Choco digitizes ordering between restaurants and suppliers, helping the food supply chain move faster and with less waste.

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Challenge

Choco’s global stakeholders had plenty of answers buried in Looker—but they were “lost in the pile of dashboards.” Even designated Looker champions fielded a steady stream of requests about where to find numbers and how to interpret definitions. Analysts spent outsized time unblocking basic questions instead of tackling architectural data work. The result: delays, uneven interpretation of business logic, and high coordination costs across time zones.

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“Lots of questions from global stakeholders on little things that were available in Looker but lost in the pile… lots of time by analysts spent on just enabling the team—even those meant to be Looker champions.” — Marina Begovic

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Approach

Dot became the low-friction front door to Choco’s data across Looker and Databricks. Stakeholders ask questions in natural language and get governed, reviewable answers—without hunting through dashboards or memorizing metric definitions. When prompts are ambiguous, Dot asks for clarifications rather than guessing, and it routes users to the right sources, reinforcing a single source of truth.

Operational teams now handle quick questions in-channel, tag colleagues to iterate, and lean on Dot’s deeper analysis features for follow-ups. For the central analytics team, Dot acts as a force multiplier: it absorbs routine Q&A while incentivizing stronger documentation, clearer business definitions, and proactive data quality—because the assistant only performs well when the foundation is sound.

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“It’s an amplifier for all the work data teams do — accessible, fast, easy, scalable… and it forces us to structure, document, and maintain definitions the business can rely on.” — Marina Begovic

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Impact

Choco has saved 3,300+ hours, realized an estimated 18× ROI, and cut time-to-insight by 99%. Self-service adoption surged among the least technical and most operational stakeholders, while the analytics team reclaimed time for higher-leverage architectural and structural data problems—turning scattered dashboard spelunking into clear, governed answers on demand.