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Check out highlights of RelationalAI at Snowflake's Data Cloud Summit 2024!

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POWER EVERY DECISION WITH INTELLIGENCE

To make fast, repeatable decisions across the company, you have to put your
organization’s knowledge to work. RelationalAI’s Knowledge Graph Coprocessor
operationalizes the rules, relationships, and decision systems that drive your
business, all while staying entirely within the Snowflake data cloud.

get started



THE KNOWLEDGE GRAPH…

Think of the knowledge graph as a “collective common sense.” It doesn’t just
capture that you have a sales table, it captures that your team keeps an eye on
transactions over a certain amount that come from one of your more “eccentric”
customers.

dive in


… COPROCESSOR

Instead of creating another silo, we integrate powerful decision-making tools
like graph analytics, rules-based reasoning, and mathematical optimization
directly into your data cloud. Our Native Application, built on Snowpark
Container Services, keeps everything securely within the Snowflake perimeter.

explore


DECISIONS, SIMPLIFIED

The knowledge graph is a map for the decisions a business makes. By traversing
and annotating that map, you carve a path around all the tangled systems that
slow decisions down and start pushing the company forward. Let’s look at a few
examples.

> Which financial transactions need human reviewUnderstanding how money, items,
> and data flow through your business is a key for detecting issues like fraud
> before they become significant liabilities. Use AI to label suspicious actors
> and then traverse the transaction graph for situations that need review.

with model.rule():
    t = Transaction(from_=Customer(), to=Suspicious(Person))
    t.set(Suspicious)
    with t.amount > 40000:
        t.set(NeedsReview, 
              reason="Large transaction to suspicious person")

> Which customers in a social network are the most influentialIdentifying key
> influencers within a customer base enhances marketing strategies and
> engagement. Using graph algorithms like PageRank, businesses can determine the
> most influential customers based on their connections and interactions,
> enabling targeted marketing and efficient resource allocation.

with model.rule():
    msg = Message()
    weight = sum(msg, msg.length, per=[msg.sender, msg.to])
    Edge.add(msg.sender, msg.to, weight=weight)

with model.rule():
    user = User()
    rank = graph.compute.pagerank(user)
    user.set(rank=rank)

> Which nodes in a power grid need repairBy leveraging the graph structure of
> the power grid, repairs can by easily prioritized by impact, ensuring critical
> loads and distribution centers are automatically prioritized first.

with model.rule():
    upstream, downstream = NeedsRepair(), NeedsRepair()
    graph.compute.is_reachable(upstream, downstream)
    upstream.set(
        load_priority = aggregates.max(
            downstream.load_priority.or_(0), per = [upstream]
        ),
        connection_priority = aggregates.count(
            downstream, per = [upstream]
        ),
    )





BEYOND COUNT(*)

For business queries over massive amounts of data, there’s nothing better than
Snowflake, but SQL struggles to go beyond aggregations over tables.

Decisions require you to utilize every form of automated intelligence at your
disposal, from community detection and centrality analysis for finding your most
influential users to mixed integer solvers that determine the optimal allocation
of goods in your warehouses.

RelationalAI extends the best that Snowflake has to offer with an array of
powerful decision making tools to help deliver on the promise of “all data, all
workloads.”

01

Graph Analytics
Learn more
02

Rules-based reasoning
Learn more
03

Optimization
Learn more
Get Started
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