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

For GTM & Venture Studios

Everything a partner needs, in 1 deck.

PROBLEMS SOLVED @launch

Wrong answers on $M decisions are expensive. 1 AI on its own was off by $1.34 million on the Section 179 tax limit.
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We fix your questions (explained in the "YOU ASK" section) & route them to 1 or multiple AIs that we know are best from testing 169 AIs mapped to ~500 topics, safety, & other variables.

That routing was created originally from market research, then by internal testing, then will be 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.
Within 2 points of Claude Opus 5 in accuracy, at 1/40th the cost.
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On our $9 & $19 a month plans. 62 held out difficult questions: we got 59 right, 95%. Claude Opus 5 also got 59, but out of the 61 it answered, 97%. It returned nothing on the 62nd; we answered all 62. (That last line was written by Claude picking on itself, but most AI models have the same problem.) Inside over 4,000 questions, 169 engines scored, ~75,000 answers & 4,164 answers double-graded. Not a cheaper, worse answer. The same answer, cheaper. The best accuracy you can get at each price is what economists call the Pareto frontier. AI prices are far out of line with how accurate each AI is, & which AI gives the most for its price depends on the kind of question, so we keep measuring where that frontier sits.
We've created a ready to launch ecosystem (15 funnels for companies, forums & hobby groups, & individuals) to collect human correction data & get people consulting gigs as a byproduct.
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An ecosystem where companies, groups & individuals are rewarded for correcting AI, while the AI staff we give them uses their knowledge base to organize their life & attract consulting gigs they weren't even looking for (In Company accounts, our Job Scout looks for business opportunities for the company not any individual).
People can get paid 2 ways doing what they already do today. They'll 👍👎 AI results & stop giving away their knowledge for free on Reddit, Twitter etc.
Sam Altman, Elon Musk & Dario Amodei all say AI is moving too fast. Mass multi-channel crowdsourcing is the only way to keep up.
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What each one said, reported by CNBC on Sep 12, 2026 (Ashley Capoot, “OpenAI rules out IPO this year as Altman, Musk & Amodei warn AI is moving too fast”): Dario Amodei (Anthropic) published an essay urging AI labs to pace how fast they raise model capability. Sam Altman (OpenAI) told Fortune an OpenAI IPO this year would be “ill-advised” & posted agreement with Amodei on X. Elon Musk (xAI) posted “Dario is right” on X.

Nobody we found is addressing mass attestation from hobbyists, researchers, & professionals who are searching AI or posting in groups as they normally do & not looking to have an attestation job but get to correct AI when they are frustrated it gets things wrong & perhaps get paid for it.

If billions of people were correcting AI every day, the worst risks people worry about with AI would shrink.
3 AIs read the same public rule & gave 2 different answers. Looking it up is not what's missing.
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AIs can already read the internet. When they disagree it is usually not because the page is missing, it is because the page is ambiguous, out of date, or does not quite cover your case. Somebody who does that work for a living settles it in 30 seconds. Nobody we could find is writing down who that person is, or whether they turned out to be right.
Anthropic, OpenAI & xAI now back outside, independent checking of AI. We are that outside check for the answers people get every day.
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Source: all 3 cosigned AEF-1, the AI Evaluator Forum’s standard for independent, third-party checking of AI (reported by Latent Space, Sep 15, 2026; aievaluatorforum.org).

