Amir SohailCAMBRIDGE, Mass., Sept. 23, 2026 (ZM NEWSWIRE) — Ekai, a platform that turns your analytical data and expert knowledge into business context for AI, today announced a $1.7 million pre-seed funding round led by Misneach with participation from C10 Labs. The funding will accelerate product development, scale its go-to-market operations and deepen platform integrations.
The round arrives as enterprises converge on a single diagnosis for why AI keeps failing inside their businesses: the models aren’t the problem, the missing business context is. Analysts, vendors, Anthropic, and Gartner have all said versions of it this year. What almost no one agrees on is how to fix it, and Ekai says most of the industry is doing it backward, inferring what a company’s data means from dashboards, your query history, your dbt project, rather than asking the people who actually know. Query history is useful for documenting what you have, but it does nothing for what you need.
“Reverse-engineering from existing BI dashboards, query history, your dbt project, is asking the exhaust pipe what the engine was thinking,” said Moatassim (Mo) Aidrus, co-founder and CEO of Ekai. “This is useful for documenting what you have, but it does nothing for what you need next.”
Ekai’s product is built around a sequence it calls forward-engineering: go to the domain experts who actually know what the data means, treat what they say as ground truth, and only then translates it into machine-readable business logic, transformation code, & validation rules, reconciling every generated artifact against the warehouse before it’s allowed to ship. Ekai is not another developer tool but an AI augmentation layer where human experts drive the semantic model and AI handles the speed, scale, and mechanical translation into production infrastructure.
Ekai frames this as a rejection of the industry’s growing comfort with unverified AI output: automation without an accountable human checking the results isn’t a feature, it’s the risk the industry keeps rediscovering the hard way.
“Foundation models were trained on the public internet, not on your enterprise data. They know the term active user; they have no idea what it means in your company or where it lives in your data warehouse. No model ships with that knowledge” said Hussnain Ahmed, co-founder & Chief AI Officer at Ekai. “It has to be captured from the people who define the business, built and proven in the data, and owned by someone with their name on it. With Ekai, nothing ships unchecked.”
Ekai is also releasing benchmark figures from its early engagements: semantic modeling that has historically taken teams three to six months has been completed by Ekai’s process in as little as six hours, at a fraction of the cost. Ekai frames this as a verification story more than a cost one: the speed comes from not having to guess, correct, and re-guess what the data means.
“Mo and his co-founders, Hussnain and Tero Miikki, each have spent over two decades in leadership roles at Accenture, Microsoft and UPM, which put them in a room with chief technology and data officers trying to get AI into production. They aren’t guessing at the problem Ekai solves; they watched it go unsolved from the other side of the table,” said Mark Coffey, co-founder and Managing Partner at Misneach. “Ekai closes that gap in days rather than months, without the data ever leaving the company, and what a company gets back from its AI goes from unreliable to the exact answers the business needs to run on.”
Ekai draws a line that the context layer for enterprise AI is not a technology problem. It is a knowledge capture problem. The knowledge lives in documents, in databases, in processes, and in people heads i.e. tribal company knowledge. Ekai is careful to distinguish its position from the “context engineering” conversation currently dominating AI infrastructure discourse, a term increasingly used to describe how AI agents manage memory, retrieval, and prompt context in real time. Ekai’s focus, the company says, sits one layer below that: not how an agent holds context during a conversation, but whether the business meaning it’s reasoning over was ever actually verified in the first place, with a clear trail of who declared what.
“Every AI agent deployed in the enterprise relies on knowing what data actually means to the said Patricia Geli, co-founder and Managing Partner of C10 Labs. “Ekai is the first platform that puts domain experts directly in charge of defining that business meaning with full governance, speed, and accuracy. We are proud to back Ekai in this pre-seed round as they set a new standard for AI-ready enterprise data pipelines.”
Ekai’s AI workflows that turn raw warehouse data into secure, governed, AI-ready business data are available now to enterprise customers on Snowflake (along with listing on their marketplace), Databricks, BigQuery, Postgres, ClickHouse, DuckDB, RedShift, and Azure Synapse environments. Ekai runs inside a customer’s own cloud; data is read in place & never copied out or retained. Both the data & Ekai stay under the customer’s control, always.
ABOUT MISNEACH
Misneach is a Boston-based Founder Propulsion Platform backing early-stage founders from formation through seed. Capital is the entry point. The differentiator is what comes with it: embedded AI infrastructure and execution leverage that give founders capabilities most companies can’t access until much later in their lifecycle.
ABOUT EKAI
Ekai turns your analytical data and expert knowledge into business context for AI. The company captures business meaning directly from the people who run the business, verifies it against the underlying data, and publishes governed, AI-ready models with an accountable author, so data products ship in days, not months.
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