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Kehai

Cookieless web analytics with a forecast that grades itself

Kehai is cookieless web analytics hosted in the EU. Reports are counted from recorded events without sampling, and imported or modeled numbers keep their own labels. The forecast tests itself on your history and shows its error next to the answer. Pacing tells you mid-month whether a target is still in reach. Sales and refunds come from the shop itself, AI referrals and bots get their own reports, and a read-only MCP server lets your assistant ask.

Top comment

Hey Product Hunt! 👋

I'm Mikołaj, the maker of Kehai. Today I'm launching it here: cookieless web analytics with a forecast that grades itself, run from Warsaw and hosted in the EU.

What can you do with Kehai?

📈 Forecast a goal's completions or its net revenue, with the forecast's own error next to the answer

🎯 Set a target for the month or quarter and see the run rate you need beside the one you have

💶 Get revenue from the shop itself: confirmed sales and refunds from Stripe, WooCommerce, PrestaShop, Wix, or Squarespace

🤖 See which AI assistants send you people, and every excluded bot with the rule that caught it

🔌 Let Claude, Cursor, or VS Code read your reports through a read-only MCP server

📦 Bring your history from GA4, Plausible, or Umami, with its origin on every day

🪶 Run all of it from a 2.5 kB script, with no cookies and nothing in browser storage

✨ How does the forecast work?

It forecasts one outcome: a goal's completions or its net revenue. Before it answers, it tests itself on your own history against the simplest baseline there is, repeating last week. Next to the forecast you see its skill and how often its 80% band actually held. With too little history it refuses and says what's missing.

The same rule runs through the whole product: counted, imported, and modeled numbers never share a column, and nothing is sampled.

🎯 Motivation

I've worked in digital marketing for 15 years, and today's popular analytics tools are why I built Kehai. Their scripts are heavy, and I trusted their reports less every year: sampling, thresholds, and modeled numbers mixed in with the counted ones. I wanted analytics where every number says how it was made: counted, imported, or modeled.

💬 Good to know before you click

- It's paid and built for businesses, from €49 a month. Pacing, MCP, and imports are in every plan. The forecast starts at Growth, revenue reports at Commerce.

- Every plan starts with 7 days free, and Checkout asks for a card.

- For Product Hunt: PRODUCTHUNT30 takes 30% off your first month on a monthly plan, until October 17.

- There's no session replay, no heatmaps, and no user profiles. Use Hotjar for that. We won't build it.

- It's hosted only. Not open source.

👀 Look around without an account

The demo opens on two years of generated shop data, with the forecast, pacing, and revenue full: https://kehai.io/sign-in?demo=1

Thanks, everyone! I'd love to hear what you'd check before trusting a forecast, and where the counting is unclear.

I'm here all day.

Best,

Mikołaj

About Kehai on Product Hunt

“Cookieless web analytics with a forecast that grades itself”

Kehai was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #142 on the daily leaderboard. Kehai is cookieless web analytics hosted in the EU. Reports are counted from recorded events without sampling, and imported or modeled numbers keep their own labels. The forecast tests itself on your history and shows its error next to the answer. Pacing tells you mid-month whether a target is still in reach. Sales and refunds come from the shop itself, AI referrals and bots get their own reports, and a read-only MCP server lets your assistant ask.

On the analytics side, Kehai competes within Analytics, Marketing and Privacy — topics that collectively have 655.5k followers on Product Hunt. The dashboard above tracks how Kehai performed against the three products that launched closest to it on the same day.

Who hunted Kehai?

Kehai was hunted by Mikołaj Salecki. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.

For a complete overview of Kehai including community comment highlights and product details, visit the product overview.