Ganesh Subramanian

Ganesh Subramanian

Product Lead - Cogentiq

Fractal

Ganesh Subramanian leads product management for Cogentiq, Fractal’s Agentic AI platform designed to deliver autonomous business insights and actions. Over his 15-year journey at Fractal, Ganesh has transitioned from an analyst in the CPG domain—where he developed solutions in BI, advanced analytics, and solutions like Business 360—to focusing on building AI-powered products for business decision-making.
Previously, he led product management for Crux Intelligence, a Fractal-built decision intelligence product that pioneered natural language Q&A, anomaly and pattern alerts, and one-click root cause analysis—well ahead of the GenAI wave. Ganesh holds a Master’s in Management Science and Operational Research from Warwick Business School, UK.

Many business users have valuable expertise but are often unable to directly contribute to building AI solutions because of technical barriers.
 
Cogentiq by Fractal, a no-code platform, addresses this by enabling professionals without coding backgrounds to design, build, and deploy agentic AI solutions.
 
In this Hack Session, we’ll demonstrate how Fractal’s agentic AI platform - Cogentiq makes it possible to transform business knowledge into working AI agents. The session will include a hands-on walkthrough of building an agent and showcase examples of how organizations can apply no-code agentic AI to solve practical problems.
 
By the end, you will understand how business users can move from expertise to execution, making agentic AI adoption faster and more accessible.
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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More
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