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MVP Investment

$9K - $12K
6-10 weeks
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$8,000
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$240
SaaS Stack
$300
Domain & Legal
$100

6mo ROI

2-4x

3yr ROI

10-20x

Lightweight AI tools can reach profitability quickly. At $500/mo average contract, 20 customers = $10K MRR by 6mo, 200+ by 3yr.

Talent Scout

W

Walid Maalej

University of Hamburg

A

Ali Ebrahimi Pourasad

University of Hamburg

M

Meyssam Saghiri

University of Hamburg

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Feedback experts on LinkedIn & GitHub

References (33)

[1]
Requirements Elicitation Follow-Up Question Generation
2025Yuchen Shen, Anmol Singhal et al.
[2]
Can LLMs See What I See? A Study on Five Prompt Engineering Techniques for Evaluating UX on a Shopping Site
2025Subin Shin, Jeesun Oh et al.
[3]
Unmoderated Usability Studies Evolved: Can GPT Ask Useful Follow-up Questions?
2024Eduard Kuric, Peter Demcak et al.
[4]
Privacy-Preserving Techniques in Generative AI and Large Language Models: A Narrative Review
2024G. Feretzakis, Konstantinos Papaspyridis et al.
[5]
Does GenAI Make Usability Testing Obsolete?
2024Ali Ebrahimi Pourasad, Walid Maalej
[6]
ChatBR: Automated Assessment and Improvement of Bug Report Quality Using ChatGPT
2024Lili Bo, Wangjie Ji et al.
[7]
On the Automated Processing of User Feedback
2024Walid Maalej, V. Biryuk et al.
[8]
LLM-BRC: A large language model-based bug report classification framework
2024Xiaoting Du, Zhihao Liu et al.
[9]
Preserving Privacy in Multimedia : Text-Aware Sensitive Information Masking for Visual Data
2024Ardon Kotey, Tejan Gupta et al.
[10]
Consumers' promotion focus mitigates the negative effects of chatbots on purchase likelihood
2023Hiba Khan, Pasit Sararueangpong et al.
[11]
Unveiling Security, Privacy, and Ethical Concerns of ChatGPT
2023Xiaodong Wu, R. Duan et al.
[12]
Lost in the Middle: How Language Models Use Long Contexts
2023Nelson F. Liu, Kevin Lin et al.
[13]
What should I Ask: A Knowledge-driven Approach for Follow-up Questions Generation in Conversational Surveys
2022Yubin Ge, Ziang Xiao et al.
[14]
Automatically Selecting Follow-up Questions for Deficient Bug Reports
2021M. M. Imran, Agnieszka Ciborowska et al.
[15]
Learning to Identify Follow-Up Questions in Conversational Question Answering
2020Souvik Kundu, Qian Lin et al.
[16]
Can a Conversation Paint a Picture? Mining Requirements In Software Forums
2019James Tizard, Hechen Wang et al.
[17]
Extracting and Analyzing Context Information in User-Support Conversations on Twitter
2019Daniel Martens, W. Maalej
[18]
Assessing the quality of the steps to reproduce in bug reports
2019Oscar Chaparro, Carlos Bernal-Cárdenas et al.
[19]
Release Early, Release Often, and Watch Your Users' Emotions: Lessons From Emotional Patterns
2019Daniel Martens, W. Maalej
[20]
The ABC of Software Engineering Research
2018Klaas-Jan Stol, Brian Fitzgerald

Showing 20 of 33 references

Founder's Pitch

"FeedAIde enriches mobile app feedback by guiding users with smart follow-up questions, improving interaction between developers and users through context-aware AI."

Feedback EnhancementScore: 8View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

4/4 signals

10

Series A Potential

4/4 signals

10

Sources used for this analysis

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Why It Matters

The research addresses a key challenge in mobile app development: the gap between user-submitted feedback and developer needs. By enriching user feedback with context-aware follow-up questions, it reduces clarification cycles and enhances the quality of information developers receive.

Product Angle

FeedAIde can be commercialized as a modular SDK for mobile apps, enabling app developers to plug in advanced feedback capture technology without extensive customization.

Disruption

FeedAIde could replace traditional feedback forms in mobile apps that often produce vague, incomplete, or unstructured user responses.

Product Opportunity

Developers in the mobile application industry need effective feedback management tools to improve app quality and user satisfaction; hence, app platforms, developers, and enterprises focusing on customer experience are potential customers.

Use Case Idea

An API tool for mobile app developers to integrate into their apps, enabling automatic enhancement of user feedback, leading to better bug reports and feature requests without additional user effort.

Science

The paper describes FeedAIde, a system that asks app users intelligent follow-up questions to gather rich feedback. It integrates multimodal large language models to analyze context data like screenshots and user interaction logs to generate relevant queries, thereby refining feedback into detailed reports for developers.

Method & Eval

The FeedAIde iOS framework was tested with users of a gym app. They submitted feedback using both FeedAIde and a simple form, then evaluated through expert assessment of 54 feedback reports, showing improvement in feedback quality.

Caveats

The system's dependency on device information could raise privacy concerns, and high initial setup cost and integration effort for app developers might hinder adoption.

Author Intelligence

Walid Maalej

LEAD
University of Hamburg
walid.maalej@uni-hamburg.de

Ali Ebrahimi Pourasad

University of Hamburg
ali.ebrahimi.pourasad@uni-hamburg.de

Meyssam Saghiri

University of Hamburg
meyssam.saghiri@studium.uni-hamburg.de