Vikas Rungta
Co-Founder & CEO
A company built to help mobility enterprises operationalize artificial intelligence across the entire automotive value chain, Ravity offers a verticalized, industry-specific platform that connects engineering, quality, after-sales, and monetization.
“We’re building one of the very few platforms designed to serve the entire mobility and automotive value chain, from engineering to after-sales to monetization”, says Vikas Rungta, Co- Founder & CEO, Ravity. “Our solution is founded on a clear premise: how can we use artificial intelligence and AI agents to operationalize complex, core tasks within the mobility industry, particularly in areas such as engineering and quality that have traditionally been highly manual and time-consuming”, Vikas adds.
Intelligence in Production Line
The automotive industry’s most valuable currency is trust. With electric vehicles that overheat, autonomous systems that disengage unexpectedly, or asset values that depreciate faster than expected, consumers hesitate to adopt new mobility technologies because reliability remains uncertain. Vikas believes this trust deficit as the fundamental constraint on growth.
By partnering with original equipment manufacturers (OEMs), the company enables real-time insights into vehicle health, usage, and long-term asset value. It allows automotive organizations to bring AI directly into core functions, automating the analysis of telemetry, engineering, and quality data to generate actionable insight.
Processes that previously required manual triage can now be handled through data-driven orchestration, bringing measurable consistency and speed.
Its Quality Intelligence engine helps manufacturers compress diagnostic and corrective cycles from the industry’s 150-day average to barely ten. What was once a long corridor of spreadsheets, manual testing, and delayed recalls now becomes a closed-loop process where engineers can act within days, not months. But solving for quality only stabilizes the present; the real advantage lies in anticipating the next fault. That’s where Predictive Maintenance comes in. By analysing streams of telemetry and usage data, a manufacturer can reach out to a customer weeks in advance, schedule preventive service, and avoid a warranty crisis altogether. The result is greater reliability for drivers and leaner cost structures for manufacturers.
Once reliability becomes predictable, the same intelligence turns into a strategic asset. Ravity calls this phase Data Monetisation, which is using the vehicle’s behavioural data to craft personalised service and warranty plans. Instead of generic offerings, an OEM can reward careful drivers with lower-priced extensions or customised service bundles. In the process, what used to be a post-sale cost centre evolves into a profit-aligned engagement model.
Built for Scale, Designed for Trust
Ravity’s model emphasises scalability without sacrificing specificity. Its architecture allows most functions to deploy quickly while leaving room for deep integration within each OEM’s environment. This design has drawn adoption from established manufacturers such as Maruti Suzuki and Volvo, and the company reports measurable gains in deployment time and system return.
However, technology alone doesn’t guarantee adoption. Automotive organisations are inherently conservative; AI adoption requires cultural change. Ravity addresses this through what Vikas calls a human-in-the-loop architecture. Every automation still accommodates expert oversight; engineers remain decision-makers, not spectators. This approach reassures teams that AI isn’t replacing judgment but amplifying it.
On the compliance front, the company encrypts and anonymises all privacy-linked data, enforces strict access control, and aligns with global automotive cybersecurity standards such as ISO/SAE 21434. OEMs, suppliers, and even end-users are made explicitly aware of what data is collected and how it is used. That governance model reinforces the same trust the company seeks to restore between driver and vehicle.
Ravity aims to convert data into decisions that cut costs, compress timelines, and sustain consumer confidence. Beyond commercial goals, the company is also working to democratize high-quality mobility data for research and entrepreneurship, generating datasets through its own fleets to fuel broader innovation. In the data-dense world of connected mobility, intelligence is no longer optional; it’s the measure of responsibility. Ravity’s approach suggests that technology’s moral opportunity lies not in the volume of information it collects, but in the clarity it creates.
Its Quality Intelligence engine helps manufacturers compress diagnostic and corrective cycles from the industry’s 150-day average to barely ten. What was once a long corridor of spreadsheets, manual testing, and delayed recalls now becomes a closed-loop process where engineers can act within days, not months. But solving for quality only stabilizes the present; the real advantage lies in anticipating the next fault. That’s where Predictive Maintenance comes in. By analysing streams of telemetry and usage data, a manufacturer can reach out to a customer weeks in advance, schedule preventive service, and avoid a warranty crisis altogether. The result is greater reliability for drivers and leaner cost structures for manufacturers.
Once reliability becomes predictable, the same intelligence turns into a strategic asset. Ravity calls this phase Data Monetisation, which is using the vehicle’s behavioural data to craft personalised service and warranty plans. Instead of generic offerings, an OEM can reward careful drivers with lower-priced extensions or customised service bundles. In the process, what used to be a post-sale cost centre evolves into a profit-aligned engagement model.
Built for Scale, Designed for Trust
Ravity’s model emphasises scalability without sacrificing specificity. Its architecture allows most functions to deploy quickly while leaving room for deep integration within each OEM’s environment. This design has drawn adoption from established manufacturers such as Maruti Suzuki and Volvo, and the company reports measurable gains in deployment time and system return.
By partnering with original equipment manufacturers (OEMs), the company enables real-time insights into vehicle health, usage, and long-term asset value
However, technology alone doesn’t guarantee adoption. Automotive organisations are inherently conservative; AI adoption requires cultural change. Ravity addresses this through what Vikas calls a human-in-the-loop architecture. Every automation still accommodates expert oversight; engineers remain decision-makers, not spectators. This approach reassures teams that AI isn’t replacing judgment but amplifying it.
On the compliance front, the company encrypts and anonymises all privacy-linked data, enforces strict access control, and aligns with global automotive cybersecurity standards such as ISO/SAE 21434. OEMs, suppliers, and even end-users are made explicitly aware of what data is collected and how it is used. That governance model reinforces the same trust the company seeks to restore between driver and vehicle.
Ravity aims to convert data into decisions that cut costs, compress timelines, and sustain consumer confidence. Beyond commercial goals, the company is also working to democratize high-quality mobility data for research and entrepreneurship, generating datasets through its own fleets to fuel broader innovation. In the data-dense world of connected mobility, intelligence is no longer optional; it’s the measure of responsibility. Ravity’s approach suggests that technology’s moral opportunity lies not in the volume of information it collects, but in the clarity it creates.
