Connect enterprise data into a single semantic network that understands relationships across entities — products, assets, customers, transactions, and events. E.g., Connect “organic yogurt” to its brand, shelf placement, and store.

Interpret visual, audio, and sensor data alongside structured business data to detect anomalies, track performance, and trigger automated responses. E.g., Video flags missing Coke end caps during a promotion, triggering a real-time fix.

Use historical and real-time data to anticipate outcomes, forecast demand, detect risk, and recommend proactive actions before issues occur. E.g., Predicting a spike in iced tea sales because of a heat wave and moving stock from one warehouse to another.

Domain-tuned large language and generative models that synthesize insights, summarize findings, and generate human-like narratives or recommendations grounded in enterprise data.

Specialized, task-oriented AI components that coordinate across systems to execute multi-step workflows — from investigation triage to maintenance scheduling. E.g., Product launch and promotion optimization agents.

Ensures expert supervision where judgment, governance, or compliance is critical — allowing humans to review, approve, or refine AI-driven recommendations.. E.g., Approving a nationwide price reset recommendation before it goes live.