NeuralFeedback

Simulate and improve upon user feedback before launch.

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Inspiration

As creators and aspiring entrepreneurs, we often find ourselves brimming with product ideas but struggling to get quick, actionable feedback. Gathering critiques from a diverse group of people is time-consuming and difficult, especially when trying to reach a wide range of demographics. We wanted a tool that could simulate this process instantly, giving meaningful insights to help refine and improve ideas efficiently.

What it does

NeuralFeedback allows users to input a product idea through text, optionally upload supporting documents, and specify their target demographic. Using this information, the app leverages the Gemini API to generate multiple AI reviewers, each with unique personalities and demographic characteristics. These AI reviewers provide individual critiques, highlighting strengths, weaknesses, and potential concerns from the perspective of different customer types.

Users can interact with each reviewer through a chat interface, receiving personalized guidance on how to improve their product. As the user implements changes in real-time, the AI reviewer can reassess and provide updated feedback.

Additionally, NeuralFeedback offers a summary feature that aggregates all reviews into key insights, showing the general sentiment and main areas for improvement for your target audience. For users who prefer conversational interactions, the app also includes a call feature powered by Eleven Labs, allowing a human-like AI reviewer to discuss the product in a natural, spoken dialogue.

How we built it

We built NeuralFeedback using:

  • Python & Flask for the backend server and web interface

  • Gemini API for generating AI reviewer content

  • Eleven Labs for human-like voice generation in the call feature

Our architecture combines real-time text and voice feedback with AI-powered summarization to provide both detailed critiques and high-level insights.

Challenges we ran into

  • Managing multiple branches and merges became complex as each team member worked on different features simultaneously.

  • Ensuring the AI-generated feedback was coherent and contextually relevant required careful prompt engineering and iterative testing.

  • Handling uploaded documents of varying formats (PDF, DOCX) and integrating them into the AI workflow posed some unexpected technical hurdles.

Accomplishments that we're proud of

  • We successfully created a multi-persona AI reviewer system that can simulate diverse customer feedback.

  • Built a real-time chat and call interface that allows users to interact naturally with AI reviewers.

  • Delivered a fully functional prototype within the hackathon timeframe, combining multiple advanced technologies seamlessly.

What we learned

  • How to effectively prompt AI models to generate targeted and realistic critiques.

  • Best practices for merging collaborative code and managing branches in a large project.

  • Techniques for integrating voice synthesis with text-based AI interactions.

What's next for NeuralFeedback

  • Expand AI reviewers to cover more nuanced demographic and psychographic traits, improving the realism of feedback.

  • Explore integration with prototyping tools, so users can get feedback on wireframes and mockups in addition to written ideas.

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