ReallySolved · 3 AIs. 1 question. We settle the disagreement.

FOR VENTURE STUDIOS & GTM

Problems solved

Within 2 points of Claude Opus 5 in accuracy, at 1/40th the cost.
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95% against its 97%, on the same questions.
Wrong answers on $M decisions are expensive. We fix your questions
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Explained below in the YOU ASK section.
& route them to 1 or multiple AIs that we know are best in 1 of ~500 topics & variables
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Created originally from market research, then by internal testing, then updated in real time by our user-created, platform-owned, topic accuracy data.
We then run a proprietary blend of math + statistical analysis across their answers to resolve most of the disagreements. Fewer tokens, higher accuracy.
AI is moving faster than its creators & fixers can control - Mass Multi-modal crowdsourcing, is the only fix.
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2 things we do today. When the AIs disagree, we say so in their own words instead of picking a winner. And every human correction is stored beside the answer it corrected, with the model, the date & who wrote it, so nothing has to be traced back later.
Nobody keeps a record of who turned out to be right.
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Everyone records something. None of them record whether the answer was true.

Reddit & Quora: votes. That is what people preferred, not what was right.
Stack Overflow: an accepted answer. That means the person who asked was satisfied.
ChatGPT, Gemini, Perplexity & Copilot: a thumbs up or down on the reply. Private, & never published per answer.
Arena: millions of votes on which reply people liked better. No claim either was correct.
Scale, Surge & Mercor: paid labeling for the labs. The work is bought & kept, not scored in public.
Upwork, Fiverr & GLG: star ratings for the person, not for the answer.
Microsoft Researcher: runs models against each other & shows where they disagree. It hands you the disagreement & stops.

A company that sells its own AI cannot publish a fair scoreboard of whose AI was right. That is the gap, & it is why the referee has to be somebody with no model to sell.
Humans enjoy playing an unprecedented 9 self-reinforcing viral games that recruit hobbyists up to attestation pros to correct what's still not 100% resolved & get paid for what only frustrates them today.
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Top 3 AI models said no other company in the world has more than 3 self-reinforcing flywheel loops, We run at least 9! (then again...our whole premise is you can't trust what AI tells you until a human checks it)
Your 19 staff use the knowledge base you build or import to record, find, sell, promote, organize, remember, post, send, & more.
Even expensive AI models are confidently wrong, & go stale without telling you. We fix both.
People don't want to read a book every time AI answers. We summarize the answers & point to the disagreements so members can quickly see where they may have been misled.

Low risk

VC Founder funded, not raising money & tiny burn (mostly patent attorney).
Patent pending, reviewed by a top 10 patent firm.
Consumers already pay for a far thinner version of this. 2 of the 4 biggest apps that just show you several AIs earn over $100M a year at ~$30 a month, & not 1 of them settles the disagreement.
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ChatOn ~$115M to $135M a year · Chat & Ask AI ~$109M in 2 years · Nova & ChatNow publish no revenue figure. Their listed prices work out at ~$30, ~$30, $9.99 & ~$26 a month against our $5. Every 1 of the 4 shows you several AIs; not 1 writes out where they disagree, improves the answer, or brings in a person to settle it. Full table, with sourcing, on the direct-competitors page.
Excluded from our full forecast to be ultra conservative: $100M's expected from AI labs
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Current solutions pay by the hour for anonymized human-corrected data. We GET PAID by providing very low cost high quality AI plans, while allowing hobbyists & professionals, a way to effortlessly monetize their specialized knowledge as instant consultants & an outlet for their frustration when they type a short correction, at the moment they see a mistake, & get paid their share of human corrected data they would never be able to sell.

~$30B spent on human data so far in 2026. Scale AI was selling it at $2B/yr before Meta investment, Mercor $614M in 6 months
AI creating income for people rather than replacing them.
~88-94% margins.
The AI labs already pay people by the hour for this work.
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Widely reported figures, not our own measurement: Scale was said to sell about $2B a year of it & Mercor about $614M in 6 months.
Ours arrives free or as revenue, as a by-product of people using the product (depends on plan).
We turn everyday people, who never realized their oddly granular knowledge of trivial things is valuable, into instant consultants. Their credentials come from paying customers' feedback, not self-posted & buddy-endorsed claims.
Built, running & tested today. Not a plan.
History proves the consumer angle is less risky than trying to capture a few large companies (counterintuitive)
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History has proven a buyer you depend on can be taken away by somebody else's regulator, somebody else's election, or somebody else's change of mind. Consumer subscriptions are the only money in this category that no single institution can withdraw.

A studio's first instinct is 3 big logos. Every company that tried this before us died when 1 of those logos walked, so we build on many small payers first.

