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Nov 29, 2025
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Ekai Slashes Enterprise Data Modeling From Months to Hours
NewDecoded
3 min read
Enterprise AI has a dirty secret: most companies hit a wall when trying to apply AI tools to business-specific tasks. Ekai, featured in Snowflake's latest Startup Spotlight, tackles this head-on with a platform that reduces data product development from three to six months down to just three to four hours. The startup's "business data lab" automates what has traditionally been a painfully manual process of contextualizing enterprise data for AI systems.
The problem isn't the AI models themselves, according to co-founders Mo Aidrus (CEO) and Hussnain Ahmed (Chief AI Officer). It's the complete absence of structured, machine-readable context about how enterprises actually operate. Ekai's platform connects directly to data warehouses, automatically generates enterprise mind maps and entity-relationship diagrams, then produces all the artifacts downstream AI applications need: data catalogs, business glossaries, metrics definitions, lineage maps, and validation rules.
What sets Ekai apart is its conversational AI agent that assists business analysts in defining semantic models without extensive IT involvement. The platform learns patterns from existing SQL code, catalogs business definitions at scale, and enables natural language data discovery. This democratization means business users can prototype and test models independently before engaging IT teams for production deployment.
Ekai's strategic use of the Snowflake Native App Framework solves two critical adoption barriers simultaneously. First, all data and metadata remain within the customer's security perimeter since execution happens entirely within their Snowflake account. Second, it positions Ekai as a native extension rather than another external tool requiring complex integration work.
The founding team brings over 20 years of combined experience in data engineering, agentic AI, ML Ops, and enterprise cloud infrastructure. Their vision centers on a market shift where automated context generation transitions from competitive advantage to absolute necessity as AI begins leading analytics workflows.
Ekai is currently available through Snowflake's private listing. Interested enterprises can reach out directly through the company's contact page to get started with the platform.
Ekai's emergence signals a maturing enterprise AI market where the bottleneck has shifted from model capability to data readiness. While vendors like Databricks, Collibra, and Alation have built data governance empires, Ekai bets that AI-native automation will disrupt traditional catalog approaches that often sit underutilized despite hefty price tags. The 99% time reduction claim (months to hours) mirrors broader trends in AI-accelerated development but specifically targets the unglamorous yet critical work of semantic modeling. By embedding within Snowflake's ecosystem rather than building standalone infrastructure, Ekai follows the playbook of successful data tooling startups: reduce deployment friction, leverage existing security models, and tap into established procurement channels. The real test will be whether enterprises trust AI-generated context for production systems or still demand human oversight that erodes the speed advantage.
