Data silos choked model scaling. A unified platform now powers 20,000 models to optimize sales routes and cut inventory waste.
One of the world’s largest snacking and confectionary companies, operating in over 160 countries with iconic brands like Oreo, Ritz, and Toblerone.
Data silos and duplicated development efforts across business units stifled innovation, while technical limitations made it difficult to scale...
“Databricks provides the platform and AI capabilities necessary to drive product innovation, enhance operational efficiencies and implement responsible AI practices across our organization.”
Global manufacturer of chocolate, biscuits, and snack food products.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Mondelēz International's Demand forecasting and sales is part of this use case:
Related implementations across industries and use cases
A dev backlog slowed AI rollout. Now, 7,000 staff build their own agents with no-code tools, saving 20,000 hours monthly.
Siloed data forced manual reviews of thousands of reports. AI agents now analyze sales volumes and SKU performance instantly.
Vendors missed demand targets by 30%. Now, ML automates SKU-level forecasts and GenAI extracts insights from customer service tickets.
Siloed data forced manual reviews of thousands of reports. AI agents now analyze sales volumes and SKU performance instantly.
Vendors missed demand targets by 30%. Now, ML automates SKU-level forecasts and GenAI extracts insights from customer service tickets.
Routine queries burdened HR. Now, an AI WhatsApp agent instantly answers remote factory workers, escalating complex issues to HR.
Disconnected systems trapped buyer data and complicated B2B orders. Now, AI-powered commerce unifies buying across showrooms and screens.
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.
Data silos choked model scaling. A unified platform now powers 20,000 models to optimize sales routes and cut inventory waste.
One of the world’s largest snacking and confectionary companies, operating in over 160 countries with iconic brands like Oreo, Ritz, and Toblerone.
Data silos and duplicated development efforts across business units stifled innovation, while technical limitations made it difficult to scale...
“Databricks provides the platform and AI capabilities necessary to drive product innovation, enhance operational efficiencies and implement responsible AI practices across our organization.”
Global manufacturer of chocolate, biscuits, and snack food products.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Mondelēz International's Demand forecasting and sales is part of this use case:
Related implementations across industries and use cases
A dev backlog slowed AI rollout. Now, 7,000 staff build their own agents with no-code tools, saving 20,000 hours monthly.
Siloed data forced manual reviews of thousands of reports. AI agents now analyze sales volumes and SKU performance instantly.
Vendors missed demand targets by 30%. Now, ML automates SKU-level forecasts and GenAI extracts insights from customer service tickets.
Siloed data forced manual reviews of thousands of reports. AI agents now analyze sales volumes and SKU performance instantly.
Vendors missed demand targets by 30%. Now, ML automates SKU-level forecasts and GenAI extracts insights from customer service tickets.
Routine queries burdened HR. Now, an AI WhatsApp agent instantly answers remote factory workers, escalating complex issues to HR.
Disconnected systems trapped buyer data and complicated B2B orders. Now, AI-powered commerce unifies buying across showrooms and screens.
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.