Data Context Graph (ReasonBase™)

Ontology

LLMs don't understand your business vocabulary.

For example, the word 'visibility' could refer to content readership for a marketing team but financial runway for a finance team - same term, completely different business meaning.

WALT analyzes your enterprise's structured data and supporting systems, building a vocabulary map that continuously evolves as users define new business concepts.

Knowledge Graph

WALT canonicalizes business concepts to accurate data assets.

Modern enterprise data architectures introduce new complexity. As pipelines become more distributed, tracking lineage grows exponentially harder. Teams end up with revenue, revenue_final, revenue_corrected - unsure which metric is correct.

WALT works like a data engineer: introspecting data, mapping relationships, and tracing sources from Tableau to Datahub, and building lineage graphs for accurate inference. He integrates with MDM, governance tools, BI dashboards, and data catalogs to leverage existing investments.

Semantic Layer

WALT agentically harvests KPIs from your existing business intelligence.

Every business defines metrics differently. If you've invested in data catalogs or governance tools, WALT leverages them with bidirectional access. If not, he creates the initial framework for your data steward to approve. All KPIs are stored centrally in human-readable format, enabling natural language interaction to distinguish between accurate definitions and hallucinations.

The data context graph (ReasonBase) is an independent virtual layer - it holds context about your data, not the data itself. Your data stays in your warehouse or lake, accessed via SQL or other methods. Nothing has to lift and shift.
Each ReasonBase is specific to the department and user. "Visibility" means one thing to a CFO and something entirely different to a marketing team. The same term, resolved to the right meaning for the right person.
The data context graph is accessible via MCP, enabling natural language interaction across all your downstream human and agent workflows.
Together with the deterministic analytical inference engine, this forms Walt's neuro-symbolic foundation - a stable, governed layer that any future agent can build on to interpret your data and take action with confidence.