Roundtable (Proof of Human) records 854 monthly visits with -60.5% month-over-month growth and Contact for Pricing pricing. TypeSafe AI - Jev records 6,148,688 monthly visits with -36.4% growth and Paid pricing.
Prove every user is real — invisibly and continuously.
Machine-native AI that returns typed, calibrated decisions for software automation
| Dimension / Metric | | |
|---|---|---|
| Official Domain | roundtable.ai | typesafe.ai |
| Pricing Model | Contact for Pricing | Paid |
| Monthly Visits | 854 (854) | 6.15M (6,148,688) |
| MoM Traffic Delta | -1.31K (-60.5%) | -3.51M (-36.4%) |
| Primary Market & Share | 🇺🇸 United States (100%) | 🇺🇸 United States (26.4%) |
| Direct Visits / mo | 336 | 3.14M |
| User Rating | 3.9 | 4.2 |
| Supported Platforms | Website | Website |
Prove every user is real — invisibly and continuously.
Proof of Human runs silently behind login and checkout flows, scoring behavior in under 200 milliseconds without CAPTCHAs or friction for real people.
Continuous behavioral models look for programmatic typing, teleporting mouse movement, jump scrolling, centered clicks and pasted text to catch bots and AI agents mid-session.
The service states it collects no PII and does no cross-site tracking, helping teams meet compliance and privacy requirements.
A single script tag with a site key and user ID is enough to begin verifying sessions, and results include an example session report with explainable signals.
Machine-native AI that returns typed, calibrated decisions for software automation
Jev outputs typed decisions that application code can act on directly, rather than prose that must be parsed and interpreted.
Every decision carries an estimate of how confident the model is, so you can set thresholds for autonomous action versus human escalation.
TypeSafe reports Jev at roughly 193x faster and 444x cheaper than LLM-based workflows on comparable System One tasks, with a price of $42 per billion input tokens.
Because confidence estimates expose uncertainty, Jev is designed to avoid hallucination-style failures in automation, enabling zero-hallucination decision paths where confidence is high.
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