference and automatically flag the areas most likely to explain the gap in performance.
The goal was never to spit out generic racing tips. Whatever the system produced had to be grounded in the actual lap data and the specific differences between the driver and the reference.
From there, the analysis becomes a starting point for the coach, who reviews it, adds their own read on things, and gets to personalized guidance faster.
The solution
We built an analytics workflow that takes an uploaded racing lap and compares it against a reference lap.
The application looks at differences in telemetry and driving behavior across the whole circuit, picks out the patterns that matter, and turns them into two kinds of recommendations:
Driving advice
Guidance on braking, acceleration, steering, racing line, corner entry, apex behavior, and corner exit.
Engineering advice
Observations about how the vehicle is behaving and setup adjustments worth looking into for stability, consistency, or performance.
Instead of starting from raw charts and hunting for every difference by hand, coaches get a structured analysis that already points to the areas worth reviewing.
Telemetry comparison — driver vs. reference across one track section; the system flags where time is lost
How the analysis works
1
Uploading the racing lap
The user uploads a supported lap replay or telemetry file. The app processes the driving and vehicle data and gets it ready for comparison.
2
Selecting a reference
The lap gets compared against a reference that represents a stronger run — a coach, an experienced driver, a previous customer lap, or another approved source.
3
Comparing telemetry
The system looks at differences across the relevant sections of the circuit: speed, throttle, brake, steering, gears, acceleration and braking points, corner entry and exit, vehicle balance, and time gained or lost per section.
4
Identifying meaningful differences
Not every difference deserves a coach’s attention. The system prioritizes the patterns most likely to affect lap time, consistency, or vehicle control, so nobody’s drowning in small variations.
5
Producing recommendations
The analysis turns into clear driving and engineering observations — a solid starting point for reviewing the lap and putting together personalized advice.
Key capabilities
01
Reference-based lap analysis
Performance gets measured against a specific reference, not some generic set of driving rules.
Advice stays relevant to the actual circuit, vehicle, and performance target in front of the driver.
02
Automated telemetry comparison
Multiple data channels get processed at once, with the most important differences pulled to the top.
Coaches start from a prepared analysis instead of reviewing every graph from scratch.
03
Track-section insights
Differences get tied back to specific corners or sections of the circuit.
Coaches can explain not just how much time was lost, but where — and what behavior caused it.
04
Driving recommendations
Practical observations a driver can actually act on.
Brake later or more progressively, carry more corner speed, get on the throttle earlier, try a different gear, tighten the line, cut down steering corrections, prioritize exit speed.
05
Engineering recommendations
Vehicle behavior worth a closer look on the setup side.
A faster jumping-off point for evaluating stability, traction, braking behavior, or balance through different corner types.
06
Coach-supported decisions
AI analysis backs up professional judgment — it doesn’t replace it.
Coaches check the findings, add context, and adapt the advice to where the driver actually is.
Implementation approach
The project brought together telemetry processing, performance comparison, and AI-generated explanations in one workflow.
Implementation focused on:
Importing and validating racing telemetrySynchronizing laps for meaningful comparisonDividing the circuit into useful analysis sectionsDetecting significant differences between lapsDistinguishing meaningful patterns from normal variationTranslating technical data into understandable adviceSeparating driving observations from engineering recommendationsPresenting analysis coaches can review quicklyKeeping the coach in control of the final guidance
Traceability
Recommendations stay tied to the telemetry differences that produced them. A coach can always see why the system suggested something and decide for themselves whether it fits the customer.
Before the AI-assisted workflow
1Receive the customer’s lap file
2Import or open the telemetry
3Select a suitable reference
4Review the laps across multiple data channels
5Identify the sections with the largest differences
6Determine which differences are meaningful
7Interpret the likely driving or vehicle behavior
8Prepare understandable feedback
9Discuss the findings with the customer
After the AI-assisted workflow
1Upload the customer’s lap
2Select the reference lap
3Review prioritized differences and generated recommendations
4Add the coach’s own interpretation
5Deliver personalized advice to the customer
The result
The app shrinks the gap between getting a customer’s lap and having something useful to tell them.
Coaches and venue managers no longer start every analysis staring at a blank set of telemetry charts. The system prepares an initial comparison, flags the differences that matter, and lays out possible explanations and recommendations. In practice, visual lap analysis now takes about half the time it used to — and the write-up that reaches the driver carries far more detail than a coach could reasonably assemble by hand.
That frees the coach up for the part of the job that actually needs a human: checking the findings, understanding what the customer needs, and delivering advice that fits them.
With less routine analysis per driver, the business can support more customers in the same amount of time — without losing the expertise that made the coaching worth paying for in the first place.
