Business GPT
Build on Data, Not Hallucinations — with an AI agent.
31 followers
Build on Data, Not Hallucinations — with an AI agent.
31 followers
Bunzee is an AI agent that helps you make better business decisions—grounded in real data. Instead of hallucinating, it tests your ideas against real competitor performance and customer feedback. From traffic and revenue to user sentiment, Bunzee surfaces where the real opportunities are, so you can act with clarity and confidence.
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I have some questions about the competitor data feature.
I noticed that it’s described as real, performance-based data, which is really impressive, but I’m curious about the sources being used for this. For example, which companies’ data are you referencing, and how reliable is it? I think knowing this would help users like me feel more confident in the insights.
I actually tried inputting one of my own ideas, and it seems to generate quite a lot of information, including competitor lists along with actual user metrics and revenue indicators. This is really interesting, but it also makes me wonder about the accuracy of the data. How can we be sure that the revenue numbers and user statistics reflect reality? It would be fantastic if there were a clear explanation of the basis for the actual revenue data, as that would make the insights much more actionable and trustworthy.
One more about the review data where exactly is it coming from? Is it being aggregated from multiple platforms, or is it sourced from a specific set of sites? I think it would be extremely helpful if details like this were documented more thoroughly, so users could better understand the foundation of the insights they’re seeing.
Finally, as you mentioned, I’m particularly interested in understanding whether the outputs are truly reflective of reality or if there’s any possibility of hallucinations by the AI. Knowing how you address this concern and how you ensure the reliability of the data would be really valuable. Overall, the feature seems extremely powerful, but getting more clarity on these points would help me and probably many other users, use it more effectively and confidently.
@golang_key Thanks for your thoughtful questions these are exactly the kinds of concerns we want users to have. While we can’t disclose every detail about our data pipeline, I can share a general overview of how things work.
For review-related data, we aggregate and analyze information from multiple real-world sources.
This includes platforms like app stores, Product Hunt, and GitHub (particularly issue and discussion data).
We collect this data through a combination of APIs and direct crawling, depending on the source.
Because the volume and variety of data are quite large, it’s not tied to a single platform but rather a combination of multiple inputs.
Regarding the competitor metrics you mentioned—such as user numbers and revenue estimates—these are sourced through APIs provided by specialized data platforms. These are paid services that offer access to real market data, and we’ve invested significantly in integrating them. As you might imagine, the cost of acquiring and maintaining this data is quite high.
You also brought up a very important point about hallucinations. We agree that providing clearer evidence and references alongside the insights would greatly improve trust and usability. Your feedback on this is extremely valuable, and we’ll definitely take it into account as we continue to improve the system.
Thanks again for sharing such detailed and constructive input—it really helps us make the product better.
Hi Product Hunters! 👋 I’m Bill, the maker of Bunzee.
Like many of you, I’ve had countless "Next Big Thing" ideas.
But every time I tried to validate them using standard AI, I hit a wall: Hallucinations.
Without hard performance data and real user reviews, the AI’s insights remained thin and unreliable.
I wanted a 'Business GPT' that doesn't just chat, but actually validates and generates deep, actionable reports for you.
Here’s how I’m using Bunzee 2.0 to stop the guessing game:
🔎 Find Real Pain Points: Stop assuming. Bunzee scrapes Reddit, X, and communities to find what people are actually struggling with.
📈 See What’s Working: Analyze trending apps on PH and App Stores to see the existing landscape before writing a single line of code.
📊 Validate with Hard Metrics: No more "I think this market is big." Check real competitor signals to see if the opportunity is real.
💡 Spot the Gaps AI Misses: By analyzing authentic user reviews, find exactly where competitors are failing their customers.
🚀 From Idea to Prototype: Once the data checks out, Bunzee helps turn that validated concept into UI mockups and a working prototype.
I’m so excited to share this with the community.
@shyunbill I’m curious about the scoring system....
It seems like the scores are measured on a scale of 0 to 100, and I’d like to understand what criteria are used for this measurement. It looks like AI is doing the scoring, so I’m interested in how this differs from traditional scoring methods. If there are any specific databases, sources, or unique criteria that your system uses to generate these scores, I’d love it if you could share a bit about that as well.
