RETAIL

Your Warehouse Has Data. WALT Gives It Meaning.

Your dashboards show what happened. 
WALT tells you why — consistently, deterministically, every time.

The same partner shows up three different ways.

Entity inconsistency, contextual drift, and semantic misalignment cause otherwise modern data stacks to produce unreliable answers.

Entity Fragmentation

Entity inconsistency, contextual drift, and semantic misalignment cause otherwise modern data stacks to produce unreliable answers.

Semantic Drift

You tried plugging AI directly into the warehouse. It produced believable answers, but occasionally wrong ones. Thanksgiving YoY comparisons misaligned because of calendar logic. Cross-channel attribution didn't match. New product performance queries failed depending on how the product was referenced.

Adoption Stall

Accuracy mattered more than speed. Adoption stalled.

Your data warehouse has everything. Except meaning.

You modernized onto a cloud warehouse. You invested in BI. Dashboards show what happened. But every Monday meeting still ends with exports to Excel so teams can figure out why. The problem isn't tooling. It's meaning.

WALT Starts With Meaning. Not Queries.

he Reasoner builds a Data Context Graph - a living layer that describes your business reality. SKU hierarchies. Partner entities. Calendar logic. Metric definitions. Canonicalized. Governed. Continuously evolving. Once meaning is stabilized, everything else works.

No LLM-generated SQL. Stable Logic Models ensure production-grade consistency. Eval-driven reinforcement learns your company's logic over time.

"What's the sell-through on the fall jacket line by region?" Answered in seconds. Deterministic SQL. Same question, same answer, every time.

New SKU launched this morning, referenced by name this evening. Still resolved.

Partner entity inconsistencies auto-merged with a governed audit trail.

Holiday calendar logic handled correctly across fiscal and retail calendars.

Meetings Changed.

No more Excel digging. Teams move directly to decisions. Questions got harder and still worked. Repetitive requests became automated dashboards in Power BI or Superset. Analysts stopped acting astranslators between the business and the data.

Then The Agents Took Over.

Transformer

Optimizes marts based on real usage. 40% compute drop.

Ingestor

Tracks schema drift and fixes pipelines before Monday.

Operator

Monitors freshness and cuts alert noise by 90%.

Governor

Guides your platform evolution - Hive to Iceberg, batch to streaming.

The core realization.

AI wasn't failing because models were weak. AI failed because enterprise data lacked consistent meaning.Once meaning was stabilized, automation suddenly worked. And kept working.

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