For Ayushi, Feedback Fusion did not begin as a typical startup idea.
It began at the intersection of two worlds.
As a computer science student specializing in machine learning, she had spent years learning how to build intelligent systems. At the same time, through her work and collaboration with Dr. Greg Mayer, a professor in the School of Mathematics at Georgia Tech, she was seeing firsthand how those systems often failed to translate into real-world impact.
“The gap between what we build in technology and what people can actually use is still very wide,” she said.
That gap became real through their shared experience working with student feedback.
Greg was teaching large enrollment courses and receiving hundreds of qualitative responses from students. Ayushi, coming from a technical background, immediately recognized the scale of the problem.
“We were looking at the same issue from two different perspectives,” she explained. “On one side, there was a huge volume of feedback. On the other, there wasn’t a practical way to make sense of it.”
That was the starting point for Feedback Fusion.
Student feedback has always been part of education. Surveys, reflections, and course evaluations are standard practice.
But as enrollment grows, so does the volume of feedback, especially open-ended responses.
When Ayushi began exploring how institutions handle this today, a clear pattern emerged.
“Most existing tools weren’t built for higher education. They were either too technical, too research-heavy, or designed for commercial use cases.”
Even when tools offered analysis, they often required significant manual effort.
“Instructors still have to define categories, tag responses, and interpret everything themselves.”
At scale, that becomes unsustainable.
And more importantly, it means something critical gets lost.
Quantitative data is structured. A score has a clear meaning.
Qualitative data is different.
“The value lives in the ambiguity,” Ayushi said.
A single student comment can contain tone, confusion, frustration, or a subtle request for help. But most tools reduce that into simplified summaries.
Even standard AI models fall short.
“They capture dominant sentiment, but miss everything else.”
That includes subgroup differences and the diversity of student experiences within a single course.
“When everything gets averaged out, you lose the meaning behind what students are actually saying.”
This became a core principle behind Feedback Fusion.
There is no shortage of excitement around AI in education. But for Ayushi, its value is very specific.
“AI is most useful when it helps solve the scale problem.”
Students generate more feedback than instructors can realistically process. That mismatch is where AI can help.
“If an instructor can see in real time that a concern is emerging across multiple students, that becomes actionable.”
At that point, AI is no longer just a tool. It becomes a way to improve teaching decisions in real time.
But she is clear about one thing.
“AI should amplify instructor judgment, not replace it.”
In education, trust is essential.
That shaped how Feedback Fusion was built from the beginning.
“We don’t just give instructors a score,” she said. “Everything is grounded in actual student responses.”
The platform is designed to preserve tone, narrative, and context, not flatten it.
It also prioritizes privacy, including FERPA compliance, to ensure student information is protected.
“Qualitative feedback is subtle. Our goal is to capture that complexity in a way that educators can trust.”
Another challenge became clear early on.
Even when powerful tools exist, they are often not designed for everyday use by instructors.
Tools like Qualtrics and SurveyMonkey provide basic analysis but still require manual effort. Research tools like NVivo offer advanced capabilities, but are difficult to learn and use consistently.
“There isn’t really a tool that a typical faculty member would want to pick up and use regularly,” Ayushi said.
Feedback Fusion is designed to bridge that gap.
The goal is not to replicate everything, but to focus on what matters most and make it intuitive.
“We want this to be easy to follow, easy to use, and actually helpful in real teaching environments.”
One of the most meaningful opportunities Ayushi sees is around timing.
Today, feedback is often reviewed at the end of a course, when it is too late to act on it.
“What excites me most is closing that feedback loop in real time.”
With better visibility into qualitative feedback, instructors can identify patterns mid-course and respond when it matters most.
That can change outcomes.
“If we can surface issues earlier, we can help improve student outcomes and even retention, especially in large courses.”
Feedback Fusion is Ayushi’s first startup.
“It’s exciting, but also challenging. There’s a lot of uncertainty.”
At the same time, it is exactly the kind of work she was drawn to.
“You get to wear multiple hats and build something creatively from the ground up.”
Through it all, the experience has been deeply personal.
“The startup is building me as much as I’m building it.”
Feedback Fusion is an AI-powered qualitative data analytics platform designed for higher education. It helps institutions turn open-ended student feedback into meaningful, actionable insights while preserving context, nuance, and trust.
The goal is not to replace educators, but to support them by making it easier to understand what students are saying and respond in real time.
We are currently speaking with educators, instructional designers, and higher education leaders as we continue building Feedback Fusion.
If you are interested in learning more or sharing your perspective, we would love to connect.