TECHNOLOGY

How WALT Engineers Your Data Platform.

From building your data context graph to running your pipelines. Here's what's under the hood.

The Data Context Graph

WALT introspects your data sources, harvests KPIs from existing BI tools, canonicalizes business vocabulary, and builds the ontology, knowledge graph, semantic layer, and stable logic models autonomously. Continuously learning. Continuously evolving.

Production-ready SQL without LLM uncertainty.

We don't use LLMs to generate SQL. Our three-part analytical inference engine converts questions into safe, consistent SQL every time:

Data context graph: provides semantics: ontology, metrics, entities, relationships.

Logic models: provide predefined analytical patterns: time-over-time, cohort analysis, fanout-protected joins, multigrain models.

AST SQL Compiler: builds each SQL clause step-by-step through an abstract syntax tree. Deterministic. Auditable. Identical results every time.

Years of data engineering experience, encapsulated.

WALT has been trained and benchmarked on dozens of open-source data engineering repositories across industries and scales. E-commerce behavior, healthcare claims, financial markets, SaaS operations, NYC taxi data, government spending.Deploy immediately with zero configuration.

This isn't prompt engineering. This is learned data engineering judgment — the long tail of edge cases, schema quirks, calendar logic, and business rule exceptions that only experienced engineers know how to handle.

Same question, instant answer. Across your whole organization.

When two people in different departments ask similar questions, WALT resolves them from cache - no re-computation. Semantic similarity matching ensures slight variations ("Q4 revenue by region" vs "regional revenue last quarter") resolve to the same trusted result.

Data is stateful. WALT treats it that way.

Enterprise data isn't a static file - it's a living system with schemas that drift, pipelines that break, business rules that evolve, and consumption patterns that shift. WALT maintains state across every interaction, every schema change, every business decision.

The graph evolves with your data estate, not against it.This is why agents, not software. Software gives you a UI and waits. WALT observes, learns, adapts, and acts — continuously.

Every night, WALT grades its own work.

Every answer is evaluated against your business logic. Potential improvements are identified and applied. Accuracy strengthens over time - not through retraining, but through continuous alignment with your evolving data reality.

Evaluation-Driven Reinforcement Learning

Every day (and night), WALT evaluates every answer, strengthens your Data Context Graph, and validates responses against your business logic.

Continuous Evaluation

Every output is tested, benchmarked, and refined to maintain accuracy across your enterprise.

KPI-Aligned Reasoning

Trained on real datasets and public repositories to mirror dashboards and business metrics precisely.

Context Graph Strengthening

WALT proactively strengthens relationships between entities in your data to ensure rock-solid answers.