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TypeSafe AI
An AI lab whose System One model, Jev, returns typed, calibrated decisions software can act on
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2.2MB · 11,362px tall · uses Times, Die Grotesk C Medium, LisaTerminal Paper 2X3Y Medium
Industry
AI Infrastructure, Devtools
Style
Brutalist, Typographic, Maximalist
Stage
Series A
Built with
Framer
About the site
TypeSafe AI is an AI lab building System One models, a class of model meant to be used by machines rather than chat. Its first, Jev, returns typed decisions with calibrated confidence for automation, at a fraction of an LLM's cost and latency. The homepage looks like a retro operating system: grey terminal-style nav buttons and a 'TypeSafe News' window announcing its a16z-led Series A sit over a cloudy gradient, above a giant headline calling Jev 'The First (Public) System One Model'. Below, a dithered pink desktop of draggable windows compares model families and prices, a manifesto argues 'We Took The Opposite Research Direction', and a huge '193.6x Faster, 444.6x Cheaper.' leads into a side-by-side race against an LLM. Pixel charts cover confidence thresholds, accuracy against cost, hallucination rates and per-token pricing, before a hiring band, a green blog section with a customer case study and a dark FAQ. A useful reference for making a deeply technical model launch feel playful and ownable through one committed visual metaphor.
How this page sells
For engineers automating decisions with LLMs: name a new model category, then prove it with speed, cost and error-rate charts against the big labs.
Primary navigation
- Enterprise
- Thoughts
- Company
- Docs
- API Console
- Contact Us
ClickTap a section to see it on the page
A retro 'TypeSafe News' window ('We raised a really big Series A led by a16z') floats over clouds, above 'The First (Public) System One Model; Jev Gives AI The Properties Of Code' and Create Account / Join Discord links.
Why it converts: Coining 'System One Model' gives Jev its own category instead of a slot in the LLM rankings, and the funding note lends weight without a logo wall.
A dithered pink 'TS.AI.OS1' desktop stacks windows for LM, LLM, RLHF, RLVR and RLCD over a table putting Jev at 193.6x for $0.39 against Claude and GPT models, with a clock and a Game of Life widget.
Why it converts: Turning model history into a playful desktop shows deep technical fluency, and it makes the page memorable to an audience used to sterile AI launches.
'We Took The Opposite Research Direction' argues RLHF made chat models overconfident and unreliable, needing humans in the loop, then lists Decisions Not Strings, Calibrated Confidence and More Like Code.
Why it converts: Blaming the training method rather than the models explains why bigger LLMs won't fix automation, which makes a new approach believable.
'193.6x Faster, 444.6x Cheaper.' in huge type, then a 'String Tax' race between TypeSafe and an LLM terminal: $0.000081 in 0.114s against $0.013880 in 8.566s, with a link to the real video.
Why it converts: Two multipliers and a head-to-head run make the economic case in seconds, and the video link offers proof to anyone who doubts the numbers.
A 'Jev.Confidence' window grids decisions shaded by confidence with a '03 / 40 PASS' threshold readout, beside copy on setting when Jev acts autonomously and when it asks for review.
Why it converts: Showing thresholds as a control the developer owns turns calibrated confidence into an obvious workflow benefit, not a research term.
'Jev's Intelligence Per Dollar Is Literally Off The Charts.' over a Pareto chart, a structured-output error table with Jev at 0.00%, and price bars ending in '$42 per billion input tokens' and '238x lower input price'.
Why it converts: Benchmarking against named OpenAI, Anthropic and Google models on accuracy, errors and price answers every comparison an evaluator would run first.
A large TypeSafe AI wordmark sits above 'Come Build With Us' and an 'Open Roles' link on the pink grid.
Why it converts: A confident hiring call signals a growing company, which reassures buyers adopting a brand-new model.
A green blog section leads with 'How Jack & Jill Cut Candidate Screening Costs By 88% With Jev', beside the launch post and 'Jev: For The Builders AND Dreamers'.
Why it converts: A named customer with a specific cost cut is the proof a cautious buyer needs after a page of benchmarks.
A dark 'We Give A FAQ' asks the skeptic's questions: Is Jev just a smaller LLM? How is it different from JSON mode? Are these prices subsidized? Can Jev still get things wrong?
Why it converts: Raising the doubts an engineer already has, including subsidized pricing, shows confidence and keeps the evaluation going instead of ending it.