4 companies in this exact business found out the hard way. Logically lost 2 platform contracts, went into administration in July 2025 & was sold for parts. Full Fact lost 1 contract worth over £1M a year & cut about a quarter of its staff. Factmata sold cheaply & was folded into somebody else's product. NewsGuard won every legal fight it faced, & still lost most of its business. A $13M defamation suit against it was dismissed. It sued the FTC over a documents demand & the FTC withdrew. A congressional inquiry produced nothing. Meanwhile 2 of its 3 kinds of buyer went away anyway: government contracts gone, & regulators barred the big ad groups from buying ratings like its own, as a condition of somebody else's merger. 1 large customer is left. It last claimed to be profitable in January 2022 & has disclosed nothing since.
We need you to grow it. We have plenty of zero-cost outreach strategies that need your experienced review.
Outreach ideas.
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Correction of the week. An email carrying the questions still open, a short summary of where the AIs disagreed & what needs settling. The best upvoted answer earns 1,000 track-record points.

Weekly public boards, built from what people actually asked. Where the AIs disagreed hardest, where they all agreed on something surprising, & the funniest wrong answers. The disagreement is the content, so it writes itself.

Debates, contests & games. The engines argue in rounds, our referee calls it in a voice people enjoy, & the crowd votes.

Sharing earns free services, not cash. An upvote only ranks things for us. A share brings somebody new, so that is what we reward, & we reward it with more of the product rather than a payment.

Creators who already post AI failures. We give them content they actually want to share, which is how they earn the benefits, & recruiting 1 brings the contributor & their audience in the same move.

The gift, never a pitch. Finished content starring them, no strings. Influencers get things built for their own feed. Everyone gets a private vault they fill with their own notes & links, & the more they add the more they can earn.

Real corrections, sent free. Show a company what the AIs tell its own customers about it. We ask the AIs the questions its buyers actually ask, then send it the answers that are wrong, out of date, or that contradict each other with an invitation to come & get the rest.

A leaderboard, & the best answer of the week on the homepage. Once somebody signs in, the fun stuff invites them into the games, contests & debates.
Take 100% of quick ramp-up revenues until you've recouped 3x your time & costs + equity with ongoing profit sharing.

Moat

We automatically improve the question & intelligently route to the best AIs on ~500 (& growing) Accuracy & Safety Topics based on market research, internal testing, & real time at launch. We know which specific or multiple AIs in each topic to ask, & especially which AIs not to ask.
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Just in our internal testing alone, AI accuracy data changed within days.
Human correction data far more detailed than the labs collect.
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it isn't worth it for Attestation companies like Scale or Surge to get as granular. We get the byproduct of people doing exactly what they do today while becoming instant consultants for information they just give away free today, & simultaneously, effortlessly, build up a corpus of human corrections the AI labs will find valuable (some, not all). Google gave Wikipedia 3 billion facts, only 1% got in because there were not enough humans to check it. We also keep every AI's original answer permanently next to the correction, so our grading can always be checked, which almost no AI benchmark does.
18 questions about AI mistakes we don't think anyone else has fully solved, & we've answered every one.
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All 18 are written out on page 7, beside the 19 staff built on the answers. Months of test runs sit behind them. Anyone can imagine the experiment; but maybe no one has learned from all the incorrect assumptions or surprise results & continued to run every scenario possible, which is the part that is hard to copy.
knowing WHEN to suggest question improvements & when to stay quiet.
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Too many warnings & people leave. Too few & the feature is pointless. Where we set that line comes only from feedback from real people, & how we set it is held internally (trade secret revealed to serious partners under NDA). Planned: users can click a WRONG button, with a checkbox that says “Don't suggest for this type again.” Example: Should I walk or drive to a nearby car wash? Without our post-question check, an AI answers helpfully “walk,” missing the point that walking there doesn't get your car washed. The question still gets asked verbatim, but the user gets a gentle warning with the answer that their question may have had an ambiguity or other problem, so they can search again if they want.
Accuracy & safety, from the same crowd.
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A wrong answer splits 2 ways: inaccurate, & unsafe. The same correction that fixes 1 measures the other, so safety costs us nothing extra. We map 245 safety topics across 22 categories, from the scams & deepfakes an ordinary person meets to prompt injection & agent tool use. Nobody has measured most of them.
9 self-reinforcing viral games vs a best-in-world 3.
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According to the 3 smartest AIs, no other company in the world runs more than 3 self-reinforcing flywheel loops. We run 9! Of course AI is trained to tell you what you want to hear, so, open to being corrected.
Comparing 3 or 10 engines simultaneously keeps turning up unexpected lessons & benefits

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