We are hoping they will also engage with our independent, neutral www.FixAI.group, but that is not a business for you to think about. It's simply a way to keep a "morally binding" agreement working.
Personal AI agents like Instinct, xAI's Grok Bot, Meta's Muse & OpenAI's ChatGPT Work now buy, book & send for people.
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Instinct raised $1B at a $10B valuation in September 2026 (Bloomberg).
Before they act, they should check facts with us: several AIs, whether they agree & a plain "safe to act" or "check with a person."
Big funds are buying small firms whose owners are retiring, like accountants & IT shops, & handing the work to AI. Before that work reaches a client, it needs an outside check.
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The biggest 1 has raised over $3 billion & already owns 70+ accounting & IT firms (August 2026). Their own plan already has a checking step before a person signs off. We are built to be that step. How it works: page 21.
9 channels bring the data in, & only 1 of them is the consumer search box.
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The community forum. Expert Solvers answering paid questions. Bounties, where a company posts a paid problem. Showdowns, where 2 experts argue opposite sides in public & the public decides. Brands answering complaints about themselves. Company accounts, where the company chooses who gets paid. Our own testing runs, which need no users at all. AI hosts & networks handing us their AI to be scored. & the consumer search box. 4 of the 9 are businesses.
Your 21 AI staff use the knowledge base you build or import into your notebook to remind, organize, find, sell, promote, post, warn, send, remember, find consulting gigs, & more.
Our AI agents do real jobs in people’s lives. Every fix a person makes shows which AI was right on what people actually ask.
Nobody we found 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. Put another way, the AIs need a third party checking the data they are fed & the answers they give back. That is the layer we are building.

Every answer is kept whole: the question, which AI said it, the exact model version, what it cost & how long it took.

When the AIs disagree, we use their own words instead of picking a winner. Our algorithm then provides a more accurate answer & when even that turns out to be suspect, human corrections are stored beside the answer it corrected, with model, date & who wrote it, so members can be credited & paid if the AI labs value their corrections.
Even expensive AI models are confidently wrong, & go stale without telling you. We fix both for people & companies.
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An AI chatbot was asked how many e's are in "seventeen" (it's 4) & said 3. Even after the word was spelled out for it, letter by letter, it still would not change its answer. Read what happened, Futurism, Sep 2026 →
(1st group already engaged) A group keeps its setup, or we host a new one (free to us).*
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We nudge cold threads so somebody picks a settled answer (nobody else we found collects this). We record which AI/person got it right & who to trust on which subject. New questions go into related unsettled threads which shows the asker they’re not alone & see how others framed it, creating another chance to get the answer settled which makes the group more valuable. Threads that have no definitive answer mimic debugging methods with easiest to eliminate on top. (It’s too complicated for Groups to share correction earnings)
We bridge the chasm between paid attestation which cannot keep up & the masses that have everyday granular knowledge but can't be bothered to take 1 minute to correct AI & just shake their head. If millions are incentivized to build up their cred correcting wrong answers & potentially benefit in 2 ways, everybody wins & AI is less out of control when its answers are corrected in real time by mass crowdsourcing.
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.
User feedback lets us know when to stay quiet & not pester -
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AIs reword the same answer constantly, & almost none of those rewordings change anything. The hard part is spotting the 1 change that actually matters among the 40 that don't, & everybody else pays for a top-end AI to read both versions & judge, every single time.
too many warnings & people leave, too few & it's ineffective. How we set it is a trade secret.
(future) Insurers are cutting AI losses out of their coverage. Pricing that risk back in takes a record of which AI is wrong on which subject, & how often, built by watching for years.
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Standard business policies are being rewritten to exclude losses caused by an AI, & standalone AI policies are appearing to fill the gap. Insurers already price AI risk privately, model by model, through 1-time technical audits & certifications at the point a policy is written. We found no insurance company keeping a continuously-growing, comparative record across many AI engines & subjects to provide accurate AI actuarial data. That record can only be built by watching for years.
* GTM partner to help decide, including whether letting a group keep its own copy wins more groups than the lock-in it gives up.

MOAT

21 questions about AI mistakes we don't think anyone else has fully solved, & we've answered every one.
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All 21 are written out on page 8, beside the 21 staff built on the answers. 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. (Over 4,000 questions, 169 engines scored, ~75,000 answers & 4,164 answers double-graded sit behind them.)
We’ve already tested 169 AIs mapped to ~500 (& growing) Accuracy & Safety Topics so we know which ones to ask about your subject before we ask. This giant matrix (some of it filled out by research) gets more accurate over time.
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From market research, our own testing, & real time at launch. So we know which specific or multiple AIs to ask on your subject, & especially which not to ask, before we improve the answer accuracy further. We also fix your question before we send it.

