Dashboards for everything, yet any off-script question meant a ticket and days in the queue. Now managers just ask and get the answer.
One of Brazil's largest pharmacy chains, with hundreds of physical stores, e-commerce, and a health and wellness business, operating across high-turnover inventory, regulated pricing, expiration management, and omnichannel transactions.
The team had a dashboard for almost every metric, yet any question that strayed off script meant filing a ticket with the BI team and waiting hours...
“Dashboards give static answers to questions already asked. That's fine when today's question matches yesterday's, but not when the business moves as fast as pharmacy retail does.”
Retail pharmacy chain operating over 600 stores in Southern Brazil.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Grupo Panvel's Business data queries is part of this use case:
Related implementations across industries and use cases
Cross-functional teams queued every data question—even simple ones—through a 3-day ticket backlog. Natural language queries made them self-sufficient.
Business users queued requests to specialists for simple questions. Now they ask in plain language—business knowledge drives the work.
With three doctors serving 400 stores, queries took 24 hours. GenAI now mines 500k files to cut response times by 70%.
Checking market share meant waiting for reports. Now, managers ask a Teams AI bot for instant, governed answers.
With three doctors serving 400 stores, queries took 24 hours. GenAI now mines 500k files to cut response times by 70%.
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.
Dashboards for everything, yet any off-script question meant a ticket and days in the queue. Now managers just ask and get the answer.
One of Brazil's largest pharmacy chains, with hundreds of physical stores, e-commerce, and a health and wellness business, operating across high-turnover inventory, regulated pricing, expiration management, and omnichannel transactions.
The team had a dashboard for almost every metric, yet any question that strayed off script meant filing a ticket with the BI team and waiting hours...
“Dashboards give static answers to questions already asked. That's fine when today's question matches yesterday's, but not when the business moves as fast as pharmacy retail does.”
Retail pharmacy chain operating over 600 stores in Southern Brazil.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Grupo Panvel's Business data queries is part of this use case:
Related implementations across industries and use cases
Cross-functional teams queued every data question—even simple ones—through a 3-day ticket backlog. Natural language queries made them self-sufficient.
Business users queued requests to specialists for simple questions. Now they ask in plain language—business knowledge drives the work.
With three doctors serving 400 stores, queries took 24 hours. GenAI now mines 500k files to cut response times by 70%.
Checking market share meant waiting for reports. Now, managers ask a Teams AI bot for instant, governed answers.
With three doctors serving 400 stores, queries took 24 hours. GenAI now mines 500k files to cut response times by 70%.
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.