PitLogic.ai
Team Soutar Motorsport McLaren Artura GT4 at Hidden Valley
CASE STUDY · DEVELOPMENT PROGRAMME

Team Soutar Motorsport
McLaren Artura GT4

Building an AI Race Intelligence System around the pressure, pace and trust requirements of a real GT4 race weekend.

Team Soutar Motorsport
Leader
ROUND 4 · HIDDEN VALLEY RACEWAY

The development environment is the race weekend.

The source material defines the system as a Microsoft NOVA programme delivered by MrPC Advanced IT Solutions, using Azure AI Foundry and Copilot+ PC around Team Soutar Motorsport's McLaren Artura GT4. The design target is a ranked, cited Top-3 within roughly four minutes, with the engineer retaining approval over every output.

ProgrammeMicrosoft NOVA
Delivered byMrPC Advanced IT Solutions
PlatformAzure AI Foundry · Copilot+ PC
CarMcLaren Artura GT4
Team Soutar Motorsport driver and McLaren Artura GT4
WHY THIS MATTERS

Race engineering is a time problem.

The car already logs more data than a small crew can fully read in the minutes between Practice and Qualifying. PitLogic.ai is being designed to compress the analysis problem: convert data to facts, combine it with the driver's words, then rank the few actions worth considering.

411 chat up to 1 kHz in the design brief
2 cars · 4 driverssupported by a lean engineering crew
THE SYSTEM TARGET

Fast, cited and bounded

~4 minFlag to ranked Top-3
Top 3Actions per driver, never noise
100%AI claims cited to a fact
0Car actions without engineer
REAL-WORLD RACE CONTENT

Round 4 · Hidden Valley Raceway

The gallery below uses the race-weekend content supplied with the project. It shows the Team Soutar environment that the platform is being designed around.

TSM McLaren at Hidden Valley RacewayTeam Soutar Motorsport driverTeam Soutar Motorsport pit laneMcLaren Artura GT4 on circuitTSM transporter powered by MrPC Leader Microsoft and Copilot+ PCTeam Soutar Motorsport on the grid
CASE STUDY STATUS

Development and field validation, not invented results

The supplied design document is forward-looking and explicitly describes intended system behaviour rather than measured performance outcomes. This site keeps that distinction: it presents the design, technology, race environment and evaluation programme without claiming unverified lap-time gains.

Solution developmentArchitecture, deterministic fact pipeline, corner model, retrieval index and web app.
Initial productionFirst live deployment, Top-3 between-session view, driver/engineer briefs and voice capture.
Production completion & tuningFeature completion, calibration, accuracy/performance tuning and QA.
Monitoring & fine-tuningLive evaluation, refinement and case-study delivery.
TECHNICAL REFERENCE

Read the AI Race Intelligence design document

Architecture, model roles, ingestion, governance, security and the human-in-the-loop trust model.

Open PDF