One governed data layer. Every warehouse, every agent, no lock-in.
Snowflake today, Databricks tomorrow — Ekai outputs standard dbt and YAML you own, so the platform decision stays yours, not your AI vendor’s.
See how it works ↓See a verified model of your data.
Bring one dataset and one question you can’t confidently answer today.
Runs in your environment. Nothing leaves, not even metadata.
Sound familiar?
Wrong answers go on the record.
When a copilot guesses at account terms, that guess becomes a customer conversation you have to walk back.
Consulting fees for what you already know.
Semantic modeling engagements run six figures, and hand-authored models start aging the moment they ship.
Security reviews slow every launch.
Tools that copy data out add another review. Ekai runs in your environment — nothing leaves, not even metadata.
Ekai gives every agent the same verified definitions — built in hours, run inside your environment, and checked before they reach a customer.
AI agents sped up the code. Context engineering is still the bottleneck.
Coding agents cut the build to days or hours. Manual data discovery, relationship mapping, and business-rule definition still take months. Ekai automates and co-authors that work instead.
of traditional time-to-value is spent on manual data and context engineering — not on building or coding. Ekai removes that bottleneck, so data products go from an idea to production in hours, not months.
How Ekai works
One security team onboarded a new threat feed with Ekai — a 97%+ reduction in both time and cost.
“By 2028, most enterprises running agents in production will standardize on a dedicated context platform instead of stitching context together ad hoc.”
Questions CIOs ask first
- Where does this sit in an enterprise data architecture?
- Ekai is the business-context layer, not another store. It reads the warehouse you already run and publishes a unified data layer that your BI tools and your agents both read from. Nothing moves and nothing is copied out.
- We are mid data modernization. Is this too early?
- It is the opposite. Legacy data modernization moves tables; the meaning stays behind in the heads of the people who wrote the original logic. Capturing it while those people are still on the project is the cheapest it will ever be.
- How do we know our data is ready for AI?
- Start with a data readiness assessment on one domain. Ekai profiles the tables, puts the open questions to your experts, and returns a verified model — so AI data readiness becomes something you measured rather than something you asserted.
- Does this replace our enterprise data strategy?
- No. It supplies the data foundation for AI that most strategies assume and few actually have: definitions that are written down, owned by a named person, and checked against the warehouse.
Answer customers from what’s true.
See Ekai turn your experts’ knowledge into a verified semantic model, live.