Even business analysts had to learn SQL just to run queries. Now teams ask in plain Portuguese — logistics, sales, and fraud self-serve.
Brazil's leading household goods retailer, managing data across logistics, supply chain, sales, and fraud prevention.
Non-technical staff, including business analysts, had to learn SQL just to run queries, creating a bottleneck where every data request funneled...
“Genie is the key to our data democratization strategy. It ensures that everyone, regardless of their background, can access the insights they need to perform at their best.”
Omnichannel retailer of electronics, furniture, and appliances in Brazil.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Grupo Casas Bahia's Natural language querying is part of this use case:
Related implementations across industries and use cases
Dashboards for everything, yet any off-script question meant a ticket and days in the queue. Now managers just ask and get the answer.
Cross-functional teams queued every data question—even simple ones—through a 3-day ticket backlog. Natural language queries made them self-sufficient.
Pulling reports from scattered dashboards took two days. Now, employees query an AI assistant for instant commercial insights.
Cross-functional teams queued every data question—even simple ones—through a 3-day ticket backlog. Natural language queries made them self-sufficient.
Analysts couldn't touch the data—every question funneled through engineers, taking hours or weeks. Now they get answers in seconds.
Two homegrown bots drained the engineers who built them. Claude Tag catches alerts, traces root causes, and opens PRs—without being asked.
Recruiters juggled disconnected systems while managers fielded candidate calls from the back office. An AI now screens and engages 24/7.
Energy market research that took weeks now closes same-day. Staff freed from chasing information to make the calls that matter.
With 99% of records missing metadata, teams sifted unlabeled databases. Now, AI generates column descriptions for data experts to verify.
Even business analysts had to learn SQL just to run queries. Now teams ask in plain Portuguese — logistics, sales, and fraud self-serve.
Brazil's leading household goods retailer, managing data across logistics, supply chain, sales, and fraud prevention.
Non-technical staff, including business analysts, had to learn SQL just to run queries, creating a bottleneck where every data request funneled...
“Genie is the key to our data democratization strategy. It ensures that everyone, regardless of their background, can access the insights they need to perform at their best.”
Omnichannel retailer of electronics, furniture, and appliances in Brazil.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Grupo Casas Bahia's Natural language querying is part of this use case:
Related implementations across industries and use cases
Dashboards for everything, yet any off-script question meant a ticket and days in the queue. Now managers just ask and get the answer.
Cross-functional teams queued every data question—even simple ones—through a 3-day ticket backlog. Natural language queries made them self-sufficient.
Pulling reports from scattered dashboards took two days. Now, employees query an AI assistant for instant commercial insights.
Cross-functional teams queued every data question—even simple ones—through a 3-day ticket backlog. Natural language queries made them self-sufficient.
Analysts couldn't touch the data—every question funneled through engineers, taking hours or weeks. Now they get answers in seconds.
Two homegrown bots drained the engineers who built them. Claude Tag catches alerts, traces root causes, and opens PRs—without being asked.
Recruiters juggled disconnected systems while managers fielded candidate calls from the back office. An AI now screens and engages 24/7.
Energy market research that took weeks now closes same-day. Staff freed from chasing information to make the calls that matter.
With 99% of records missing metadata, teams sifted unlabeled databases. Now, AI generates column descriptions for data experts to verify.