raceai-technology-page
Built for the Race Car. Connected Through the Cloud. Designed for Intelligence.
RaceAI combines onboard hardware, cellular connectivity, cloud services, live media, motorsports data, and AI into three purpose-built products: Radio, LIVE, and Studio.
Communication. Video. Intelligence.
Digital Team Communication
RaceAI Radio replaces conventional RF distance dependence with a cloud-connected communication architecture built around the race car, cellular connectivity, and team smartphones.
- In-car digital audio
- Driver PTT integration
- 5G cellular transport
- Cloud-connected team channels
- Recording and transcription
Real-Time In-Car Video
RaceAI LIVE captures the car's SDI video, processes it onboard, and streams it over cellular connectivity so approved viewers can watch while the car is still on track.
- HD SDI video ingest
- Onboard video processing
- Hardware-accelerated encoding
- Real-time cloud streaming
- Cloud video recording
Motorsports Intelligence
RaceAI Studio combines normalized racing data, specialized analysis tools, context assembly, AI Agents, evidence, and multiple LLM providers.
- Telemetry normalization
- Motorsports analysis tools
- AI Agent workflows
- Multi-LLM architecture
- Evidence-based answers
From the Driver's Helmet to a Team Member's Smartphone.
Traditional race radios depend on RF propagation between physical radios. Buildings, terrain, track layout, distance, and race-car noise all become part of the communications problem.
RaceAI takes a different approach. The driver still uses a familiar push-to-talk workflow, but the communication becomes digital and travels through cellular and internet infrastructure.
Why the architecture matters
- Communication is no longer tied to pit-to-car RF range
- Remote engineers and coaches can participate
- One smartphone can work across authorized cars and channels
- Digital audio creates opportunities for signal processing
- Conversations can be recorded, transcribed, and retained
Capture, Encode, Stream, and Preserve the In-Car View.
Designed for a moving race car
- HD SDI input from supported AIM SmartyCam systems
- Onboard processing rather than sending raw video
- Hardware-assisted encoding to reduce CPU load
- 5G cellular connection for track-to-cloud transport
- Live viewing on authorized smartphones and tablets
Sending live motorsports video is fundamentally different from uploading a recording after a session. The onboard system has to ingest the camera signal, encode it efficiently, transport it across a cellular connection, deliver it to remote viewers, and preserve the recording.
RaceAI LIVE is engineered around that entire path. The goal is low-friction real-time visibility without changing the driver's normal workflow.
RaceAI's current LIVE design targets 1280×720 at 30 fps for real-time delivery, with the onboard video architecture supporting the higher-resolution source from the camera.
Purpose-Built Onboard Computing Connects the Race Car to RaceAI.
Compute
Compact x86 onboard computing runs RaceAI services for communications, video, connectivity, device management, and local control.
Interfaces
Ethernet, USB, digital GPIO, audio, SDI video, and other interfaces connect RaceAI to the driver's controls and supported race-car systems.
Connectivity
An external cellular gateway provides the network path used by Radio and LIVE, separating communications connectivity from the core onboard compute platform.
RaceAI Studio Is More Than an LLM Interface.
The language model is one reasoning component. RaceAI builds the motorsports intelligence system around it.
The Model Should Not Have to Invent the Analysis.
Raw telemetry can contain enormous amounts of time-series data. Simply placing that data into an LLM prompt is inefficient and does not create a reliable race-engineering workflow.
RaceAI uses deterministic motorsports tools to calculate, filter, compare, summarize, and structure the relevant evidence first. The AI can then reason over results that have meaning in the racing domain.
Examples of tool-generated evidence
- Lap and sector comparisons
- Braking and throttle traces
- Speed and acceleration differences
- Friction-circle and G-force behavior
- Wheel-slip analysis
- Damper behavior and velocity distributions
- Setup and session comparisons
Some Racing Questions Require More Than One Prompt.
AI Investigate is RaceAI's agent-based approach for difficult questions that may span many laps, sessions, drivers, cars, events, or seasons.
1. Plan
The investigation determines what evidence and comparisons are needed.
2. Execute
RaceAI tools retrieve and calculate the required motorsports evidence.
3. Evaluate
The system tests findings, compares alternatives, and follows new leads where appropriate.
4. Explain
The result is assembled into a useful answer grounded in the evidence collected.
From One Corner on One Lap to an Entire Season.
Focused
Analyze a selected lap, braking zone, corner, trace, or driver input with detailed context.
Session & Event
Compare laps, setup changes, drivers, conditions, and evidence across a test or race weekend.
Historical
AI Investigate can work across larger histories to look for patterns that are difficult to see in individual sessions.
RaceAI Is Not Locked to One AI Provider.
Different models have different strengths, costs, latency, and capabilities. RaceAI's architecture separates the motorsports intelligence layer from the underlying LLM provider.
That allows RaceAI to support multiple models and continue evolving as AI technology changes, without rebuilding the motorsports platform around each new model.
Currently supported model families
- OpenAI
- Anthropic Claude
- Google Gemini
- xAI Grok
Model availability and specific model versions can change as providers evolve.
Fast Enough to Be Useful Means Doing Less Work Twice.
Caching
Reusable analysis and prepared context can be retained so repeated questions do not require every calculation to start over.
Parallel Execution
Independent analysis operations can run concurrently where appropriate, reducing investigation time.
Targeted Context
RaceAI selects the relevant evidence rather than sending an entire racing database to an LLM for every question.
When Products Are Connected, Their Evidence Can Become More Valuable.
Conversation Evidence
What the driver, engineer, coach, and crew said—and when they said it.
Visual Evidence
What was happening in and around the race car at that point in the session.
Performance Evidence
What the telemetry, setup, history, notes, conditions, and analysis show.
One RaceAI Platform. Shared Race-Weekend Context.
The value is not simply storing more data. It is preserving the relationships between the evidence so a team can understand what happened, what changed, what was communicated, and what to investigate next.
Help Race Engineers. Don't Replace Them.
RaceAI is designed to make racing knowledge easier to access, preserve, compare, and apply. It does not pretend that an AI model replaces the judgment of an experienced engineer, coach, crew chief, or driver.
The platform is built to give those people better evidence and better tools—and to make capabilities that are normally available only to well-funded teams more accessible to club and amateur racers.
Data-acquisition agnostic
RaceAI Studio is intended to work with the data-acquisition systems teams already use rather than forcing a proprietary logger or telemetry ecosystem.
Your existing systems collect the data. RaceAI helps turn that evidence into understanding.
Technology Built Around the Problems Race Teams Actually Have.
Clearer communication. Live visibility. Deeper understanding. Each RaceAI product stands on its own—and the technology becomes even more useful when the products share the same racing context.