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A modern tool stack for the modern FDE team.

ekai is the modern, end-to-end semantic data layer forward deployed and applied AI engineers need to accelerate enterprise AI implementation, without expensive systems integrators. Implementation stops being the bottleneck for AI-native products.

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Semantics › Sales and revenueOwner: FN

Model DetailsBusiness RequirementsAI ModelingData TransformationCode Sync

EXPLORERBusiness Requirements 6Schema NotesDBML SubsetConversation LogsBRD ReviewTechnical DocumentBusiness Requirements Document

Business Requirements Document

Executive SummaryNet revenue for the finance team, by region and quarter.

Business ContextBusiness Glossary · KPIs & Metrics · Consumer & Decision Context

Business RulesRevenue is reported by region and fiscal quarter.

Unresolved GapsGross or net AMOUNT, asked of Finance.

Business Requirements agentasking

Is `AMOUNT` gross, or net of tax and discount?

1Gross, before tax and discount. Matches the invoice total.

2Net of tax and discount, which matches the finance ledger.

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Why it matters

This is the $1 trillion reinvention of enterprise software implementation, and ekai is helping to disrupt it.

No more waiting on expensive systems integrators. Teams building AI-native products use ekai to go from raw data to trusted data products in hours.

ekai is the I in the AIR layers.

Enterprise AI runs on three layers: systems of action where agents work, systems of intelligence where context lives, and systems of record where data sits. ekai is the system of intelligence in between, so the context you capture once is reused by every agent you deploy.

ASystems of actionWhere your agents work

ISystems of intelligenceWhere context lives

RSystems of recordWhere your data sits

Deployments slow down at the data layer.

Every deployment starts with weeks of interviews.

You learn how each customer defines revenue by asking, one meeting at a time.

What you learn stays in your notes.

When you move to the next account, the definitions you gathered are not written down anywhere the agent can read.

Hand-written models are hard to prove.

Nobody can easily show that the model you shipped matches the numbers in the warehouse.

Customer result6 hours, $56

Secunetics built its production dbt pipeline with ekai in 6 hours for $56, against 96+ hours and about $26,400 by hand.

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Raw rows stay in your warehouse

Discovery reads your tables in place. ekai's SOC 2 Type II audit found zero exceptions.

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