Over the past few weeks, I’ve been experimenting with a question that kept coming to mind while watching Formula 1:

Could a local AI system act like a race engineer and make real-time strategy calls from live telemetry?

That idea eventually turned into a fully local AI pit-wall prototype capable of:

Streaming telemetry at 10Hz

Predicting tire cliff drop-offs one lap early

Retrieving historical race knowledge using RAG

Generating radio-style strategy calls with a local LLM

Producing automated post-race PDF reports

And the entire stack runs offline.

The Goal

Most AI systems today rely heavily on cloud inference.

I wanted to explore something different:

low-latency

local inference

explainable predictions

real-time telemetry processing

The project combines sequential ML prediction with retrieval-augmented contextual reasoning in a motorsport-inspired environment.

System Architecture

The pipeline is divided into five major components:

Telemetry Ingestion Layer

Telemetry data is streamed…