Type.com records 18,668 monthly visits with -21.6% month-over-month growth and Paid pricing. TypeSafe AI - Jev records 6,148,688 monthly visits with -36.4% growth and Paid pricing.
Agent automations your whole company can run
Machine-native AI that returns typed, calibrated decisions for software automation
| Dimension / Metric | | |
|---|---|---|
| Official Domain | type.com | typesafe.ai |
| Pricing Model | Paid | Paid |
| Monthly Visits | 18.67K (18,668) | 6.15M (6,148,688) |
| MoM Traffic Delta | -5.13K (-21.6%) | -3.51M (-36.4%) |
| Primary Market & Share | 🇺🇸 United States (57.8%) | 🇺🇸 United States (26.4%) |
| Direct Visits / mo | 11.94K | 3.14M |
| User Rating | 4.5 | 4.2 |
| Supported Platforms | Website | Website |
Agent automations your whole company can run
Each agent runs in its own sandboxed cloud VM with a filesystem, shell and browser, so one automation never interferes with another. Schedules, webhooks and events start work automatically, and runs are checkpointed and retried when an API times out.
Every turn runs a full harness — Claude Code, Codex, OpenCode, Gemini CLI and more — with tool calling, a terminal and a file system, combined with deterministic script steps for calculations and formatting.
Connect 1,000+ business tools via OAuth, MCP or custom APIs once, then share controlled access with agents, automations, apps and teammates, with permissions set by user, Space, role and tool call.
See inputs, steps, outputs, duration and cost for every run, catch failures, follow automatic retries and watch AI spend broken down by Space, automation and person.
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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Google's rapid prototyping IDE and developer API workbench for building multimodal applications with Gemini models.
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