Enterprise data startup Ekai Inc. today announced $1.7 million in new funding for software that builds the business context artificial intelligence agents need before they can be trusted with corporate data.
Ekai provides a platform that writes the semantic models and data transformation code AI tools rely on to read a company’s data warehouse correctly. Its starting point is the people who run the business. Domain experts define what a metric or term means, and Ekai treats those definitions as ground truth before converting them into machine-readable logic and validation rules. Nothing generated ships until it has been checked against the warehouse data, according to the company.
Starting with the experts is what the company calls forward-engineering. Much of the market, Ekai argues, works backward by inferring meaning from the business intelligence tools and queries a company already runs.
“Reverse-engineering from existing BI dashboards, query history, your dbt project, is asking the exhaust pipe what the engine was thinking,” said co-founder and Chief Executive Moatassim “Mo” Aidrus. “This is useful for documenting what you have, but it does nothing for what you need next.”
Co-founder and Chief AI Officer Hussnain Ahmed said the problem starts with the models. Foundation models know a term such as “active user” but have no idea what it means inside a particular company or where it lives in its data warehouse. Under Ekai’s approach, a definition like that is captured from the people who define the business and “owned by someone with their name on it.”
Ekai said that in early engagements, semantic modeling work that has historically taken teams three to six months was completed in as little as six hours. The company credits verification for the speed. When experts settle definitions up front, nobody has to guess what the data means, correct the guess and try again.
The context engineering conversation now running through AI infrastructure is mostly about how agents manage memory, retrieval and prompts in real time, and Ekai keeps some distance from it. The company says its work sits a layer below, on whether the business meaning an agent reasons over was ever verified in the first place and who signed off on each definition.
Boston early-stage investor Misneach led the pre-seed round, with participation from Cambridge AI venture fund and studio C10 Labs. Ekai said the money will accelerate product development and go-to-market operations and deepen its platform integrations.
Mark Coffey, co-founder and managing partner at Misneach, said Aidrus, Ahmed and third co-founder Tero Miikki each spent more than two decades in leadership roles at Accenture plc, Microsoft Corp. and UPM-Kymmene Oyj. That work put them in the room with chief technology and data officers trying to get AI into production. The founders “watched it go unsolved from the other side of the table,” Coffey said.
Ekai’s workflows are available now on Snowflake Inc.’s platform, including the Snowflake Marketplace. Support extends to Databricks Inc., Google LLC’s BigQuery, Amazon Redshift, Microsoft Azure Synapse, Postgres, ClickHouse and DuckDB.
