A supplier delay, counterparty failure, network outage, or suspicious transaction rarely affects one thing. RelationalAI models the entities, relationships, policies, risks, and dependencies involved, then brings together the reasoning needed to understand downstream impact, anticipate what could happen next, and determine where to act.
The result is a connected view of risk that agents and teams can use to move from “what is affected?” to “what should we do about it?”
Know what's connected. Know what's at risk. Know where to act.
Model the connected system.
Bring together the entities, relationships, dependencies, policies, and conditions needed to understand how an event or change can propagate through the business.
Customers · suppliers · counterparties · assets · products · transactions · dependencies · policies · conditions
Model · connect · map
Assess the impact.
Combine relationships, rules, predictions, and business context to trace how an event propagates, determine what is affected, and assess the significance of the impact.
Events · paths · dependencies · exposure · predicted impact · risk · business value · constraints
Traverse · infer · predict · assess
Determine where to act.
Learn enterprise semantics and recurring reasoning patterns, then evaluate affected entities against risk, predicted impact, business priorities, policies, and constraints to prioritize the response.
Exposure · impact · priorities · risk · business value · policies · constraints
Learn · prioritize · recommend · intervene
If this counterparty fails, where are we exposed—and what should we do?
If this material or supplier becomes unavailable, what is affected—and what should we change?
If this part of the network fails, which services and customers are at risk—and where should we intervene first?
Policies rarely operate in isolation. What applies can depend on who or what is involved, how entities are connected, the circumstances of a transaction or case, predicted risk, thresholds, exceptions, and other business context.
RelationalAI brings that knowledge together in the ontology and applies the reasoning needed to determine what applies, why, and what should happen next.
The result is consistent, explainable decisions that agents and teams can apply across complex cases.
Know the situation. Know what applies.
Know what's next.
Model the decision context.
Bring together the entities, relationships, policies, requirements, history, and risk factors needed to understand the situation and the conditions that may apply.
Customers · accounts · transactions · products · ownership · policies · requirements · history · risk factors
Model · connect · contextualize
Determine what applies.
Combine policies, rules, requirements, relationships, predicted risk, thresholds, and exceptions to evaluate the situation and determine how it should be treated.
Rules · requirements · thresholds · exceptions · relationships · risk scores · eligibility · constraints
Infer · evaluate · classify · validate
Determine the appropriate response.
Learn domain terminology, enterprise semantics, policies, and recurring reasoning patterns, then determine the appropriate action and preserve the context and logic behind it.
Eligibility · classifications · exceptions · risk · requirements · decision rationale
Learn · decide · explain · approve · escalate · route
Does this transaction require investigation—and why?
Is this customer eligible for this service—and under what conditions?
Can this product be sold in this market—and what requirements must it meet?
Inventory, capital, capacity, people, and infrastructure are constrained. RelationalAI brings forecasts, business objectives, policies, and operational constraints together to evaluate trade-offs and determine how resources should be allocated.
Know your options. Know the trade-offs. Know what to do.
Model the decision environment.
Bring together the resources, relationships, objectives, policies, forecasts, and constraints that shape the decision.
Resources · demand · capacity · costs · objectives · policies · forecasts · dependencies · constraints
model · connect · define
Evaluate the alternatives.
Combine relationships, rules, predictions, objectives, and constraints to generate alternatives, understand trade-offs, and optimize against the desired outcome.
Alternatives · predicted outcomes · trade-offs · risk · cost · value · constraints
predict · simulate · compare · optimize
Turn what you know into action—and improve over time. Specialize AI on enterprise semantics and recurring decision patterns, then determine the plan or action that best satisfies the objectives and constraints.
Plans · allocations · recommendations · next-best actions · schedules · interventions
Allocate · prioritize · recommend · schedule · route · rebalance · intervene
How should we rebalance this portfolio given changing risk, return, and liquidity?
Where should we add capacity, and what should we do first?
What should we replenish, reallocate, or markdown—and where?
Blue Yonder uses RelationalAI to bring semantic understanding and reasoning to supply-chain decisions—helping customers anticipate disruptions, adapt plans, and optimize decisions while reducing the complexity of the underlying application.
Know what’s likely to happen next.
RelationalAI combines predictive reasoning with enterprise context so forecasts and predictions can account for the customers, products, assets, relationships, and conditions they describe—and become inputs to better decisions.
Hear AT&T and RelationalAI discuss specialized AI, autonomous networks, and using business context to optimize decisions across complex network environments.
Know what could be. Know what it means.
Know what to do next.
Model what drives the outcome.
Bring together the entities, relationships, events, behaviors, conditions, and business context needed to understand what is being predicted and what can influence it.
Customers · products · assets · events · behaviors · relationships · conditions · history · signals
model · connect · contextualize
Anticipate what happens next.
Combine predictive models with relationships, rules, and business context to estimate likely outcomes, identify relevant drivers, and understand how predictions affect the business.
Signals · features · probabilities · forecasts · risk · drivers · relationships · predicted outcomes
predict · score · forecast · assess
Turn predictions into decisions.Learn enterprise semantics and recurring predictive and reasoning patterns, then use predicted outcomes alongside objectives, policies, and constraints to determine the appropriate action.
Predicted outcomes · confidence · business value · objectives · policies · constraints · recommended actions
learn · prioritize · recommend · act
Which customers, transactions, or positions are most likely to create risk—and what should we do about it?
What is likely to happen across the network and customer base—and where should we act first?
What will customers want next—and how should we respond?
An Ontology Management System creates, governs, executes on, and evolves a shared model of how your enterprise works.
With it, we connect semantics, relationships, rules, predictions, constraints, and decision logic in one Snowflake-native solution for Decision Intelligence.
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