Our own testing behind it: over 4,000 questions, 169 engines scored, ~75,000 answers & 4,164 answers double-graded.

Just in our internal testing alone, AI accuracy data changed within days. It also shows which companies fix a slip fastest, which is a better guide to who you can depend on than who is smartest this week.
We catch the AI mistakes the labs don’t know to look for.
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Scale & the others fix answers to questions a lab writes. Ours are caught in the wild, on questions real people actually asked, with the AI’s original answer kept next to the fix. A lab’s own thumbs-down only says an answer was wrong, & only for its own AI. We capture what’s right, across every AI, while it’s fresh, & every time someone overturns our right-or-wrong call on an AI’s answer, that becomes a labeled correction nobody else has. & AIs behave differently when they can tell they’re being tested, which makes real questions worth more every year.

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.
If we can achieve network effect, a competitor with big money can't compete.
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Money copies the software & process in a weekend, it doesn't buy 4,000 questions & ~75,000 answers of trial & error, leading to several surprises & pivots. Those are our trade secrets.

The more people ask → the more subjects get covered → which pulls in more experts → which makes the answers better → which brings more people. A rival has to grow that circle too. They can't buy it.
Companies pay, their people’s 👍/👎 build a checked-answer dataset.
A company pays to upgrade its AI & cannot tell whether the answers got better or worse on its own subjects. AI information sometimes degrades without the AI lab or the AI knowing or telling us. We have been measuring exactly that.
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The companies that make AIs don't say exactly how current their knowledge is, & they change them quietly. So how good an AI is on a subject today cannot be looked up anywhere. It has to be measured, over & over, & the measurement results are our useful secrets. Every search fills in another square of which AI to trust on which subject & gives us statistical analysis on how fast degradation happens on which topics & which companies & which of their models so we can predict it on future models. It also tells us whether a new model release actually improved anything on the subjects people really ask about, so a company can tell whether the upgrade it just paid for was worth it. It is built day by day, so somebody starting later cannot catch up by spending more. They can only start their own clock.
A machine can see the answer changed. Only a person can say which one is right.
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A shipping company asks 3 AIs about a customs rule & gets 2 different answers. A month later 1 of them quietly switches sides. None of that tells you which answer was ever correct, because all of them are just the AI's opinion. Somebody who does that work for a living has to say so, & somebody has to write down that they were right. That is the part nobody has built, & it is the only part that can't be automated.
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 we get both from 1 pass. We map safety topics across 22 categories, from the scams & deepfakes an ordinary person meets to prompt injection & agent tool use. Nobody we've found has measured most of them.

The dangerous failure is all AIs often being confidently wrong at the same time. Processing the question is how we fix it. On hard questions with previously known answers, our testing showed every single AI got it wrong about 6% of the time. In critical situations (hospitals, governments, financial institutions, etc.) that's unacceptable. We can fix that.
We know which AIs are confidently out of date.
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An AI has no way of knowing when its own information stopped being true. It answers with exactly the same confidence either way, & is often unaware when its training has ended (even if you ask it), which is what makes it dangerous. We test every engine about things that have changed since it was trained. That is how we know which AIs to trust on a subject that moves.
More cost savings knowing minimum token usage for the best answers.
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We intend to build on published data to be able to have the most comprehensive list knowing the minimum amount of time every AI engine needs to produce the best answer (most efficient use of tokens).
9 self-reinforcing viral games.
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Answer for a stated price · Get hired directly · Win disputes · Weekly contests · Invite a friend · Creator badges · Streaks & leaderboards · Dispute Hunter challenge · Your notebook

The 9 games bring people in. A quality loop underneath them brings accuracy in: every 👎 goes to peers who can correct it or debate it, & whoever turns out right is a trustable expert future questions are routed to on that granular subtopic. Our whole user base doubles as a gigantic topic Matrix scorecard & leaderboard at the same time.

