Anusama is an enterprise causal intelligence platform that models an organization as a connected causal system, enabling leaders to see changes, trace cause-and-effect across functions, and simulate interventions before committing. It unifies enterprise data with causal analytics and simulation in a single decision environment to address fragmented planning cycles. The platform builds an Enterprise Causal Memory from signals, assumptions, interventions, decisions, and outcomes.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprise planning remains fragmented across functions, systems, and time horizons, causing decision-makers to see consequences only after they have already propagated through demand, margin, supply, and cash. Signals such as demand shifts, margin compression, and competitor actions move faster than planning cycles, leaving leaders without visibility into the true causes of performance changes.
Solution
Anusama represents the enterprise as a connected causal system, bringing enterprise data, causal intelligence, and simulation into one decision environment. The platform enables leaders to understand what is changing, trace cause-and-effect relationships across the organization, and test interventions before committing resources. Anusama builds an Enterprise Causal Memory from signals, assumptions, interventions, decisions, and outcomes, revealing causal pathways, constraints, and trade-offs. This allows users to see the enterprise-wide consequences of local decisions, such as how a promotion may lift sales while compressing margins and increasing inventory exposure.
Target Audience
Primary users are enterprise leaders and planning teams in organizations that face complex, cross-functional decision-making involving demand, margin, supply, and cash trade-offs.
Features
- Enterprise Causal Memory that records and links signals, assumptions, interventions, decisions, and outcomes across the organization
- Causal intelligence engine that traces cause-and-effect pathways across functions, systems, and time horizons
- Simulation capabilities for testing interventions before committing to them, including modeling trade-offs and constraint impacts
- Unified decision environment that integrates enterprise data with causal analytics for real-time visibility into performance drivers
- Capability to model cross-functional consequences, such as promotions affecting sales, margins, inventory, and long-term brand performance