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FREQUENTLY ASKED QUESTIONS
About DATA POEM
What is DATA POEM?
What problem does DATA POEM solve?
Who uses DATA POEM?
How is DATA POEM different from other enterprise AI platforms?
Which industries does DATA POEM support?
Where is DATA POEM headquartered and where are your teams?
The Product
What is POEM365?
What is FOUNT?
How do DATA POEM, POEM365, and FOUNT fit together?
What is a Large Causal Model and why does it matter?
What does "unified" actually mean in practice?
What kind of answers do teams actually get?
Enterprise Decision AI
What is Enterprise Decision AI?
What is the difference between Decision AI and Causal AI?
What is the difference between Decision AI and Generative AI?
What is causal AI and how is it different from predictive AI or machine learning?
Solutions & Use Cases
What are DATA POEM’s four solutions and what does each one do?
What decisions can POEM365 support?
Does DATA POEM replace our existing analytics stack or sit alongside it?
Can we start with one use case and expand later?
Are the four solutions separate products, or one platform?
Implementation & Deployment
How long does it take to deploy POEM365?
What does DATA POEM need from us to get started?
How much of our team's time does this require?
What integrations does POEM365 support?
How does DATA POEM handle messy or incomplete data?
How often does the model update or refresh?
Data Security & Privacy
Does DATA POEM use our data to train models for other companies?
What security standards does POEM365 meet?
What happens if we want to stop using POEM365?
Commercial & Pricing
How is POEM365 priced?
Do you offer a pilot or proof of concept?
What does a typical enterprise engagement look like?
Do you have customer references in our industry?
Getting Started
How do we request a demo?
What should we expect from a first conversation?
Who should be in the room for a first conversation?
How Is DATA POEM Different
We already have an analytics team and multiple tools. Why do we need this?
We've tried AI before and it never delivered. How is this different?
How do we know this actually works?
We have world-class data scientists. Can't they build this themselves?
Can you explain why the model recommends something, or is it a black box?
How is POEM365 different from MMM?
What are the specific limitations of MMM that POEM365 solves?
How is POEM365 different from BI and dashboard tools?
How is POEM365 different from decision intelligence platforms?
How is POEM365 different from multi-touch attribution?
