NetApp Excellerator Cohort 14 Demo Day showcases AI startups transforming enterprises

Bengaluru recently played host to the 14th cohort of NetApp Excellerator programme, a platform that has spent the past decade nurturing startups at the intersection of AI, data, and cloud infrastructure. The Demo Day brought together founders, engineers, enterprises, and investors to witness the culmination of months of technical mentoring, GTM coaching, and proof-of-concept engagements. Over the years, NetApp Excellerator has supported 90 startups, helping them raise over $600 million, with one-third led by women entrepreneurs.

The event kicked off with a welcome note by Vasanthi Ramesh, Vice President & Engineering Site Leader, NetApp; followed by a keynote from Prof Rupesh Nasre of the Indian Institute of Technology Madras, who set the tone with reflections on AI, innovation, and scalability. He encouraged innovators to ask three foundational questions, “Is it solvable? Can it be automated? Can it scale by orders of magnitude?” These questions, he emphasized, are critical for both AI research as well as for building real-world systems with impact.

“We have consistently led advancements in technology, establishing ourselves as one of the world’s most secure storage providers while delivering intelligent data infrastructure for all workloads across hybrid clouds,” Vasanthi said.

Startup highlights

Startup pitches formed the core of the day, spotlighting five companies from the cohort: Filo Systems, Sentra, Synthefy, TrueFoundry, and Genloop.ai.

Filo Systems demonstrated lossless compression for enterprise-scale data, tackling storage, energy, and data transfer costs. Their platform, deployable in under an hour, compresses streaming, replication, and file storage automatically. Notable results include Intel shrinking 50 PB to 6 PB (saving $4.5M/year) and a national CCTV authority reducing 100 PB to 5 PB while remaining compliant.

Sentra’s mentor, Venkat presented a unified, cloud-scale data security platform designed for enterprises overwhelmed by fragmented tools and exploding data volumes. The solution discovers and catalogs all enterprise data, monitors real-time access, prioritizes risks, and ensures compliance, providing a single, unified view of cloud and on-prem structured and unstructured data.

Synthefy introduced a foundational multimodal AI model for time-series data, enabling enterprises to generate forecasts, simulations, and predictive insights from complex datasets. Early implementations include 90% precision in telecom failure prediction, preventing 40M hours of downtime, and a 12% revenue uplift for a major retailer. Its platform models numerical, tabular, and text data jointly, outperforming off-the-shelf solutions.

TrueFoundry enables organizations to go beyond retrospective reporting — delivering a unified platform to deploy, govern, and scale AI and ML applications, including autonomous “agentic” workflows. Its cloud-agnostic AI Gateway supports LLMs, embeddings, agent orchestration, tool integrations and compliance guardrails, allowing enterprises to deploy production-grade AI with security, scalability, and governance. TrueFoundry helps teams move from isolated experiments to real-time, production-ready AI systems.

Some of the key use cases include internal automation agents, multi-step reasoning workflows, RAG/agent pipelines, and AI-driven infrastructure optimization, not just predictive analytics.

Genloop unveiled the world’s first enterprise-grade self-learning data analyst, enabling business users to interact with their data conversationally. The platform auto-onboards enterprise datasets, understands business context, investigates causes, and supports secure, low-cost deployments, transforming decision-making from dashboards to instant, actionable insights.

Turning AI ideas into scalable products

Another highlight was a panel discussion, “The role of AI: From conceptualization to product development”, moderated by Vasanthi Ramesh. The panelists, Naveen Chand, Vice President, Product Management, NetApp; Chris Johnson, Senior Director, Product Management, NetApp; Prof Nasre; and Sajeev Aravindan, Co-founder and CTO, Vaultedge; shared insights for founders and technologists alike.

Key takeaways included:

• AI is an ingredient, not the product: AI enables solutions; it does not replace the need for high-quality data, customer-centric design, or scalable systems.
• Data quality and workflow integration matter more than models alone.
• Customer experience must be prioritized from day one: focus on delighting the target segment, making infrastructure invisible, and designing security and compliance into the product.
• Early market validation is crucial: work closely with a first customer, then scale gradually.
• Governance, compliance, and trust are foundational as AI becomes autonomous.
• Motivation evolves with milestones: from first customer validation to building teams and new products.

“The core of AI is data - its quality, completeness, and lack of bias. A model can’t fix poor data; it only amplifies the problems,” Chand noted. Aravindan emphasized the three Ps – product, performance, and production viability – as the markers of a viable AI product. Prof Nasre highlighted that innovation must consider decidability, automation, and scalability, grounding AI development in practicality and ethics.

The future of AI-driven startups

NetApp’s Demo Day underscored a broader lesson: success in AI-driven products goes beyond technology. Startups must navigate market needs, operational scalability, and customer trust. The 14th cohort exemplified how AI, cloud, and data can converge to create impactful, enterprise-ready solutions – from optimizing storage and energy costs to enabling predictive analytics and secure data governance.

As AI continues to reshape industries, platforms like NetApp Excellerator are proving vital in bridging research, entrepreneurship, and enterprise adoption, nurturing startups that are not just innovative but ready to scale globally.