@chris_green0742 Yes, the scoring is indeed generated by AI. And the evaluation is based on five predefined criteria that we have established
User scale
Revenue scale
Competitive intensity
Technical difficulty
User familiarity
What makes our approach different from traditional scoring AI service is that the scores are heavily grounded in competitor data. For example, if competing services have very low traffic or revenue, or conversely, if they are performing exceptionally well, those factors are directly reflected in the score.
So, it would be most accurate to think of this as a scoring system driven by competitive benchmarking data.
It seems like you’re also using Product Hunt data. I’m curious what criteria do you use to pull data from Product Hunt? Since Product Hunt has a huge amount of data, is there a specific way you select what to include? For example, do you only take the ones that have gained attention, do you sort them by votes, or do you analyze everything comprehensively?
@kaihairan Great question!!! Product Hunt does have an overwhelming amount of data, and not all of it is equally useful.
Recently, we’ve observed that more than 300 products are launched on Product Hunt Daily. However, many of these products are not actively maintained or may not function properly. Because of this, we apply a filtering process and focus only on the top 10% of products.
For example, we might take the top 30 products from a given day’s rankings, and we also aggregate data on a weekly and monthly basis. Since the Product Hunt API is well-structured, we primarily use vote count as the main criterion for ranking and filtering. In short, rather than analyzing everything indiscriminately, we prioritize high-performing and actively engaged products based on vote-based rankings.
Just signed up on the free plan and the Reddit trend source immediately caught my attention, seeing real pain points ranked by problem score and upvote volume is exactly the kind of validation I was missing when I started building.
As an indie iOS maker, I'm curious: can you filter Reddit trends specifically by mobile app opportunities, or do you have to manually scan through the categories yourself?
@misbah_abdel First, the score for the Trend Source is based on whether the problem can realistically be turned into a solution as an app or web product. We’re not currently evaluating whether it’s specifically a mobile app opportunity.
However, like you mentioned, it seems totally possible to distinguish between mobile apps and web products, and it’s something we could probably ship pretty quickly.
Is the feature you’re looking for something like separating categories so you can view opportunities by web vs. mobile app?
@shyunbill Exactly that, a simple filter like 'Mobile App' vs 'Web Product' vs 'AI Tool' would make scanning so much faster, and as an iOS maker I'd go straight to the mobile filter every time and ignore the rest.
@misbah_abdel Okay that's no problem.
Hi Product Hunters! 👋 I’m the designer behind Bunzee 2.0.
Designing a simple chatbot is one thing. But designing a workflow-driven chat that actually validates business ideas? That was a completely different beast.
As a designer, I kept hitting the same questions: What kind of experience do builders actually want? Especially on mobile how do we deliver complex market data without overwhelming the user? To be honest, there was no "right answer" or perfect reference.
We ended up building, using, and breaking our own designs over and over. Our goal was to balance deep, data-driven insights with a light, accessible interface. We spent countless hours scouting references, trying to find that sweet spot where Hard Metrics (Revenue, Traffic) and User Reviews feel intuitive, not exhausting.
Bunzee 2.0 is still in its early stages, just like many of your projects. We know it’s not perfect yet. We’re constantly tweaking the flow to make sure the "Agentic AI" feels like a partner, not just a tool.
I would love your professional eyes on this! If anything feels clunky, if the data is hard to read, or if the mobile experience feels "off", please let me know. Your visual and UX feedback is exactly what we need to make Bunzee better.
Bill, we’ve come a long way since 1.0! Excited to see how everyone interacts with the new design.
Finally, Bunzee 2.0 is out! (A note from the co-builder)
Hi Product Hunters! I’m the one who’s been in the trenches with Bill building Bunzee.
To be honest, building 1.0 was easy, it was just another chatbot scraping basic info. But we quickly realized: Generic AI advice is useless for real business. We saw the hallucinations, the thin insights, and we knew we had to pivot.
The transition to Bunzee 2.0 was brutal. Moving from simple "Chatting" to a real Agentic AI workflow that actually anchors to market truth? It was a massive technical headache. We spent countless nights fighting to integrate hard metrics (Revenue, Traffic) and real user reviews just to kill those damn hallucinations.
There were so many moments where we didn't know if we could keep up with how fast AI is changing. But seeing it finally generate validated reports and prototypes from real-world data... it makes all that grinding worth it.
We’re not just launching a GPT; we’re launching a workflow I’m actually proud to use.