From data to decisions with
the ontology management system
for Decision Intelligence.

Model how your business works. Reason over enterprise knowledge.
Operationalize decisions across AI agents, applications and workflows.

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What’s possible.

Which 5G cells will breach SLA in the next 2 hours due to the storm?

18 cells at risk; 6 already >80% load. Rebalance to sectors A/B and cap video to 720p in ZIPs 76112–76120. Predicted drop in call‑failures: −67%.

What price floor maximizes Q4 sports revenue without missing delivery?

Set floor at $14.20; revenue +8.3%, under‑delivery risk 1.1%. If $15.00, revenue +10.6% but breach risk 6.4% in weeks 47–48.

Which consignments will miss reserve this month, and how do we fix it?

12 lots at risk; median shortfall 7%. Two levers: lower reserve by 3–5% (+$610k sell‑through) or boost bidder liquidity in TX/AB via targeted email to 4,200 lookalikes (+$480k).

Ask about shipments, supplies, backlogs...

Which consignments will miss reserve this month, and how do we fix it?

What price floor maximizes Q4 sports revenue without missing delivery?

Which 5G cells will breach SLA in the next 2 hours due to the storm?

Your AI has access to the data.
But does it understand the business?

Three issues stand between general-purpose AI and enterprise decision intelligence.

THE CONTEXT GAP
AI can't reliably understand your business.

THE REASONING GAP
AI can't reliably solve complex business problems.

THE MODEL GAP
AI has to relearn your business every time.

Business questions take longer to answer, require more human validation, and are harder to trust.

Complex decisions require more manual analysis and disconnected tools, making outcomes slower and less consistent.

Repeatedly supplying that context increases inference time, token consumption, and cost—and limits how efficiently AI can scale across the enterprise.

Give your enterprise a model from which to run itself.

single

RelationalAI creates a governed, executable model of how your business works on top of your Snowflake data.

Suddenly you don’t just have raw data. You have your company’s institutional knowledge available to answer complex questions in minutes—not months.

To minimize my tariff exposure, how should I distribute my part orders across my suppliers?

1 system,
3 gaps closed

UNDERSTAND
Give AI shared business context.
Model the business concepts, relationships, properties, requirements, rules and derived knowledge in a shared semantic model.
‍
REASON
Give AI the right tools for the problem.
Apply graph, rules-based, predictive, and prescriptive reasoning over the same enterprise knowledge to understand relationships, apply business logic, anticipate outcomes, and optimize decisions.
LEARN
Teach AI more about your business.
Use enterprise semantics and reasoning to specialize models through Superalignment and post-training—improving how efficiently and accurately AI works with your business.

Built for enterprise knowledge...

Expressive - Executable -
Scalable

End-to-end Decision Intelligence

Models relationships, business, and decision logic.

Reasons using unified graph, rule-based, predictive, and prescriptive technologies.

Operationalizes reasoning into enterprise-scale data and decision workloads.

Snowflake-
Native

Data never moves, governance never changes

Same architecture

Same governance

Same paradigm

1# Product substitution and halo dynamics
2alt = Product.ref ()
3overlap = Product.overlap_with(alt)
4define( Product.substitutes(alt, overlap) ) where ( overlap > 0.9 )
5define( Product. revenue_multiplier(1 + count (Product.bundles) * 0.4))
6
7# Delay impact
8define(Delay.revenue_impact(sum(
9Product. revenue * Product. revenue_multiplier * (1 - max(Product.substitutes))
10))). where (
11Delay.affects(Product)
12)

Open

Enterprise knowledge accessible
across data, applications and AI.

Open semantics: designed for interoperability across tools and platforms.

Open ontology: normalized relational tables, not a proprietary graph representation.

Queryable and governed: accesses ontology data through SQL while maintaining Snowflake governance and RBAC.

Uses the same enterprise knowledge across analytics, applications, reasoning workloads, APIs, and AI.

AI-ready

Builds the knowledge once.
Makes it available to every AI experience.

Gives agents access to all and the same concepts, relationships, policies, constraints, and decision logic.

Makes enterprise knowledge and reasoning available to many systems-- Rel, custom agents, Snowflake Intelligence, enterprise applications, APIs, and workflows.

Perfectionable: uses Superalignment and post-training to teach open-weight models the structures, semantics, and reasoning patterns of the enterprise.

1# Product substitution and halo dynamics
2alt = Product.ref ()
3overlap = Product.overlap_with(alt)
4define( Product.substitutes(alt, overlap) ) where ( overlap > 0.9 )
5define( Product. revenue_multiplier(1 + count (Product.bundles) * 0.4))
6
7# Delay impact
8define(Delay.revenue_impact(sum(
9Product. revenue * Product. revenue_multiplier * (1 - max(Product.substitutes))
10))). where (
11Delay.affects(Product)
12)

For decisions worth making.

Connected risk & impact

Understand dependencies, exposure and downstream effects across complex enterprise networks.
‍

Planning &
optimization

Allocate scarce resources across objectives, forecasts, policies and constraints.
‍

Customer & commercial intelligence

Connect customer context, behavior, economics and policy to pricing, retention and next-best actions.

Operations & resource allocation

Coordinate inventory, capacity, assets and people against changing business conditions.

Fraud, risk &
compliance

Reason across entities, transactions, relationships, policy and risk signals.

Enterprise
AI

Give agents governed enterprise knowledge and reasoning capabilities.

From data to decisions with the ontology management system for Decision Intelligence.

Your AI has access to the data. But does it understand the business?