We are told few companies run more than 3 self-reinforcing flywheel loops. We run 9.

LOW RISK

Tell us what your terms would be to take over the promotion & running of the websites. Our current idea is: take 100% of quick ramp-up net revenues
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Net is after low AI costs, card fees & contributors’ share. Contributors are paid in parallel, not after your 3x: when a company hires one, it pays them through our Stripe checkout, straight to the expert, & we take no cut.
until you've recouped 3x your time & costs + equity & ongoing profit sharing from AI lab revenue & all other revenue.
Founded & funded by a former VC, not raising money & tiny burn.
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Mostly 1-time, already paid, top 10 patent firm costs. Costs going forward are only for outreach & running the various funnel sites.
~80-92% margins after the AI & card fees, & our cost per answer goes down as we grow.
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Cost FALLS with scale because every search updates our record of which AI is right on which of the ~500 topics & variables, & the right one is often the CHEAPER one. Scale also tells us which subjects never move, so we stop paying to re-check them. More users → less cost.
Consumers already pay for a far thinner version of this. The biggest app that just shows you several AIs earns 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 ~$360, ~$360, ~$120 & ~$312 a year against our $50. 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.
Our conservative bottom-up, slow ramp-up, analysis assigns a very low value to our correction data & still shows $100Ms expected from AI labs
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~$30B a year is what human checking of AI costs once you add both halves: what companies pay outside vendors to do it, & what their own staff spend doing it in-house. The vendor half is the part with published forecasts, & it is the part we sell into: ~$2-7 Billion/yr today.

The published forecasts, & they disagree with each other: Research & Markets has human-in-the-loop AI at $6.73B in 2026 going to $16.4B by 2030. Global Market Insights has data annotation tools at $25.0B by 2032, growing 25% a year. ResearchNester has $44.68B by 2035. The staff time companies spend checking AI in-house adds $10B to $15B+ on top. We quote the low end of those on the forecast page, on purpose.

Scale AI was at $2B a year before the Meta investment. Mercor crossed $2B annualized in June. Our ramp-up will be slower than Mercor's & our data may be priced lower, but we would make up for it in quantity after filtering out exactly what the AI labs want. We present the questions as challenges & debates so people have fun creating specific human corrected data the AI labs want.
The AI labs already pay Billions for this work. We get paid for ours.
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Companies 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 & 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. They build up their knowledge base in the notebook we provide that is used by their 21 staff to help them in their daily life in many ways including trying to find them consulting gigs they would never think of going out to look for on their own.
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.
15 targeted versions of 7 funnel sites mostly built, running & tested today. Not a plan.
We need you to grow it. We have plenty of zero-cost B2B & B2C outreach strategies that need your experienced review. Outreach ideas.
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To people: invite-a-friend & referral links, creator badges, weekly contests, streaks & leaderboards, & recruiting where the experts already are, content-led, LinkedIn first, then Reddit, StackOverflow & GitHub.

To companies & groups: group owners keep their setup or we host a new one, professional-association partnerships, bounties where a company posts a paid problem, brands answering complaints about themselves, & company accounts where the company chooses who gets paid.

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.

Offer influencers & creators (who cover AI) referral rewards to share our FUN STUFF features like: AI chat host who asks bored or lonely people to fix funny, ridiculous, debatable, unsafe, etc. AI answers, plus the 3-AI search & our Stump / Split the AIs contest, to run with their own audiences. Rewards kick in when followers subscribe to a paid plan.

Expert outreach. An instant verified consultant network, then corporate HR outreach.

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.

