Launching today
IPOGyani
AI-Powered IPO Insights, Live GMP & Subscription Data
10 followers
AI-Powered IPO Insights, Live GMP & Subscription Data
10 followers
POGyani is an AI-powered IPO analytics platform built for Indian retail investors. Track accurate live IPO subscription data, GMP updated every 5 minutes, upcoming and current IPOs, allotment dates, and AI-powered listing gain predictions. We built IPOGyani to make IPO research faster, transparent, and easier to understand without jumping between multiple platforms.
The GMP updates every 5 minutes is a really thoughtful touch, saved me from the usual scramble across Telegram groups and scattered websites. Clean layout too, feels like something built by people who actually trade IPOs themselves.
honestly this looks super useful for tracking indian IPOs in one place. one thing that would really help though is adding a side-by-side comparison feature, like being able to pick 2 or 3 IPOs and see their GMP, subscription numbers, and predicted listing gains all together instead of clicking through each one separately. would save a ton of time when youre trying to decide between applying or skipping
The GMP updates every 5 minutes are honestly super useful, no more guessing on sketchy third party sites. Clean layout and the listing gain predictions feel surprisingly accurate based on the few I checked.
One thing that would really help is adding a simple portfolio tracker where I can log my applied shares, allotted quantity, and listing day P&L all in one place. Right now I still have to dig through my broker app and compare it manually with the subscription and listing data you show. Tying those dots together inside IPOGyani would make it a one-stop shop for anyone seriously tracking IPO returns.
Finally a clean place to check live GMP and subscription numbers without opening five different tabs, the allotment tracker is genuinely useful.
The GMP updates every 5 minutes is genuinely thoughtful, since most competitors leave you refreshing tabs during the day. Clean execution for what looks like a pretty data-heavy product.