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Beacon By KPIT
Mobility Intelligence Platform
adopted by OEMs to reimagine
SDLC
Adopt what is trusted and raved by OEMs globally!
Adopt what is trusted
and raved by OEMs globally!
What’s Beacon?
KPIT has put in its 25 years of Mobility Expertise, 2000+ production programs experience, Best Practices, ASPICE, Multiple automotive standards, abstratction of 25+ OEMs context >> You build a CONTEXT ENGINE thats unparellelled. This forms the base of KPIT Mobility Intelligence platform - Beacon.
Beacon is a robust mobility intelligence platform that is custom developed for Mobility OEMs. Decades only SDLC has been reimagined with 7 AI agents ( and more in making) that bring dramatic gains to what your current devlopment practices
18+
months in the making
02nd
generation Mobility AI platform & keeps getting better everyday
Why Beacon is Loved by OEMs
30%
Faster software deployments
20%
Savings in Vehicle Software Costs
30%
Improvement in Reliability via Rapid Bug Fixes and Triaging
Beacon in Action
Your Digital Cockpit Revolutionalised
Plugins that natively turbocharge your Dev Environment
AI platform that can power the entire Mobility Tech Stack
Myth Busters about KPITs Mobility Intelligence Platform
1: Mobility OEM data needs to be highly structured to even start using the platform
Mobility engineering data is rarely neat, and it doesn’t need to be. Real-world programs run on a mix of specifications, documents, code, test logs, and tool data created over years. The platform is designed to work with data as it exists today, not as an idealized future state.
2: OEM data will be used for training AI models
OEM data remains OEM’s data, full stop. It is used only within the customer’s environment to deliver outcomes for that specific program. It is not reused, shared, or fed back into any external training systems.
3: Engineers must be experts in prompt engineering to get results
Engineers don’t need to learn a new set of prompts to further optimize their work. The platform understands engineering context, intent, and standards; so engineers work the way they always have, while AI adapts to them, not the other way around.
4: Engineers must step out of their development tools to use AI
Productivity drops the moment engineers are forced to jump between systems. While KPIT AI platform runs in the background, we have developed plugin for development environments. So engineers can choose to work in their own development environments or on the KPIT AI platform interface based on use case and convenience.
5: The platform is limited to a few mobility domains
Modern vehicles are systems of systems. The platform is built to span multiple mobility domains and their interactions, because real engineering challenges don’t exist in silos.
6: AI cannot be trusted in high safetycritical production programs
AI becomes risky only when it behaves unpredictably. In safetycritical environments, execution must be deterministic, auditable, and reviewable. KPIT AI platform is designed with these principles, it becomes a reliability layer - not a liability.
7: AI is fine for POCs, but not for production programs
POCs prove possibility. Production demands discipline. The difference isn’t AI itself, it’s how deeply AI is embedded into governed processes, quality checks, and human oversight. That’s what allows KPITs AI platform to move from demos to deployment.
8: Integrating AI with existing OEM engineering tools isn’t practical
KPITs AI platform delivers value and easily connects to the systems engineers already trust. Integration with existing toolchains isn’t optional - it’s foundational principle.
