About Ekai
Ekai is an enterprise data platform that writes semantic models and data transformation code for AI tools, starting from definitions set by domain experts. Every generated artifact is validated against warehouse data before it ships. The company raised $1.7 million in pre-seed funding in September 2026, led by Misneach.
What is Ekai?
What is Ekai?
Ekai is an enterprise data startup that builds the business context AI agents need before they can be trusted with corporate data. Its platform writes the semantic models and data transformation code that AI tools rely on to read a company's data warehouse correctly.
The company calls its approach forward-engineering. Instead of inferring meaning from existing BI dashboards, query history or dbt projects, Ekai starts with the domain experts who run the business. They define what a metric or term means, such as "active user", and Ekai treats those definitions as ground truth. It then converts them into machine-readable logic and validation rules, and nothing generated ships until it has been checked against the warehouse data. Each definition is owned by a named person, giving teams a clear record of who signed off.
Ekai says that in early engagements, semantic modeling work that historically took teams three to six months was completed in as little as six hours. Its workflows are available on Snowflake, including the Snowflake Marketplace, with support for Databricks, BigQuery, Amazon Redshift, Azure Synapse, Postgres, ClickHouse and DuckDB.
The founders, CEO Moatassim Aidrus, Chief AI Officer Hussnain Ahmed and Tero Miikki, each spent more than two decades in leadership roles at Accenture, Microsoft and UPM-Kymmene.
Pros
- Starts from expert-defined business terms rather than guessing meaning from old dashboards
- Validates generated semantic models against warehouse data before anything ships
- Available on Snowflake Marketplace with support for Databricks, BigQuery, Redshift and more
- Assigns named ownership to each business definition for accountability
Cons
- Very early stage company with only a pre-seed round raised
- Speed claims such as six-hour modeling are company-reported from early engagements
- Requires domain experts to invest time defining metrics up front
How to Use Ekai
- 1Connect your warehouse
Link Ekai to a supported data platform such as Snowflake, Databricks or BigQuery.
- 2Capture expert definitions
Have domain experts define key metrics and business terms that Ekai treats as ground truth.
- 3Generate models and code
Let Ekai convert the definitions into semantic models, transformation code and validation rules.
- 4Validate and deploy
Review the checks run against warehouse data, then expose the verified models to your AI tools.