Painboard – AI-powered customer feedback analysis, humans in control

Hacker News

Hi HN, Excited to share with you what we've been building lately! We've observed a pressing issue that businesses regularly grapple with: they’re flooded with customer feedback — reviews, support tickets, etc. — yet the textual, semi-structured nature of this data makes it tough to dissect manually without substantial time investment. While AI has stepped up as a promising solution, the subjective nature of interpreting this data can reduce the utility of AI-generated reports. Every business has its own angle – the unique metrics and perspectives that define how they see success. All too often, users need to manually extract snippets from these reports and edit them on their own, a process that’s neither scalable nor efficient. That's where Painboard steps in, striking a balance between the expediency of AI analysis and the indispensable personal nuances of manual curation. Painboard harnesses the prowess of GPT-4 and cutting-edge NLP techniques to not only analyze but also offer a dynamic curation of insights - all within a familiar, Kanban-style board interface. Imagine scrolling through columns and orderly stacks of cards, each representing unique "themes" distilled from raw data, backed by relevant metrics like customer count. Want to group things? Drag and drop. Find two themes too similar and want to merge them? Drag and drop. Painboard isn't just about organizing data; it's about empowering you to interact with it. It mimics the human model of working with a team of people. You collaborate with it versus just completely outsource to it. Well, enough talking. Please check out the website where you can find a video showing Painboard in action: https://usepainboard.com/ I'd appreciate any feedback or criticism. If you have some data that you'd like to analyze, we'd love to create a board for you too.

Visit product View on Hacker NewsDec 12, 2023

Share card

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: model, apple, user · Missing: mac, agents, macos

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: reviews, interface, efficient · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, video, users · Missing: mobile apps, ios, entrepreneurs

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: collaborate · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

Painboard – AI-powered customer feedback analysis, humans in control44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Painboard – AI-powered customer feedback analysis, humans in control

Hacker News1

Sentient – AI-powered customer feedback analysis with 95% accuracy49%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Sentient – AI-powered customer feedback analysis with 95% accuracy

Hacker News2

kutuAI43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Automated Customer Feedback Analysis

Indie Hackers1ai

Feedgrip46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Get the most out of your customer feedback

Indie Hackers6ai

Caravel18%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Manages and classifies customer feedback

Indie Hackers

Qria28%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Customer feedback, understood

Indie Hackerscommitment-full-time

Symposium46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Customer feedback platform

Indie Hackers1b2b

Brandelp57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Customer feedback collecting tool

Indie Hackers1analytics

Luumy57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Customer Feedback made simple

Indie Hackers2communication

Canny

Keep track of customer feedback

BetaList

Browse all products Analyze your launch