We Raised $1.7M to Fix Enterprise AI’s Meaning Gap

Ekai has raised a $1.7 million pre-seed round led by Misneach, with participation from C10 Labs. We’re putting it toward three things: building the product faster, scaling our go-to-market, and deepening our platform integrations.

Key facts

Company

Ekai, the semantic layer that makes enterprise data AI-ready

Round

Pre-seed

Amount

$1.7 million

Led by

Misneach

Participation

C10 Labs

Use of funds

Product development, go-to-market, and deeper platform integrations

Why does enterprise AI keep failing?

But the round matters less than the problem it’s funding. Enterprise AI keeps failing for a reason most of the industry now agrees on, and we think most of the industry is fixing it the wrong way. The fix isn’t a bigger or faster model. It’s a verified one.

This year, a single explanation took hold for why AI keeps stalling inside enterprises: the models aren’t the problem. The missing business context is. Analysts, vendors, Anthropic and Gartner have all said some version of it.

Where the agreement ends is the cure. Most approaches try to infer what a company’s data means from what already exists: BI dashboards, query history, the dbt project. That’s useful for documenting what you have. It does nothing for what you need.

What is wrong with inferring meaning from dashboards and query history?

Our CEO, Moatassim (Mo) Aidrus, puts it bluntly:

Reverse-engineering from existing BI dashboards, query history, your dbt project, is asking the exhaust pipe what the engine was thinking. This is useful for documenting what you have, but it does nothing for what you need next.

Dashboards and queries are artifacts of past decisions. They record what someone built, not what the business actually means by its terms, or what it will need to ask tomorrow.

What is forward-engineering?

Ekai runs the sequence the other way around. We call it forward-engineering:

1. Go to the domain experts who actually know what the data means.
2. Treat what they say as ground truth.
3. Translate it into machine-readable business logic, transformation code and validation rules.
4. Reconcile every generated artifact against the warehouse before it’s allowed to ship.

Ekai isn’t another developer tool. It’s an AI augmentation layer: human experts drive the semantic model, and AI handles the speed, scale and mechanical translation into production infrastructure.

Why does nothing ship unchecked?

The industry is getting comfortable with unverified AI output. We think that’s a mistake. Automation without an accountable human checking the results isn’t a feature; it’s the risk the industry keeps rediscovering the hard way.

Hussnain Ahmed, our co-founder and Chief AI Officer, explains why the knowledge can’t come from the model:

Foundation models were trained on the public internet, not on your enterprise data. They know the term ‘active user,’ but they have no idea what it means in your company or where it lives in your data warehouse. No model ships with that knowledge. 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.

How long does semantic modeling take with Ekai?

In our early engagements, semantic modeling that has historically taken teams three to six months was completed with Ekai’s process in as little as six hours, at a fraction of the cost.

We see that as a verification story more than a cost story. The speed comes from not having to guess, correct and re-guess what the data means. When the meaning is captured and checked up front, the rework disappears.

Is the context layer a technology problem or a knowledge capture problem?

The context layer for enterprise AI isn’t fundamentally a technology problem. It’s a knowledge capture problem. Business meaning lives in documents, databases, processes and people’s heads: the tribal knowledge every company runs on.

That’s also why we distinguish Ekai from “context engineering,” the term now dominating AI infrastructure conversations. Context engineering is about how AI agents manage memory, retrieval and prompt context in real time. Ekai sits one layer below. Our question isn’t how an agent holds context during a conversation. It’s whether the business meaning that agent reasons over was ever verified in the first place, with a clear trail of who declared what.

Why are our investors backing this?

Mark Coffey, co-founder and Managing Partner at Misneach, on the team:

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. 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.

Patricia Geli, co-founder and Managing Partner of C10 Labs, on the category:

Every AI agent deployed in the enterprise relies on knowing what data actually means to the business. 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.

Misneach is a Boston-based Founder Propulsion Platform backing early-stage founders from formation through seed, pairing capital with embedded AI infrastructure and execution leverage.

Which platforms does Ekai support today?

Ekai’s AI workflows turn raw warehouse data into secure, governed, AI-ready business data. They’re available today for enterprise customers on Snowflake (including a listing on the Snowflake Marketplace), Databricks, BigQuery, Postgres, ClickHouse, DuckDB, Redshift and Azure Synapse.

Ekai runs inside your own cloud. Data is read in place and never copied out or retained. Both your data and Ekai stay under your control, always.

This round lets us move faster on all of it. If your AI initiatives are stalling on what your data actually means, we’d like to show you what verified business context looks like on your own warehouse. Get in touch with our team to see Ekai in action.

Frequently Asked Questions

Final Thoughts

Thank you to Misneach and C10 Labs for backing this, and to the customers and design partners who let us prove it on their own data.

The round is a start, not a result. The work is making verified business context something every enterprise can have, in days rather than months, without the data ever leaving their control.

References

1. Yahoo Finance. Ekai Raises $1.7M Pre-Seed Round Led by Misneach. finance.yahoo.com.
2. SiliconANGLE. Ekai raises $1.7M to give enterprise AI agents verified business context. siliconangle.com.
3. ekai. What is AI Semantic Modeling? ekai.ai/blog/what-is-ai-semantic-modeling.
4. ekai. SOC 2 Type II Compliance With Zero Exceptions. ekai.ai/blog/ekai-soc-2-type-ii-compliance.
5. ekai Security. ekai.ai/security.