The role of AI
AI works here as an analysis and communication layer inside a structured telemetry workflow — not a black box handing down verdicts. It doesn’t judge a lap out of context or pull advice from generic racing knowledge; recommendations come from measured differences between the uploaded lap and the reference. The coach stays responsible for reviewing the output and deciding what actually reaches the customer.
Business impact
✓
Visual lap analysis, twice as fast
Automated comparison roughly halves the manual review per lap — while surfacing more detail than before.
✓
More customers supported
Coaches and managers can prepare feedback for more drivers in the same amount of time.
✓
More consistent analysis
A repeatable process for spotting and presenting the lap differences that actually matter.
✓
Higher-value coaching time
More time spent interpreting results and talking with customers, less spent building the initial analysis.
✓
Accessible performance data
Technical telemetry turns into advice a customer can actually understand and act on.
✓
Combined driving & engineering insights
Driver behavior and setup considerations show up together in one workflow.
✓
Scalable coaching services
Data-backed coaching can grow without manual analysis time growing right alongside it.
→From raw telemetry to structured guidance
Complex racing data becomes prioritized driving and engineering observations.
→From manual review to assisted analysis
Coaches get a prepared comparison instead of analyzing every channel from scratch.
→From limited capacity to scalable coaching
Less routine analysis time means valuable feedback reaches more customers.
→From automated output to expert advice
AI supports the coach’s decisions while professional judgment still runs the show.
AI that makes experts faster — not absent
By combining telemetry analysis, reference-based comparison, and AI-generated recommendations, we built a tool that helps coaches go from raw racing data to personalized customer advice a lot faster than before.
Have a workflow where expert analysis is the bottleneck? We can scope an AI feature that fits into it — with your professionals staying firmly in control.
Turning racing telemetry into actionable coaching advice with AI
A driving analytics app that reads an uploaded racing lap, lines it up against a reference lap, and turns the gaps between them into driving and setup advice a coach can actually use — faster, and for more drivers at once.
Project overview
ServiceAI feature development
IndustryMotorsport, sim racing & driver coaching
ProductDriving & telemetry analytics web application
Racing telemetry is full of useful information about how a driver performed, but pulling that information out takes time and a fair amount of specialist knowledge.
To give useful feedback, a coach might need to dig through several data channels, spot the differences that actually matter between two laps, figure out where time got lost, and then translate all of that into advice the driver can understand.
That’s manageable for one driver. It gets a lot harder when a coach or venue manager is trying to do it for dozens of customers.
A few problems kept coming up:
telemetry analysis took a lot of manual work
coaches had to review every lap on its own
the important differences were scattered across several data channels
raw telemetry was hard for customers to make sense of
consistent advice depended on which coach was available, and how experienced they were
managers could only support so many customers in a given session
engineering observations and driving advice had to be worked out separately
The client needed something that could speed up the analysis without replacing the coach’s judgment.
02
The AI opportunity
The application already had access to structured racing data — the driver’s lap replay and vehicle telemetry. That opened up a chance to compare a customer’s lap against a chosen reference and automatically flag the areas most likely to explain the gap in performance.
The goal was never to spit out generic racing tips. Whatever the system produced had to be grounded in the actual lap data and the specific differences between the driver and the reference.
From there, the analysis becomes a starting point for the coach, who reviews it, adds their own read on things, and gets to personalized guidance faster.
03
The solution
We built an analytics workflow that takes an uploaded racing lap and compares it against a reference lap.
The application looks at differences in telemetry and driving behavior across the whole circuit, picks out the patterns that matter, and turns them into two kinds of recommendations:
Driving advice
Guidance on braking, acceleration, steering, racing line, corner entry, apex behavior, and corner exit.
Engineering advice
Observations about how the vehicle is behaving and setup adjustments worth looking into for stability, consistency, or performance.
Instead of starting from raw charts and hunting for every difference by hand, coaches get a structured analysis that already points to the areas worth reviewing.
Telemetry comparison — driver vs. reference across one track section; the system flags where time is lost
04
How the analysis works
1
Uploading the racing lap
The user uploads a supported lap replay or telemetry file. The app processes the driving and vehicle data and gets it ready for comparison.
2
Selecting a reference
The lap gets compared against a reference that represents a stronger run — a coach, an experienced driver, a previous customer lap, or another approved source.
3
Comparing telemetry
The system looks at differences across the relevant sections of the circuit: speed, throttle, brake, steering, gears, acceleration and braking points, corner entry and exit, vehicle balance, and time gained or lost per section.
4
Identifying meaningful differences
Not every difference deserves a coach’s attention. The system prioritizes the patterns most likely to affect lap time, consistency, or vehicle control, so nobody’s drowning in small variations.
