Uber Saving $121.97 Million Every Year with QueryGPT AI Agent: Here’s How
The AI Solution That Saved Uber Millions
What is Uber?
Uber Technologies, Inc. is an American multinational transportation company that provides ride-hailing services, courier services, food delivery, and freight transport.It is headquartered in San Francisco, California, and operates in approximately 70 countries and 10,500 cities worldwide. It is the largest ridesharing company worldwide with over 150 million monthly active users and 6 million active drivers and couriers.
The Big Picture
Imagine asking your database questions in plain English and getting instant answers.
That's exactly what Uber achieved with QueryGPT, turning a 10-minute database query process into a 3-minute conversation. Here's how they did it, and why it matters for your business.
The Challenge: When Data Becomes a Bottleneck
Every month, Uber processes a staggering 1.2 million database queries. That's 1.2 million times someone needs to:
Hunt through complex data dictionaries
Figure out which tables contain the right information
Write precise SQL code
Hope they got it right the first time
For non-technical teams, this was like trying to find a book in a massive library where all the titles are in a different language. Operations teams, responsible for 36% of these queries, were particularly affected.
The Game-Changing Solution
Enter QueryGPT, born during Uber's Generative AI Hackdays in May 2023. Think of it as a universal translator between human questions and database language.
How It Works (In Plain English)
Ask Naturally: Users type questions like "How many rides happened in Seattle yesterday?"
Smart Understanding: The system figures out exactly what data you need
Automatic Translation: It converts your question into a perfect database query
Quick Results: Get your answer in about 3 minutes
Below is diagram on high level,
The Secret Sauce: Three Smart Agents
The Intent Agent: Like a skilled librarian who knows exactly which section has your book
The Table Agent: Picks the right data tables, like choosing the right ingredients for a recipe
Image Credit : from Uber Blog The Column Agent: Removes unnecessary information, keeping only what matters.
Smart Features
Custom workspaces for different departments
Intelligent table selection
Automatic query optimization
Real-time validation
Let’s talk about Results
Speed: From 10 minutes to 3 minutes per query
Adoption: 300 daily active users
Satisfaction: 78% report getting more done
Scale: Handles queries across multiple business domains
Why This Matters for Business
Time Savings: Imagine cutting any data-related task by 70%
Democratized Data: Everyone can access insights, not just technical experts
Better Decisions: Faster access to data means quicker, smarter choices
Resource Optimization: Technical teams can focus on complex problems
Lessons Learned
Start Small, Think Big: Uber began with a hackathon project and iteratively improved it
User First: Focus on making complex tasks simple
Validate Early: Include users in the process to ensure accuracy
Stay Flexible: Build systems that can grow with your needs
Ready to Transform Your Data Access?
Here's how to get started:
Assess: Map out your current data access bottlenecks
Start Small: Begin with a specific department or use case
Iterate: Gather feedback and improve continuously
Scale: Expand to more teams as you prove success
Call to Action
For Business Leaders: Ready to transform how your team uses data? Start by identifying your biggest data bottlenecks.
For Tech Teams: Want to build something similar? Begin with a small proof of concept in your highest-impact area.
For Everyone: Share this story with someone who's struggling with data access - they'll thank you later.
Remember: The future of data access isn't about writing better queries - it's about asking better questions.
And that’s a wrap for this edition! Stay tuned for more updates in the next newsletter. Until then, take care and stay curious!
Read the article in detail - Uber Case Study
Note : Please note that the savings calculation is based on some assumptions. We derived this estimate using data from the article, including Uber's processing of 1.2 million queries per month and saving 7 minutes per query. The value is calculated using the average hourly rate of a data engineer at Uber, estimated at $72.12 per hour, based on an annual salary of $150,000. Uber has not officially declared this savings figure.