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AI Model for Fast Text Judgments at 23andMe

23andMe utilizes Jev, a System One AI model, to evaluate text for voice, clarity, and accuracy. This approach allows for rapid assessments, significantly reducing costs and speeding up the review process.

added by @frederikhermann

The most useful AI model we started using this month can't write a single sentence.

Generating content is cheap now. Judging it is the bottleneck.

So at @23andMe we started using Jev from @typesafeai, a "System One" model. It doesn't generate text. It makes fast, structured judgments about text and tells you how confident it is.

We took hundreds of our published articles and used them to define what great looks like for us: voice, clarity, scientific accuracy, how we talk about health. That became a rubric. Every new piece gets scored against it before an editor sees it. Clear passes move faster. Anything uncertain goes to a person. Anything health-related still goes to our reviewers, always.

Why not just use a frontier model for this?

Cost and speed. LLMs charge by the token. Ask one to grade something and it writes out its reasoning, so you pay for the essay too. Multiply by every draft, every variant, every criterion. The bill compounds and each answer takes seconds.

A #SystemOne model skips the writing. It returns a verdict, not an essay. Almost no output to pay for, answers in under half a second. In Every's independent test, one editorial check with #Jev ran ~25x faster and ~580x cheaper than a frontier model.

That changes the math. Review stops being the expensive step you skip under deadline and becomes something you run on everything. Frontier models still do the writing. Humans still make the final call.

The model is a commodity. Our standards are the asset.

That was my argument on the "AI Leadership and the Metrics That Matter" panel at the @ThinkingAI_io Agentic Growth Summit: the biggest value of AI in marketing isn't making more content. It's checking everything against a source of truth, every time, at a cost that makes skipping review pointless.

Jev: https://t.co/S1SHulBaPM
Every's test: https://t.co/CsE5kX82Tt

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Evaluation of Jev Model for SEC Filings and Fact Extraction

added by @RegenbaumShaun

The Jev model was tested on SEC filings and fact extraction tasks, showing strengths in narrow yes-or-no questions but weaknesses in understanding document context. It performed well as a first-pass filter but lagged behind production models in accuracy.

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Optimized Content Workflow with Claude Projects

added by @siliconvalleymm

The content team implemented separate Claude projects for each platform, including podcast, LinkedIn, Instagram, and YouTube, resulting in improved performance over previous methods. Each project includes a brand dossier, voice profile, and an Anti-AI Style Guide.

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AI Feedback System Test Results with Jev

added by @ChrisDiNicolas

Jev was tested in an AI customer feedback system using 1,040 GitHub issues, proving to be 98% cheaper and 84% faster than Claude Sonnet 4.6 while maintaining higher accuracy.

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AI SEO Rainmakers Community Experience

added by @cagrisarigoz

Joined the AI SEO Rainmakers community and utilized the Custom GPT tool to audit websites, uncovering 4-5 high-value action items. Engaged with classes and community posts for additional insights.

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Automated Outbound Prep with Claude Code

added by @michaelcnjoku_

Automated outbound preparation reduced time from 6 hours to under 1 hour weekly. The system scrapes, enriches, and filters 3,000 contacts, ensuring quality through a QA gate.

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Implementing Jev in AI Agent Fleet

added by @FloRyRy410

A decision layer named Jev was integrated into an AI agent fleet, achieving high accuracy and low latency for decision-making tasks. The rollout emphasized careful testing and risk management to avoid potential failures.

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