5
Producing recommendations
The analysis turns into clear driving and engineering observations — a solid starting point for reviewing the lap and putting together personalized advice.
05
Key capabilities
01Reference-based lap analysis
Performance gets measured against a specific reference, not some generic set of driving rules.
Advice stays relevant to the actual circuit, vehicle, and performance target in front of the driver.
02Automated telemetry comparison
Multiple data channels get processed at once, with the most important differences pulled to the top.
Coaches start from a prepared analysis instead of reviewing every graph from scratch.
03Track-section insights
Differences get tied back to specific corners or sections of the circuit.
Coaches can explain not just how much time was lost, but where — and what behavior caused it.
04Driving recommendations
Practical observations a driver can actually act on.
Brake later or more progressively, carry more corner speed, get on the throttle earlier, try a different gear, tighten the line, cut down steering corrections, prioritize exit speed.
05Engineering recommendations
Vehicle behavior worth a closer look on the setup side.
A faster jumping-off point for evaluating stability, traction, braking behavior, or balance through different corner types.
06Coach-supported decisions
AI analysis backs up professional judgment — it doesn’t replace it.
Coaches check the findings, add context, and adapt the advice to where the driver actually is.
06
Implementation approach
The project brought together telemetry processing, performance comparison, and AI-generated explanations in one workflow.
Implementation focused on:
Importing and validating racing telemetry
Synchronizing laps for meaningful comparison
Dividing the circuit into useful analysis sections
Detecting significant differences between laps
Distinguishing meaningful patterns from normal variation
Translating technical data into understandable advice
Separating driving observations from engineering recommendations
Presenting analysis coaches can review quickly
Keeping the coach in control of the final guidance
Traceability
Recommendations stay tied to the telemetry differences that produced them. A coach can always see why the system suggested something and decide for themselves whether it fits the customer.
Before the AI-assisted workflow
1Receive the customer’s lap file
2Import or open the telemetry
3Select a suitable reference
4Review the laps across multiple data channels
5Identify the sections with the largest differences
6Determine which differences are meaningful
7Interpret the likely driving or vehicle behavior
8Prepare understandable feedback
9Discuss the findings with the customer
After the AI-assisted workflow
1Upload the customer’s lap
2Select the reference lap
3Review prioritized differences and generated recommendations
4Add the coach’s own interpretation
5Deliver personalized advice to the customer
07
The result
The app shrinks the gap between getting a customer’s lap and having something useful to tell them.
Coaches and venue managers no longer start every analysis staring at a blank set of telemetry charts. The system prepares an initial comparison, flags the differences that matter, and lays out possible explanations and recommendations. In practice, visual lap analysis now takes about half the time it used to — and the write-up that reaches the driver carries far more detail than a coach could reasonably assemble by hand.
That frees the coach up for the part of the job that actually needs a human: checking the findings, understanding what the customer needs, and delivering advice that fits them.
With less routine analysis per driver, the business can support more customers in the same amount of time — without losing the expertise that made the coaching worth paying for in the first place.
The role of AI
AI works here as an analysis and communication layer inside a structured telemetry workflow — not a black box handing down verdicts. It doesn’t judge a lap out of context or pull advice from generic racing knowledge; recommendations come from measured differences between the uploaded lap and the reference. The coach stays responsible for reviewing the output and deciding what actually reaches the customer.
Business impact
✓
Visual lap analysis, twice as fast
Automated comparison roughly halves the manual review per lap — while surfacing more detail than before.
✓
More customers supported
Coaches and managers can prepare feedback for more drivers in the same amount of time.
✓
More consistent analysis
A repeatable process for spotting and presenting the lap differences that actually matter.
✓
Higher-value coaching time
More time spent interpreting results and talking with customers, less spent building the initial analysis.
✓
Accessible performance data
Technical telemetry turns into advice a customer can actually understand and act on.
✓
Combined driving & engineering insights
Driver behavior and setup considerations show up together in one workflow.
✓
Scalable coaching services
Data-backed coaching can grow without manual analysis time growing right alongside it.
→From raw telemetry to structured guidance
Complex racing data becomes prioritized driving and engineering observations.
→From manual review to assisted analysis
Coaches get a prepared comparison instead of analyzing every channel from scratch.
→From limited capacity to scalable coaching
Less routine analysis time means valuable feedback reaches more customers.
→From automated output to expert advice
AI supports the coach’s decisions while professional judgment still runs the show.
AI that makes experts faster — not absent
By combining telemetry analysis, reference-based comparison, and AI-generated recommendations, we built a tool that helps coaches go from raw racing data to personalized customer advice a lot faster than before.
Have a workflow where expert analysis is the bottleneck? We can scope an AI feature that fits into it — with your professionals staying firmly in control.