PriceProven
Track daily prices to verify if discounts are real.
🚀 Built in 11 days: PriceProven hits 7,000+ views and helps early users dodge fake discounts!
Just 11 days into coding and development, I launched PriceProven—a tool designed to analyze and track the true historical prices of products. The organic response has been much faster than I anticipated:
🔥 7,000+ online page views (and still climbing). 🔥 6 initial orders secured completely organically.
But what I'm most proud of isn't the traffic metrics; it's the actual money saved. PriceProven has already helped these early users spot artificially inflated prices, saving them from throwing money away on deceptive marketing and fake sales.
Instead of building a flashy but empty interface, I designed PriceProven for high information density. There is no useless white space—just raw data, historical price curves, and absolute transparency so you can make a confident purchasing decision at a single glance.
The core engine is running smoothly, but I want to make it even sharper. I’d love for the community to drop by, test it out, and give me some raw, unfiltered feedback (especially on the data visualization and UI/UX).
👉 Try it out and roast the UI here: https://priceproven.com/#pricecheck
Thanks so much for the support! 💡📉
Hey Indie Hackers 👋,
When you build a price-tracking tool like PriceProven, the obvious growth spikes are Black Friday and Amazon Prime Day. But relying solely on seasonal hype is a trap.
Coming from a background of building automated trading bots, I’m used to systems that run 24/7 based on raw, high-frequency data. I want PriceProven to have that same everyday utility—not just be a holiday novelty.
So, I’m shifting my focus to an "evergreen" SEO strategy, specifically targeting pre-purchase search intent.
Instead of just waiting for sale events, I’m mapping out the everyday questions shoppers ask before hitting "checkout":
"Is this discount real?"
"Current price vs. historical low for [Product X]"
"Has this price been artificially inflated?"
The goal is to capture users when they have high intent but are facing weak, marketing-heavy search results.
Once they land on PriceProven, they don't get fluff. They get exactly what I'd want to see: a terminal-style, high-density data dashboard. No unnecessary empty space. Just the raw historical price curve, confidence metrics, and if we don't have enough history to verify the discount, the system explicitly throws a "Not Enough Data" flag. Absolute transparency.
My question for the community: For those of you building data-heavy platforms, have you experimented with Programmatic SEO to generate pages based on your datasets? How do you balance indexing millions of product data points without tanking your crawl budget?
Would love to hear your experiences and strategies!
Meta Description: Retailers often inflate prices before big sales. Learn how to use historical price data to spot deceptive discounts and shop with 100% confidence.
Introduction We all love a good bargain. When Black Friday or Amazon Prime Day rolls around, seeing a "50% Off" tag triggers an instant rush to click "Add to Cart." But have you ever wondered if that original price was artificially inflated just days before the sale? The hard truth is: deceptive pricing is incredibly common. To actually save money online, you need to stop trusting the marketing tags and start trusting the data.
1. The "Pre-Sale Mark-Up" Trap A common tactic used by retailers is slowly raising the price of a product over a few weeks, only to "slash" it back to its normal price on the day of a big sale. You feel like you're getting a massive discount, but you are actually just paying the standard retail price.
2. Why You Must Track Historical Data You cannot beat the algorithm without your own data. To know if a discount is genuine, you need to see a product's price history over the last 30, 60, or 90 days. If the price was steady at $100, jumped to $150, and is now "on sale" for $100—that is a fake discount.
3. The Solution: Enter PriceProven This exact frustration is why we built PriceProven. We process massive daily pricing datasets to bring absolute transparency back to the consumer.
Instead of relying on browser extensions that clutter your screen, we designed our dashboard with high information density in mind. With one glance, you get the raw metrics that matter:
Daily Price Charts: Instantly view the true historical price curve of any tracked item.
Fake Discount Detection: Easily spot the exact moment a price was artificially inflated.
4. Demand Total Transparency Not every product has months of data available. A trustworthy tool should tell you what it doesn't know. At PriceProven, we prioritize absolute honesty. If a product doesn't have enough tracking history to verify its discount, our system explicitly labels it as such. No fluff, no guessing.
Conclusion Stop shopping blind. Before you check out that next "huge deal," verify the numbers. Empower yourself with real historical data and never fall for a fake sale again.
[Call to Action Button] 👉 Try PriceProven for Free Today
-
1
The part about showing when the price actually jumped before a “sale” is what makes PriceProven click for me. With $100K/mo already, this feels like it could be pulling in a lot more organic traffic than it probably is right now. That kind of existing traction makes the gap pretty interesting. Are you seeing people use it more for everyday shopping or the big sale events?
-
1
Hi Muhammad, thanks for the kind words and for checking out the post! Just to clarify a small point—we are actually still in the pre-revenue stage, so we aren't hitting $100K/mo just yet (though that’s definitely a great milestone to aim for!).
