VisitorType
Google Tag Manager
DataOrganizer.io
Matomo
PostgresML
Talk To Your Data App
Pinecone
ChatWithCloud AI
TalktoData AI
Korvus
SuperDuperDB
Hugging Face
VisitorType is one snippet that tells humans, AI crawlers, and AI assistants apart — verified by IP, not just the user-agent string anyone can spoof. Once you know who's really visiting, you can act on it: fire your ad pixels for humans only so you stop paying to retarget bots, serve AI crawlers structured facts about your pages, and finally see the AI referrals GA4 buries in "Direct" (the ones that convert ~4.4× organic search). It's not another dashboard that just watches your traffic — it's the action layer that lets you do something about it. Free during early access, and early signups are grandfathered at Pro level for good.
VisitorType PostgresMLNo features have been listed yet.
VisitorType's answer
VisitorType doesn't just detect AI traffic — it verifies it and lets you act on it. Every visit is classified (human, AI crawler, AI search bot, assistant fetch, or agentic browser) and claimed bot identities are checked against the vendor's published IP ranges, so a fake "GPTBot" gets caught instantly. Then a GTM-style rules engine fires different tags per visitor type: ad pixels for humans only, clean analytics, canonical facts for AI. On our own site, ~2/3 of verifiable AI-bot claims turned out to be impostors — most tools would have counted them as real.
VisitorType's answer
A solo founder kept seeing "Direct" traffic that wasn't people — it was ChatGPT reading pages for users, crawlers training models, and scrapers wearing Googlebot's name. Existing analytics couldn't see any of it, and tag managers treated it all as human. VisitorType was built to answer one question honestly — who is actually on your site? — and then let you serve each visitor type differently. The site runs its own product: every claim on it comes from our own dashboard.
VisitorType's answer
Detection-only tools tell you bots visited; tag managers assume every visitor is human. VisitorType is the only layer that does both: verified classification plus per-type tag firing, with server-side collection that catches the crawlers JavaScript analytics can never see. It's privacy-first by construction — no cookies, no fingerprinting, GPC honored, EU-hosted — and installs in five minutes via one snippet, a WordPress plugin, or a Shopify app embed.
VisitorType's answer
Marketers and site owners who suspect their analytics no longer reflect reality: marketing teams at SMB SaaS and content sites, digital agencies reporting to clients, and WordPress/Shopify owners. No developer required — if you've used Google Tag Manager, you already know the mental model.
VisitorType's answer
A dependency-free JavaScript snippet (~3 kB) for browsers, server-side adapters for WordPress, Shopify, Cloudflare Workers, and Node, and a detection engine that combines a curated agent registry, behavioral signals, and daily-refreshed IP-range verification from vendor-published sources — all EU-hosted with a cookieless, no-fingerprinting architecture.
VisitorType's answer
We're in early access ahead of our public launch — current users are digital marketing agencies, WordPress and Shopify site owners, and SaaS marketing teams Early-access accounts keep Pro-level features free; case studies will be published as customers opt in
Based on our record, PostgresML seems to be more popular. It has been mentiond 7 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
The web service supports generating embeddings from OpenAI and Ollama AI models. It also provides a fallback for users without access to AI models running on a remote server through PostgresML. - Source: dev.to / over 1 year ago
That's outside of the database, though. This is more like what I had in mind -- I just found it: https://postgresml.org/. - Source: Hacker News / over 2 years ago
Some excellent tools were created to represent these tasks "naturally" in SQL and even let most of the computation happen inside the database. PostgresML is a great example. It's built above PostgreSQL and provides a set of functions that allow you to train and use machine learning models with SQL. Here's how you can train a classification model for the classic handwritten digit recognition problem:. - Source: dev.to / over 2 years ago
PostgresML | You know Postgres. Now you know machine learning – PostgresML. - Source: dev.to / over 2 years ago
You can swap in almost any open-source model on Huggingface. HuggingFaceH4/zephyr-7b-beta, Gryphe/MythoMax-L2-13b, teknium/OpenHermes-2.5-Mistral-7B and more.If you haven't seen us here before, we're PostgresML, an open-source MLOps platform built on Postgres. We bring ML to the database rather than the other way around. Source: almost 3 years ago
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