CASE STUDY
Fast food, faster insights:
How PromptQL put conversational analytics on the menu for a global QSR
Company
Leading quick service restaurant (QSR) with 40,000 restaurants across 100+ countries
Challenge
The company had invested heavily in a world-class data and analytics stack, centralized under a Global Data & Analytics team. Yet business users still depended on analysts and SQL experts for answers beyond dashboards. The bottleneck was felt most in international markets with limited local data and analytics support – slowing decisions and costing the business critical moments to act.
Solution
The company selected PromptQL to deploy an AI Analyst agent on their sales and transactions data. This enabled regional strategy and operations teams to ask complex business questions in plain speak and get accurate well-reasoned answers instantly. This empowered faster decisions around improved agility, and reduced reliance on analytics experts.
First, we tried to build it. Then we evaluated 100s of vendors. Then we chose PromptQL.
Why PromptQL
After evaluating more than 100 build and buy options, the company selected PromptQL for its precision, adaptability, and enterprise-grade design.
High accuracy
At the scale of their deployment, it was critical that business users could trust the results. Low hallucination, deterministic output, confidence signaling, and explainability made this possible.
Domain-specific AI
PromptQL understood the company’s unique terminology and operational language, adapting seamlessly to the nuances of the QSR domain.
Low data prep
The company had a complex, sophisticated data infrastructure. PromptQL operated directly on top of it, delivering cross-source intelligence without costly centralization or ETL.
Enterprise-ready
Flexible deployment, granular security controls, and seamless integration with the existing data stack.
Fast time to value
With a forward-deployed model and platform features like semantic layer bootstrapping from existing documentation, the company saw proof of value on their own data within days – building confidence that this wasn’t just another shiny demo.
Example User Questions
Show the weekly trend for self-service channel orders, broken down by L1 region.
For all locations with average processing time above 120 seconds, list the location ID, manager, and staffing percentage for April 2025.
Rank each location by average transaction value within its region for Q1 2025 and return the top three per region along with their guest counts.
What were the top three order-paid channels by gross sales during the first week of July in Germany?