ThirdAI Raises $3M to Fix a Blind Spot in Semiconductor Manufacturing

ThirdAI Automation has raised $3 million in seed funding in a round co-led by Endiya Partners and Capria Ventures, as it looks to scale its causal AI platform for semiconductor manufacturing.

The India–US-based company plans to use the capital to advance product development, expand teams in India and overseas, and accelerate deployments with equipment manufacturers and semiconductor fabs.

Founded in 2024 by Dr Vivek Vishwakarma and Dr Sainyam Galhotra, ThirdAI is focused on a persistent problem inside fabs. When equipment fails, engineers often have the data but lack the clarity to understand why it failed quickly.

When Data Exists, But Answers Don’t

As chip complexity increases, semiconductor fabs are facing a paradox. Despite heavy investment in industrial analytics, root cause analysis (RCA) remains largely manual. Engineers still spend 20 to 40 hours per incident stitching together logs, sensor readings, images, and operational data across multiple systems.

Most existing tools stop at prediction or anomaly detection. They flag that something is wrong but offer limited help when a tool actually goes down.

ThirdAI’s approach centres on causal AI, which focuses on identifying cause-and-effect relationships rather than correlations. Its RCA copilot is designed to guide engineers from failure to resolution by reasoning across equipment logs, sensor data, images, and operational records.

In pilot and production environments, the company reports:

• Up to 80% reduction in diagnostic time

• Over 90% diagnostic accuracy compared to expert-led manual RCA

• 30–50% lower onboarding and deployment costs versus custom analytics setups

Moving RCA Closer to the Equipment Layer

According to Dr Vivek Vishwakarma, co-founder & CEO of ThirdAI, the goal is not to replace engineers but to reduce guesswork during downtime.

“We’re excited about the support from our investors as we scale ThirdAI. In a modern semiconductor fab, downtime is often triggered by issues that engineers already have the data for, but not the clarity to diagnose quickly. ThirdAI is built to surface cause-and-effect relationships directly at the equipment layer so that teams can act with confidence rather than rely on trial and error.”

The platform is being positioned for production-critical environments where delays directly affect yield and output.

Beyond Semiconductors

While semiconductor manufacturing is the immediate focus, ThirdAI sees broader applications for its causal AI approach.

Dr Sainyam Galhotra, co-founder of ThirdAI, said the company’s long-term aim is to build a reasoning layer for industrial systems.

“This round will provide the momentum required to take our technology deeper into real fab environments. Our long-term vision is to build an operating system for industrial operations, where AI continuously reasons over complex, multimodal data to guide decisions across the manufacturing lifecycle.”

Investors point to RCA as one of the most expensive blind spots in advanced manufacturing.

Raghav Gupta, Vice President, Endiya Partners, noted that downtime and process issues remain unresolved despite years of analytics adoption.

“ThirdAI is tackling a real problem in semiconductor manufacturing, where equipment downtime and process issues persist. Root cause analysis remains largely manual and time-intensive. Their approach has the potential to change that.”

Surya Mantha, Managing Partner, Capria Ventures, highlighted the wider relevance of the technology.

“ThirdAI tackles one of the industry’s costliest bottlenecks, turning complex equipment data into clear, actionable causal insights. Their approach can significantly improve uptime and yield in high-volume, production-critical fabs.”

ThirdAI’s work also aligns with the goals of the India Semiconductor Mission (ISM), particularly ISM 2.0, which emphasises building capabilities in equipment, materials, and Indian intellectual property.

As India looks to strengthen its semiconductor ecosystem, tools that reduce downtime and improve yield could play a role in making fabs more resilient and operationally efficient.