Every new meeting question triggered days of data prep. Finance teams now ask the data lake directly and test hypotheses in minutes.
A global consumer goods company whose finance organization consolidated more than 30 SAP data sources and multiple legacy systems into a centralized finance data lake on Databricks.
Finance teams built meeting prep around pre-prepared analysis sets, but live discussions consistently surfaced questions that required additional...
“With Genie, we’ve changed how finance works. What used to take days now takes minutes, and we can explore questions much more freely.”
Global producer of consumer goods across beauty, personal care, home, and nutrition.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Unilever's Financial analysis is part of this use case:
Related implementations across industries and use cases
Every 'why is this number moving?' queued behind the domain team. Now leaders ask Genie directly and get the answer on the spot.
Appliance insights bottlenecked through Slack—hours or days, shaped by who you asked. Now product teams query in plain English.
Ad-hoc queries required custom dashboards. Now, specialized agents answer natural language questions with 99.9% accuracy.
Every 'why is this number moving?' queued behind the domain team. Now leaders ask Genie directly and get the answer on the spot.
Appliance insights bottlenecked through Slack—hours or days, shaped by who you asked. Now product teams query in plain English.
Disparate tools slowed 320,000 employees. Unifying on Teams and Copilot connected the workforce and embedded AI into daily habits.
Disconnected systems trapped buyer data and complicated B2B orders. Now, AI-powered commerce unifies buying across showrooms and screens.
Routine administrative tasks once tied up human experts for entire days. AI now completes these workflows in just minutes.
Manual spot-checks missed duplicates. AI now audits 100% of spend, catching $20k in errors and reclaiming 160 hours for analysis.
Every new meeting question triggered days of data prep. Finance teams now ask the data lake directly and test hypotheses in minutes.
A global consumer goods company whose finance organization consolidated more than 30 SAP data sources and multiple legacy systems into a centralized finance data lake on Databricks.
Finance teams built meeting prep around pre-prepared analysis sets, but live discussions consistently surfaced questions that required additional...
“With Genie, we’ve changed how finance works. What used to take days now takes minutes, and we can explore questions much more freely.”
Global producer of consumer goods across beauty, personal care, home, and nutrition.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Unilever's Financial analysis is part of this use case:
Related implementations across industries and use cases
Every 'why is this number moving?' queued behind the domain team. Now leaders ask Genie directly and get the answer on the spot.
Appliance insights bottlenecked through Slack—hours or days, shaped by who you asked. Now product teams query in plain English.
Ad-hoc queries required custom dashboards. Now, specialized agents answer natural language questions with 99.9% accuracy.
Every 'why is this number moving?' queued behind the domain team. Now leaders ask Genie directly and get the answer on the spot.
Appliance insights bottlenecked through Slack—hours or days, shaped by who you asked. Now product teams query in plain English.
Disparate tools slowed 320,000 employees. Unifying on Teams and Copilot connected the workforce and embedded AI into daily habits.
Disconnected systems trapped buyer data and complicated B2B orders. Now, AI-powered commerce unifies buying across showrooms and screens.
Routine administrative tasks once tied up human experts for entire days. AI now completes these workflows in just minutes.
Manual spot-checks missed duplicates. AI now audits 100% of spend, catching $20k in errors and reclaiming 160 hours for analysis.