#Web
#AI
#B2B
AI-POWERED DATABASE
An AI SQL optimization tool for the enterprise product that leverages LLMs to rewrite queries for better performance, empowers database analysts and developers to work more efficiently, reduce errors, and confidently integrate AI into their workflows.
Business AI
Concept to Creation(0-to-1)
Sep 2024 - Dec 2024
Product Designer
Frontend Developer
Why this is a challenge?
Enterprise SQL is not simple: In large organizations, queries often involve nested subqueries, CTEs, joins across many tables, stored procedures, and business-specific logic.
Our solution to the issue
We developed an AI-powered SQL optimization tool with intuitive query comparisons, real-time validation and suggestions, and a scalable React.js frontend designed for future growth.
Timeline
Quick overview of this project timeline
Team work
How I work in the team
We followed a 1-week Agile sprint cycle, using JIRA to manage tasks and track progress. Updates and blockers were shared in real-time via Slack, and we continuously refined our approach based on feedback and expert insights.
Daily Stand-up
1-Week Sprints
Retrospective with Mentor
Interview
So…why traditional SQL queries create friction? To uncover key challenges, we interviewed 10+ Google database engineers.
1
The Struggle of Writing Efficient Queries
"Writing optimized SQL feels like black magic—I never know if my query is efficient." – Software Engineer
2
Time-Consuming Debugging Process
"I waste so much time debugging performance issues. If AI could help, I’d use it daily." – Database Administrator
3
Trust & Transparency in AI Suggestions
"AI-generated queries sound useful, but I need to trust what’s happening under the hood." – Data Analyst
Persona
From scattered pain points to a clear persona that guided our design decisions.
Sketch
Bridging the Gap, Turning Ideas into Action
With user needs defined, I quickly explored solutions through low-fidelity sketches, mapping out layouts and interactions for early validation. This rapid iteration helped align stakeholders before refining the design in Figma, where I built the first high-fidelity prototype, ensuring usability and AI-driven functionality were seamlessly integrated
Sketch
Explain how do you think? And why you choose the final ideas to go forward
Mockup
Explain how do you think? And why you choose the final ideas to go forward
Identify improvement
In our Agile retro, I gathered feedback from our Google mentor and team to identify improvement areas.
❤️ Likes
1
AI-powered query optimization intrigued users
2
Side-by-side query comparison made it easier to evaluate improvements
3
Visual runtime difference indicators helped users grasp performance gains
😍 Wants
1
Clearer button naming for better action clarity
2
More transparency in AI-generated outputs
3
Improved execution control to prevent redundant query submissions
Iteration
Iteration 1 - Side-by-side query
1. Users now see their input on the left and AI suggestions on the right, making comparisons effortless
2. Added performance metrics to show AI-improved efficiency at a glance
Before
One-Side Query
After
Side-by-Side Query
Iteration 2 - Clearer CTA naming
Renamed "Optimize with AI" to "AI Rewritten" for better clarity.
Before
Normal CTA text
After
Clearer CTA Naming
Iteration 3 - Predefined query boxes
Users can select from common query patterns, reducing the need to start from scratch
New feature
Challenge
When our frontend engineer Zhiqian had an accident. We quickly adapted by redistributing his unfinished tasks across the team to stay on track.
Solve technical problem
I solve the problem with my frontend experience ✨
My frontend experience allowed me to step in during team challenges, ensuring continuity and a smooth user experience.
With continuous iteration, the final design achieved
Iteration
✨ Intuitive AI-powered SQL optimization with transparent query comparisons
⚡ Faster performance with real-time syntax validation & AI-suggested improvements
✅ A scalable frontend framework, built with React.js for future extensibility
UI Demo
Prototype
Impact
Impact & result
In 4 months
designed, developed, and launched from 0 to 1
86%
reduction in query time
(TPC-H: 255.77s → 35.92s,
SSB: 22.7s → 2s)
98&
user satisfaction, validated through 5+ in-depth user interviews
Reflection
What I learned
✅
Beyond UI/UX, effective product design means understanding user pain points, business goals, and technical feasibility to create impactful, scalable solutions
✅
Building AI-driven features requires balancing automation and user control. Users trust AI when given clear explanations and the ability to refine results
✅
Working closely with engineers, researchers, and mentors reinforced the importance of iterative feedback and agile workflows for refining solutions
