ThirdAI seed funding for semiconductor AI platform

A causal AI platform targeting semiconductor manufacturing secures early capital to scale deployments, reduce diagnostic time and automate root cause analysis in production-critical environments facing rising complexity.

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DQC Bureau
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ThirdAI seed funding for semiconductor AI platform

The ThirdAI seed funding for semiconductor AI marks an early milestone for the India–US based startup focused on automating root cause analysis in semiconductor manufacturing. The company has raised $3 million in a seed round co-led by Endiya Partners and Capria Ventures.

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The capital will be used to scale product development, expand teams in India and overseas and accelerate deployments with equipment manufacturers and semiconductor fabrication plants.

Addressing a manufacturing bottleneck

Founded in 2024 by Dr Vivek Vishwakarma and Dr Sainyam Galhotra, ThirdAI develops an AI-powered root cause analysis and troubleshooting platform for semiconductor manufacturing environments.

As chip complexity increases and fabs approach physical process limits, yield pressures have intensified. Despite investments in industrial analytics, many existing solutions focus on prediction or anomaly detection. When equipment fails, engineers often rely on manual root cause analysis, spending between 20 and 40 hours per incident reviewing data across multiple systems.

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ThirdAI positions its causal AI technology as a response to this gap.

Causal AI for faster resolution

The platform leverages a causal AI-powered RCA copilot that identifies cause-and-effect relationships across equipment logs, sensor data, images and operational records. The goal is to move engineers from failure detection to resolution with greater clarity and less reliance on manual trial and error.

In pilot and production environments, the company reports:

  • Up to 80 percent reduction in diagnostic time compared to manual RCA workflows

  • Over 90 percent diagnostic accuracy versus expert-led manual methods

  • 30 to 50 percent lower onboarding and deployment costs compared to custom analytics solutions

These results are based on pilot deployments and customer environments.

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Alignment with India’s semiconductor ambitions

ThirdAI’s work aligns with the objectives of the India Semiconductor Mission, including ISM 2.0, which focuses on building capabilities in equipment, materials and Indian intellectual property.

Dr Vivek Vishwakarma, Co-Founder and CEO, ThirdAI, said downtime in modern fabs is often triggered by issues where data exists but diagnostic clarity is lacking. He stated that the platform is designed to surface cause-and-effect relationships at the equipment layer, enabling teams to act based on structured insights rather than iterative troubleshooting.

Dr Sainyam Galhotra, Co-founder, said the funding round will help the company deepen deployments in real fabrication environments. He added that the long-term objective is to build an operating system for industrial operations, where AI continuously reasons over complex, multimodal datasets to support decision-making across the manufacturing lifecycle.

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Investor perspective

Raghav Gupta, Vice President at Endiya, noted that equipment downtime and process issues remain persistent challenges in semiconductor manufacturing. He said root cause analysis is still largely manual and time-intensive, and described ThirdAI’s approach as having the potential to change that dynamic.

Surya Mantha, Managing Partner at Capria Ventures, stated that the platform addresses a critical cost bottleneck by converting complex equipment data into structured causal insights. He added that while the immediate focus is semiconductor manufacturing, similar challenges exist in other always-on industrial systems such as power generation, utilities and chemical processing.

Scaling the platform

With this funding, ThirdAI will focus on expanding its team, strengthening product capabilities and increasing deployment across semiconductor fabs and equipment manufacturers. The emphasis remains on embedding causal AI directly into production environments to reduce downtime and improve yield in high-volume, production-critical operations.

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