Regarding usage, the initial hook for most people is definitely the big sale events like Black Friday or Prime Day, as that's when the "fake discount" anxiety hits hardest. However, once they see how the raw data exposes everyday pre-sale mark-ups, we're seeing them run regular checks on standard purchases to make sure they aren't buying at the top of a price curve. The goal is to shift that behavior into an everyday shopping habit
-
1
That actually makes the SEO opportunity more interesting. Black Friday and Prime Day give you the obvious spikes, but the everyday-shopping use case gives you a much bigger evergreen search layer.
I’d map the questions people ask before buying — things like checking price history, whether a discount is real, when to buy, and whether a current price is actually low — then build around the ones that have strong intent and weaker SERPs. That could help turn the seasonal hook into year-round discovery.
That’s the kind of search-intent research I work on, so I’d be happy to share a few angles I’d test for PriceProven if useful.
-
1
Hi Muhammad, that is such a brilliant perspective! You hit the nail right on the head! 🤩
I completely agree with you about the 'evergreen' SEO opportunity. Capturing those pre-purchase questions to turn seasonal spikes into year-round discovery is exactly the kind of sustainable, long-term growth we want to aim for.
I'm incredibly excited and would be super grateful if you could share some of those search-intent angles! Your expertise is exactly the kind of insight PriceProven needs to optimize right now.
What's the best way to connect? Feel free to drop them here in the thread, or shoot me a DM on X (Twitter) if that's easier for you. Thanks so much for the enthusiasm and the top-tier insights! 🙌
-
-
-
Hey Indie Hackers 👋,
I usually spend my time architecting data-heavy applications and processing massive datasets (like training XGBoost models for arbitrage bots). But recently, I got fed up with falling for "50% off" e-commerce deals that were artificially inflated just days before.
I realized the only way to beat deceptive marketing is with the same raw, hard data approach used in trading. That’s why I built PriceProven.
The Data Architecture: Tracking and recording daily price changes across a massive catalog of products requires a highly optimized, scalable backend. The core challenge wasn't just scraping; it was structuring the database to log daily historical snapshots efficiently without blowing up server costs or slowing down query times when a user searches for a product's history.
The UI/UX Philosophy (Against the Grain): When it came to the frontend, I deliberately went against the modern trend of overly spacious, minimalist web design.
I built the dashboard with a dark-mode, terminal-style aesthetic. My primary focus was high information density. I stripped out all the unnecessary empty space. When a user looks at PriceProven, I want them to see the entire historical price chart, the confidence metrics, and the data labels at a single, comprehensive glance.
If a product doesn't have enough tracking history, the system explicitly flags it. No fluff, no marketing tricks—just a raw, dense data terminal for smart shoppers.
We are currently in the pre-revenue stage. I'm curious: for those of you building heavy data-logging tools, what database structure do you prefer for massive daily time-series data?
Let me know your thoughts!
-
1
The transparency angle comes through clearly across both posts. Curious how people react when the tool tells them there isn't enough history to verify a discount.
-
1
Thanks Aryan! Honestly, the reaction has been very positive. Because the dashboard is built to feel like a raw data terminal with high information density, users actually appreciate the blunt honesty. When they see a 'Not Enough Data' flag, it reinforces the idea that we aren't just guessing or throwing fluff at them to keep them engaged. It sets an expectation of absolute transparency and builds a lot of trust right out of the gate.
-
1
That’s an interesting trust signal. People accepting “Not Enough Data” rather than expecting an answer says a lot about how the transparency is being perceived. If you’re open to continuing the conversation, what’s the best email to reach you at?
-
1
Thanks, Aryan! I appreciate that. I try to keep my inbox light, but I'm happy to continue the conversation right here in the comments. What did you want to discuss?
-
1
That’s fair. I was mainly curious about how you’re thinking about the business as it develops, but happy to keep it here.
-
1
Awesome! I'm an open book and would love to discuss the business side of things right here.
Right now, since we're in the pre-revenue stage, my absolute focus is on nailing the data architecture and building a core base of users who truly value this raw, high-density transparency approach.
As the business develops, I'm thinking about a freemium model—keeping the core historical tracking and 'Not Enough Data' flags completely free for everyday shoppers, but potentially offering premium features like real-time drop alerts, custom tracking lists, or even an API for power users down the line.
I'd love to hear your take on it! What specific aspects of the business development were you most curious about? Any models you've seen work well for this type of data-heavy tool?
-
1
That’s exactly the kind of thing I’d rather discuss properly than unpack in a comment thread. What’s the best email to reach you on?
-
-
-
-
-
-
Comment