Designing the Feedback System for AI Summaries at Sprinklr
Gave agents a way to respond to summaries and help improve Sprinklr’s AI, making it easier to trust and use summaries during case handling.
My Role
I was the sole designer responsible for turning four loosely defined use cases into a fully designed feature. My job was to clarify what the UI should do, explore how it should work, and deliver developer-ready designs.
In just two weeks, I designed a feedback system that made Smart Summaries easier to trust and easier to improve. The solution helped agents respond faster and gave Sprinklr’s AI smarter data to learn from. For this sprint, I collaborated with a UX Research, PM, and developer.
❌ The Problem
|
✅ The Solution
An intuitive UI for thumbs up/down, structured feedback, and peer votes. |
📈 The Outcome
Faster responses, stronger trust in summaries, and smarter AI training data.
Overview
Sprinklr is a customer experience platform that helps companies manage interactions across digital channels.
As part of Sprinklr Service, the Care Console is a unified workspace where care agents handle conversations, resolve tickets, and track case history efficiently.
To help agents work more efficiently, Sprinklr introduced AI-powered Smart Summaries, a feature agents can click to create a summary of case history, making it faster to understand past interactions without reading full transcripts.
Problem Statemement
Agents had no way to give feedback on Smart Summaries in the Care Console. They couldn’t confirm if a summary was helpful or explain what was wrong. This slowed down case handling, made the summaries less trustworthy, and gave the AI nothing to learn from.
The Solution at a Glance
I designed a simple, intuitive way for agents to leave a Like or Dislike on Smart Summaries, explain why a summary wasn’t helpful, and see how others had responded. This helped agents work faster and gave the AI better signals to learn from.
Clarifying What to Design
The design ticket helped visualize each user story alongside its pain points and requirements—clarifying which features I needed to design and why.
Used research-backed user stories to define the core design needs for Smart Summary feedback
Before the sprint began, the UX Researcher and PM shared four user stories outlining what agents needed and why current solutions weren’t working. These stories defined the problem and user needs. I was responsible for turning these needs into clear, intuitive designs that fit within the product.
Agent tasks that need design
Based on the user stories, these were the key tasks the design needed to support:
👍
Positive Feedback
Give positive feedback on accurate and helpful summaries
👎
Negative Feedback
Give negative feedback on
unhelpful summaries
🔢
Feedback Counter
See a vote count when clicking like, dislike, or regernate summary
🤬
Structured Feedback
Open a modal to give a reason when submitting negative feedback
Finding Patterns to Build From
Reusing patterns helped me move faster by referencing approved components and reducing extra work for engineering
Before exploring design solutions, I reviewed Sprinklr’s Hyperspace Design System, internal design files, and checked with teammates about any recently approved examples not yet in the library. I was looking for existing patterns—like voting, feedback, or modals—I could reuse or adapt to stay consistent and reduce dev effort, while identifying areas where new designs were needed.
Key Insights from reviewing design system and files
I found existing Like/Dislike icons, but no patterns for vote counts or structured AI feedback, and modal designs needed updates for feedback input.
Exploring Design Directions
Shared early design options to quickly align on a direction and save time in sprint
Since the requirements were clear and I was building on existing components, I skipped low-fidelity wireframes and jumped straight into high-fidelity explorations to make the most of the sprint timeline.
I created 2–3 design options for each user story using reusable components and patterns. These were shared with peers and the Principal Designer by the second day of the sprint to gather early feedback.
The goal was to quickly align on a single direction to guide the rest of the sprint.
Final design direction approved after review
The final direction placed feedback buttons on the left, removed UI outlines for clarity, moved the regenerate option to reduce clutter, used a modal for negative feedback, and kept all actions within the summary component.
Finalizing the Full Design
Turned the approved direction into final flows to align teams and move development forward
Once we aligned on a design direction, I walked through the solution with PM and Dev to ensure it met the business needs and was technically feasible. This gave the team a chance to raise concerns, clarify edge cases, and begin laying the engineering foundation in parallel with design refinement.
I then completed the full screen flows and interaction details for each user story and shared them with the Principal Designer for review. While waiting on feedback, I kept PM and Dev in the loop so we could proactively catch issues early.
After a few rounds of iteration, the Principal Designer approved the flows, and Dev confirmed they were ready to begin final implementation.
QA & Implementation
Once engineering began development, I reviewed early builds and ran QA to make sure the final implementation matched the approved designs. I:
Created a QA checklist based on the final designs
Identified any visual or interaction gaps in the implementation
Flagged fixes for engineers before the feature shipped
Impact & Reflection
This system helped agents respond faster, trust summaries more, and train the AI over time. While I wasn’t able to capture success metrics due to my departure, the feature was prioritized for rollout to key partners and became the foundation for future AI feedback tools.
How My Design Improved the Experience
✔️
Enabled agents to provide structured feedback, improving AI accuracy.
✔️
Built transparency into AI-generated summaries, increasing trust.
✔️
Replaced inefficient manual feedback methods with an integrated, scalable solution.
✔️
Ensured seamless implementation by leveraging existing UI patterns.
Lessons Learned
✔️
Aligning early with stakeholders minimizes rework and accelerates development.
✔️
Reusing design patterns ensures consistency and reduces complexity.
✔️
A clear, structured feedback loop is essential for building trust in AI and driving continuous improvement.