ThirdAI Automation has raised $3 million in seed funding led by Endiya Partners and Capria Ventures to scale its causal AI-powered root cause analysis platform for semiconductor fabs.
Semiconductor manufacturing is entering an era where incremental efficiency gains carry outsized economic impact.
ThirdAI Automation has raised $3 million in seed funding in a round co-led by Endiya Partners and Capria Ventures, the company announced.
The Bengaluru-headquartered, India–US-based startup plans to use the capital to expand product development, grow its engineering teams in India and overseas, and accelerate deployments with semiconductor equipment manufacturers and fabrication plants.
Targeting a costly bottleneck in chip production
As chip architectures grow more complex to support AI and high-performance computing, manufacturing processes have become increasingly sensitive. Even minor tool malfunctions can disrupt production, lower yields, and trigger costly downtime.
Despite heavy investment in industrial analytics, root cause analysis (RCA) in semiconductor fabs remains largely manual. Engineers often spend 20 to 40 hours per incident examining equipment logs, sensor outputs, imaging data, and operational records across fragmented systems.
ThirdAI positions its platform as an automated RCA “copilot” powered by causal AI — a branch of artificial intelligence focused on identifying cause-and-effect relationships rather than simple correlations.
Instead of merely detecting anomalies, the system maps relationships between equipment behavior and failure events, aiming to shorten the path from detection to resolution.
Reported performance gains
According to company data from pilot and production deployments, ThirdAI’s platform has demonstrated:
- Up to 80% reduction in diagnostic time compared to manual RCA workflows
- Over 90% diagnostic accuracy versus expert-led manual methods
- 30–50% lower onboarding and deployment costs compared to custom analytics systems
These figures are based on pilot environments and customer deployments, according to the company.
In high-volume fabs, where downtime can cost hundreds of thousands of dollars per hour, even marginal improvements in yield and uptime translate into meaningful financial gains.
Causal AI as industrial infrastructure
Causal AI has emerged as a growing area within applied AI research, particularly in complex systems where prediction alone is insufficient.
Semiconductor manufacturing environments generate multimodal data — including machine logs, sensor feeds, imaging data, and service documentation. Extracting actionable insights from these sources requires systems capable of structured reasoning rather than simple statistical forecasting.
Dr. Vivek Vishwakarma, co-founder and CEO of ThirdAI, previously worked across semiconductor and battery domains at Intel and Roche. He said the platform is designed to surface equipment-level cause-and-effect relationships directly, reducing reliance on trial-and-error troubleshooting.
Co-founder Dr. Sainyam Galhotra, who holds a PhD from UMass Amherst and serves on the faculty at Cornell University, described the company’s long-term ambition as building “an operating system for industrial operations” capable of continuously reasoning across manufacturing workflows.
Alignment with India’s semiconductor ambitions
The fundraise comes amid renewed policy focus on domestic semiconductor capability under the India Semiconductor Mission (ISM). ISM 2.0 emphasizes development of equipment, materials, and intellectual property capabilities within India.
While ThirdAI operates globally, its technology development aligns with India’s broader push to build deep-tech capacity in semiconductor tooling and analytics.
Venture investors appear to view the opportunity as both locally strategic and globally scalable.
Raghav Gupta, vice president at Endiya Partners, said the company is addressing a persistent pain point in fabs where RCA remains time-intensive and manual. Surya Mantha, managing partner at Capria Ventures, added that the technology could extend beyond semiconductors to other “always-on” industrial systems such as utilities and chemical processing plants.
Broader industry implications
As AI accelerates demand for advanced chips, fabs face pressure to maximize yields while managing tighter process tolerances. Automation of diagnostic workflows may become critical infrastructure rather than optional tooling.
Industrial AI, particularly in capital-intensive environments, is increasingly focused on operational resilience rather than predictive dashboards alone.
If causal AI platforms can reliably shorten downtime cycles and improve yield consistency, they may shift how fabs manage maintenance, workforce allocation, and incident response.
For ThirdAI, the immediate challenge will be scaling deployments within conservative, production-critical environments where validation standards are stringent.
The longer-term opportunity lies in embedding AI-driven reasoning into the operational backbone of advanced manufacturing.
In an industry where minutes matter and margins hinge on yield precision, the value of faster, more accurate root cause analysis is structural — not incremental.
