People use AI. They don't believe it.
llaizy is a small team building free AI tools that people come away trusting. Two are in production. This page is the case for why that matters to the people who build the models.
Pew Research Center, Americans and AI 2026, June 2026.
Pew Research Center, June 2026 · Stanford HAI AI Index 2026, public opinion.
Adoption is running ahead of trust.
Trust is what decides whether adoption lasts, and whether the forecasts behind every lab hold up. Low trust caps adoption. Adoption caps revenue. That makes public perception a line item for every model provider, not a communications problem.
The public already has a word for what erodes it. Slop was the 2025 Word of the Year at Merriam-Webster, Macquarie and the American Dialect Society. Every cheap model and every over-promised bot adds to it, and the bill lands on everyone building AI.
What we build, and the three rules it follows
Tools that give a person a good first experience of AI on something that matters to them. The rules are what keep that experience from turning into slop.
Free
No paywall between a person and a good first experience of AI.
Frontier only
Claude and Grok on their makers' own routes. The cheap tier is where slop comes from.
Guarded
Refuses rather than fabricates. Explains every change. Masks personal details in anything it stores.
What happens to every line
A frontier model drafts. Before a person sees a word of it, seventeen checks compare the draft against the source. Any one of them can stop the run and say which rule broke. That refusal is the part we are proudest of, because it is the part that keeps a person's name off something untrue.
The seventeen checks, in plain terms
Named after the functions in the engine source, which is public.
Two products. Both live, both free.
Same engine under both. Adding a country to dualcv touches no shared code, which is how a small team keeps two products honest at once.
What credits buy
Your model's reputation is built one document at a time. We build the documents, and we can show where the credits go.
Model credits
On Claude for the Pro path and Grok for the Quick path, first-party routes only.
Free documents
For real people, on the highest-stakes thing they write, with no paywall.
Good outcomes
Checked against the source, so nothing shipped can embarrass the model behind it.
Published numbers
Usage, refusal rates and outcomes, reported to you and to the public.
Pinned to the makers' own endpoints rather than to a reseller, because a quantised copy of a frontier model is not the model we evaluated. Names and marks belong to their owners and appear here because we run their models, not as a claim of partnership.
What we can show, and what we can't yet
Every model we run was chosen by blind evaluation against a benchmark we keep for the purpose. The corpus, the checks and the reports are published, so a number we claim is a number you can reproduce.
Open questions we would rather answer with a partner than guess at
- The refusal rate at production scale. Our benches so far are small, and we will not put a number on this page until it is one we can defend.
- Whether a tool that refuses improves hiring outcomes for the person using it.
- Where deterministic checks beat a model judging a model, and where they don't.
- What a good first experience of AI does to a person's trust in the next one.
These are the questions a research partnership would be for. The corpus and the checks are ready to be handed over.
Talk to a human
[email protected]All three reach the same inbox, read by the people who wrote the checks. Projected usage, the evaluation corpus and the reports are ready for any application form; ask and we send them. Answered within two working days.