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Why is it hard to launch accurate AI?

Most AI projects fail because they lack business context. ekai automates the manual work of writing that context down.

Semantics › Sales and revenueOwner: FN

Model DetailsBusiness RequirementsAI ModelingData TransformationCode Sync

EXPLORERAI Modeling 6Business GlossaryLineageSales and Revenue Semantic Model ReviewData CatalogMetricsData Validation

Data Validation

Each rule has its validation logic in SQL and an expected result. The Data Transformation agent runs the rules when it builds.

GRAINone row per ORDER_IDExpected result0 violations

REFERENTIALevery REGION_CODE exists in REF.REGIONSExpected result0 violations

MEASUREAMOUNT - TAX - DISCOUNT >= 0Expected result0 violations

TEMPORALORDER_DATE falls inside the fiscal quarterExpected result0 violations

MEASUREnet_revenue >= 0 on every orderExpected result0 violations

← Business RequirementsData Transformation →

ekai product · demo data

90% of traditional time to value is spent on manual data and context engineering.

ekai removes the bottleneck, so data products go from an idea to production in hours, not months, turning your data and expert knowledge into business context for AI.

Traditional pathManual data and context engineering4 to 6 months

Data discovery and profiling
Relationship mapping
Business rules and definitions

With ekaiAutomated discovery and co-authored context4 to 6 hours

Ready for review

Nothing ships unchecked.

Generation is the easy part, so we made accountability the product.

Before anything publishes

One row per ORDER_IDData Validation · grain rule

Expected result: 0 violations

Every CUSTOMER_ID exists in dim_customerData Validation · referential rule

Expected result: 0 violations

net_revenue = amount − tax − discountData Validation · measure rule

Expected result: 0 violations

Sales and Revenue Semantic Model Reviewekai reviewing agent · read-only, binding

Pass

Publish approval checkpoint + publishData Transformation agent task · your approval

Checkpoint

◈ You approve each checkpoint before anything publishes

Anchored to your experts' answers

Each answer becomes a Data Validation rule with an expected result, written before the model is built.

Tests ship with the code

Schema tests, validation rules and documentation are generated with every dbt model.

Humans hold the gate

Inferred relationships, definitions and metrics are reviewed by your people. ekai shows its evidence and takes correction.

Neutral by design

No warehouse to lock you into. The output is standard dbt, YAML and JSON you own, wherever you run next.

Built by hand

Takes months and stays unverified.

Mined from query history

Repeats whatever the past did.

ekai

Builds from meaning and proves the result.

Illustration · demo data

Why can't enterprises do this by hand anymore?

Mo Aidrus, ekai's co-founder and CEO, explains why he built ekai.

The current challenges of semantic modeling.

Speed

Context engineering is still manual

Skills

Not everyone can learn every technology

Access

Business teams wait on IT before they can try an idea

Trust

Accuracy is hard to guarantee

Illustration · demo data

ekai's answer: make context engineering automated and self-service, so accuracy and speed stop being a trade-off.

Every tool in your stack does one job well. ekai ships the data product.

Observability watches the pipelines and flags when data breaks.

Catalogs list what you have and who owns it.

Transformation reshapes raw data into clean tables.

ekai ships faster data products, from the business question to a governed, published product.

ekai works alongside all three and does the step none of them do, turning what your experts know into proven definitions that every tool reads. It pushes standard dbt to your own Git, and catalog exchange is coming soon.

How does ekai compare to manual modeling or forward deployed engineers?

Honest trade-off: ekai still needs a few hours of expert time, and highly bespoke one-off logic may still need an engineer.

Time to first model

How it's verified

What you keep

Best fit

Manual modeling

Time to first modelMonths

How it's verifiedManual spot checks

What you keepDocuments that age

Best fitOne-off strategic redesigns

Forward deployed engineers

Time to first modelWeeks, then ongoing

How it's verifiedDepends on the engineer

What you keepCode tied to the team

Best fitHighly bespoke, long-term builds

Time to first modelHours, not months

How it's verifiedEvery definition reconciled against the warehouse

What you keepStandard dbt, YAML and JSON in your Git

Best fitRepeatable domains that need fast, verified answers

AI agents sped up the code. Why is context engineering still the bottleneck?

TraditionalMonths

DiscoveryManualMappingManualBusiness rulesManualCodeAI-assistedTestingManual

With ekaiHours

Each step automated, rules co-authored with your experts, and approval checkpoints before anything publishes.

Illustration

Common questions

Last updated October 28, 2026

Why do enterprise AI projects fail?

Most fail because they lack business context: the AI does not know what your numbers mean.

What is context engineering?

Writing down what your data means, such as definitions, rules and relationships, so AI can use it correctly.

How much faster is ekai than manual semantic modeling?

Secunetics built its production dbt pipeline with ekai in 6 hours for $56, against 96+ hours and about $26,400 by hand. In Allen Chen's words, that was 5% of the anticipated time.