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Laya AI is the free Jev alternative.
It does the same job as Jev, it does it about seven times faster, and on most tests it gets more answers right.
And you can run it yourself for free.
Let me explain what that means, because most people have never heard of Jev, and even fewer have heard of Laya.
What you're watching: a real billing email on my Mac. Laya reads it once and answers all four questions together in 52.7 ms — department "billing", how urgent, will they leave, do they want a refund.
This month the daily tutorials walk through putting Laya or Jev in front of your main agent — so your Claude, Hermes or OpenClaw only does the heavy thinking when it needs to.
What you're watching: the same billing complaint in Hindi, Thai and Khmer, on my Mac. Left: the English model alone can't read it. Right: the Router checks the language first, sends it to the multilingual model, and gets "billing" every time.
What you're watching: six messages flipping between languages on my Mac. Out of the box, every language switch reloaded a model — about 20 seconds per answer here. With all models loaded at the start, the same messages took about 19 ms.
from laya import Router router = Router(preload=True)
What you're watching: the same 60 test emails Jev sorted for me two days ago, now through Laya with no training. Jev got 54 of 60. Untrained Laya got 36 and 28 — and never once claimed to be 85% sure. It knew it was guessing.
What you're watching: 24 made-up contact-form messages (no real people) sorted by untrained Laya on my Mac — about 57 ms each against Jev's 392 ms. Laya got 16 of 24, Jev got 24. Fast already. Sharp after training.
What you're watching: one article brief, five possible websites, picked on my Mac in milliseconds. Across 10 test briefs untrained Laya picked the right site 8 times; Jev picked 10.
Members post their wins every day — agency owners, ecom founders, course creators, solo operators across 38 countries.
Read the 158-page wins doc →What you're watching: Laya's built-in preset scoring eight test requests on my Mac — how hard is this, from 0 to 3. "Is this email spam?" scores lowest. The database migration scores highest. That score decides which agent gets the task.
No. The everyday 90% runs on free local models on your own machine, free APIs slot in for more, and the big work goes through the CLIs you already pay for — your Claude subscription already includes the Claude CLI.
A decision layer like this one is exactly how the small choices stop eating tokens. And inside the Boardroom there are full token-efficiency tutorials.
The lead sorting for the agency. The content routing across the sites. The safety check on inbound before an agent acts on it. All of it gets shared inside the AI Profit Boardroom as it's built.
