Live webinar - May 19, 2026 · 11 AM IST · Free
Most tools model your data. Some model your domain.Surface blindspots.
DecisionX models all three and traces the causal chain from data to decision to outcome.
1 Studio · 3 Ontologies · One reasoning fabric
The complete model of how your organization decides.
Agents build the ontology.
Humans govern the judgment.
Agents ingest raw, undocumented data, resolving mismatched joins, inferring entity relationships, and bootstrapping 90% of the ontology without human input. No schema prep. No clean-room assumptions. The ontology emerges from whatever you have.
A second plane sits above the entity graph. This is where reasoning lives: what was decided, under what policy, against what state of the world, and what outcome it produced. The causal chain becomes queryable and governable for the first time.
When agents surface a conflict, two valid interpretations of the same policy, an ambiguity the data can't resolve, your team makes the judgment. Every call is captured. The system learns from it. The ontology gets sharper with every decision your organization makes against it.
Webinar Agenda Overview.
People who feel the cost when decisions don't travel well across functions.
You run the operating rhythm. You see where decisions made in one function quietly break something in another, and there's no system that traces why, only a post-mortem.
You have governance infrastructure for data quality and access. You have nothing for governing how the organization reasons and decides at scale. That is the gap of the AI era.
Your agents have context. They still get it wrong across function boundaries. The missing piece is a substrate that models the decisions the data is meant to drive, not just the data itself.
Your decisions touch engineering, design, GTM, and data simultaneously. When one function's context overrides another's, the product breaks. Decision Ontology is the shared reasoning layer that prevents that.
You own outcomes, but the decisions producing them happen across five functions. When something goes wrong, there's no causal chain to audit. Just a gap between what should have happened and what did.
You encode business logic in dbt and metric layers that nobody outside your team can query. Decision Ontology is where that logic should live, versioned, cross-domain, agent-legible, and accountable to outcomes.
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May 19, 11 AM IST. One hour. A live build on real enterprise data from raw, undocumented assets to a fully queryable State Graph with Decision Ontology. Not a slide deck. The thing itself.
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