The viral & automated outreach timeline, who to target, & the pages their referral links land on are already written. We would rather bounce all of it off experienced GTM people than defend it.
AI creating income for people rather than replacing them.
Comparing 3 or 10 engines simultaneously keeps turning up unexpected lessons & benefits
Using our stored results lowers costs even more over time.
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Instead of costing tokens for top-end AI every single time. We index & use previous answers which costs a fraction.

THE DECK THE MACHINE, DRAWN Contact us →

Or just click to try an example:

They split 2 to 1. Agreement is not proof. You settle it. Vote to help. Simulated example · pre-launch

Thinking mode is sometimes slow to be more accurate

The 3 free AIs answered See all 20

How you get paid

Your consulting fee: we take nothing
AI-lab data: split 50/50 with you
Your name: never leaves us

You show up as a nickname you pick. Your real name is never shown.

We don’t take a cent from you, or from the company that hires you.

Expert invitations opened at launch, 2026.

Only we can link your handle to you, and we never pass your name to companies or AI labs.

How accurate is this?

Odds of getting the correct answer (we publish the lower number, our tests scored higher)

~60%On your own, 3 AIs unaidedOn hard & fast-changing questions, asking on your own.
~82%3 popular AIs & Certainizer™Certainizer →
~95%Best 3 picked from 5 more expensive AIs & Certainizer™PLUS →
~95%Best 3 picked from all 169 & Certainizer™PRO →

On the questions people really ask, the best free AI we tested scores 94.4% & the worst 70.4%. A 24-point gap, & the person asking has no way of knowing which one they got.

AI engine	Crypto	Bio/Med	Law	Cyber	News	Trend	Verified
OpenAI o3	82	89	90	94	72	▲ 1	2d ago
Claude Opus	63	86	89	88	68	▲ 2	1d ago
GPT-5.6 Sol	80	87	88	91	82	0	3d ago
Claude Fable	78	89	91	90	70	▲ 1	4d ago
Claude Sonnet	75	83	85	86	68	▲ 1	1d ago
GPT-4o	72	80	82	82	80	▲ 2	2d ago
Gemini Pro	44	83	84	84	90	0	3d ago
GLM-5.2 ⚠	70	75	79	76	78	▲ 1	5d ago
Claude Haiku	62	69	72	70	60	▲ 1	5h ago
DeepSeek	76	78	78	84	65	▲ 2	8h ago
Command R+ ⚠	60	66	82	68	75	0	6d ago
Mistral ⚠	70	75	84	82	65	▼ 1	7d ago
Grok ⚠	75	76	76	80	92	▼ 1	4d ago
Qwen Max ⚠	72	78	81	80	80	0	6d ago
Kimi ⚠	66	72	80	72	78	▲ 1	7d ago
ChatGPT	50	54	54	58	45	0	2d ago
Gemini	48	64	60	62	82	▲ 1	3d ago
Meta	54	58	58	62	48	0	4d ago

AI engine

Crypto

Bio/Med

Law

Cyber

News

Trend

Verified

OpenAI o3

▲ 1

2d ago

Claude Opus

▲ 2

1d ago

GPT-5.6 Sol

3d ago

Claude Fable

▲ 1

4d ago

Claude Sonnet

▲ 1

1d ago

GPT-4o

▲ 2

2d ago

Gemini Pro

3d ago

GLM-5.2 ⚠

▲ 1

5d ago

Claude Haiku

▲ 1

5h ago

DeepSeek

▲ 2

8h ago

Command R+ ⚠

6d ago

Mistral ⚠

▼ 1

7d ago

Grok ⚠

▼ 1

4d ago

Qwen Max ⚠

6d ago

Kimi ⚠

▲ 1

7d ago

ChatGPT

2d ago

Gemini

▲ 1

3d ago

For GTM & Venture Studios

3 AIs disagreed. You settle it.Anonymous, or start a track record.Free to try. No payment details.

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