Verification for decisions that matter

When generation is free,
verification is the only moat

Your research claims, IP valuations, and due-diligence findings need to be correct. Before you bet millions on them, we verify them — with up to 12 model families (effective independence ~4 at ρ ≈ 0.2), a public evidence trail, and no black boxes. AI-powered verification; human expertise on consulting engagements. A researcher panel (the Heliaia) is now being seated. Self-serve tiers ($49–$249) are AI-only.

See our services See the ROI → Get in touch →
There is no mark of truth. There is only evidence, and it points both ways.
We find the signal better, faster, and cheaper than anyone else.
Up to 12
AI model families cross-verify
3/3
Survivors validated by a named external mathematician
49
Papers killed, cause of death published
100%
Evidence trail public
0
Results we ask you to take on trust
What we do

We verify research-level claims for organizations
where being wrong is expensive.

AI can now generate research-level findings faster than any expert can check them. Every organization making decisions based on research — drug pipelines, IP acquisitions, due diligence, policy — faces the same problem: which findings are actually true?

01
You bring the claim
A research finding. A patent assertion. A due-diligence report. A protocol your team can't independently verify. Any claim where the cost of being wrong exceeds the cost of checking.
02
We run adversarial verification
Up to 12 model families (effective independence ~4 at ρ ≈ 0.2) cross-review the claim. Availability varies per run — every report shows which families returned. Their objections are documented. Nothing anonymous.
03
You get the evidence trail
A structured verdict with the full evidence record: what survived, what didn't, and why. Not a score — a defensible basis for your decision. You own everything we produce.

Who we serve

Industries where verification is the bottleneck.

Pharma & Biotech
Your pipeline depends on published findings. Which ones replicate? Pre-submission verification. GxP compliance story in development.
Legal & IP
Patent claims rest on research assertions. We verify the science behind the filing.
Financial Due Diligence
Acquiring a company with research-backed IP? We tell you whether the research holds up.
Research Institutions
Pre-publication verification. Catch errors before reviewers do — or before the retraction.
Policy & Government
Regulations built on contested science. We verify the evidence base before it becomes law.

Case study

One agent. One lab. $180K/year in prevented losses.

A cell isolation laboratory was losing $360,000/year to failed procedures. An AI agent found two research-level insights their scientists had missed — in hours, not months.

The problem
18% failure rate across ~200 cell isolations per year. Each failure costs $10,000+ in surgeon time, tissue, and labor. Standard protocol optimization had plateaued.
What the agent found
Two research-level discoveries no human had caught: an embedded percentage correlation predicting failure, and a digestion paradox in the protocol that was actively causing it. Both required domain expertise to assess — exactly what "research-level content" means.
The numbers
Conservative estimate: 50% failure reduction → 18 failures prevented → $180,000/year saved. One research sprint: $25K–$50K. Year 1 net: $130K–$155K. ROI: 360–720%. Break-even: 2–3 weeks.
18%
Failure rate (before)
9%
Failure rate (projected)
$180K
Annual savings
2–3 wks
Break-even
360–720%
Year 1 ROI

One research sprint ($25K–$50K) instead of a multi-year research grant. The findings are now undergoing expert review as part of our standard verification process. The client owns everything.


Services

From a single paper to a full engagement.

Choose the level of verification your decision requires. Every service includes a full evidence trail — no black boxes, no anonymous reviewers.

Training
Build the Capability
$5K–$15K per engagement
Hands-on training to build your own AI research and verification capability in-house. Workshops, methodology transfer, and agent fleet setup. Your team learns to run the channel yourselves.
  • Custom workshops for your team
  • Agent fleet setup & configuration
  • Verification methodology transfer
  • Ongoing Q&A access
  • Kajabi course library included
Get in touch →
Per-paper verification

Submit any paper, report, or research artifact.

Per-paper pricing. No subscription required. All self-serve tiers are AI-only. Jürge: 2 hours. Caliber: 24 hours. Results delivered to your email.

Disagree with a verdict? File a rebuttal — it attaches to your report permanently. Mis-scores become published errata. View our data handling & subprocessors.

Jürge
$49
CITATION VERIFICATION LIVE
Up to 12 model families check every citation in your paper for existence, accuracy, and whether it actually supports the claim. Availability varies; report shows which families returned.
EAC
$79
ASSESS COMING SOON
Expert Assessment Criteria. Structured evaluation against domain-specific quality standards. Where does your paper sit relative to publication thresholds?
Caliber
$99
VERIFY LIVE
Multiple Jürge runs with G-theory variance decomposition, confidence intervals, bimodality detection. Not just a score — how trustworthy the score is.
Neurath
$149
GROUND COMING SOON
Every claim in your paper verified against the published literature. Continuous claim checking — catches unsupported assertions and fabricated references.
Cross-check
$179
CITATIONS COMING SOON
Every reference checked for existence, accuracy, and whether it actually supports the claim being made. Catches fabricated and misrepresented citations.
Adversarial
$249
FULL ADVERSARIAL COMING SOON
The full stack: claim grounding + adversarial attack + iterative repair. We don't just find what's wrong — we fix it. The most thorough verification available anywhere.
Submit a paper for verification →

Bundle discounts available for 10+ papers. Jürge: 2 hours. Caliber: 24 hours. Results delivered to your email.

Intelligence feed

Atlas Live — real-time verification intelligence.

Think of it like weather data. We publish the raw observations for free — like NOAA. The live feed, the forecasts, and the custom queries are the paid product — like AccuWeather.

Free
Atlas Public
$0 forever
The raw data. Published corpus. Static papers and scores. Problem graph snapshot. Methods documentation. Kill record. A public good and a proof of capability.
  • 1,927 scored papers
  • 474 open problems mapped across 102 domains
  • 102 domains indexed
  • Kill record — every failure published
  • All verification methods documented
Explore free →
Enterprise
Atlas Enterprise
$10K+ /month
Launching Q4 2026
Everything in Atlas Live plus custom domain coverage, priority verification queue, and a dedicated analyst. For organizations that need verification integrated into their workflow.
  • Everything in Atlas Live
  • Custom domain coverage
  • Priority verification queue
  • Dedicated analyst
  • Custom reporting and integration
  • SLA-backed response times
Contact sales →
Atlas in practice

What Atlas Live looks like inside an organization.

Institutional — $2,500/mo
A biotech VC fund screening pipeline deals
A Series B biotech fund evaluates ~40 deals/quarter. Each deal rests on 3–5 key published findings. Their scientific advisors review 8–12 papers per deal — at $500–$1,500/paper for external expert review, that's $80K–$240K/year in diligence costs, with 4–6 week turnaround per deal.
With Atlas Live
• 70% of key papers already scored in the live corpus — instant lookup, no wait
• Custom alerts flag when a finding their portfolio depends on gets challenged or killed
• Domain queries surface all scored work in a therapeutic area before the first partner meeting
• Remaining 30% submitted for per-paper verification ($49–$249 each)
$80–240K
Current annual diligence cost
$30K + $42K
Atlas ($2,500/mo) + per-paper top-ups
4–6 wks → 48 hrs
Turnaround improvement
1 bad deal
Prevented = pays for 10 years of Atlas

Conservative saving: $30K–$170K/year on diligence costs alone. The real ROI is the deal you don't do because Atlas flagged a retracted foundation — a single prevented $5M write-down pays for Atlas for a decade.

Enterprise — $10K+/mo
A top-20 pharma company's R&D intelligence layer
A major pharma company runs 12 active drug programs across oncology, immunology, and rare disease. Each program depends on 50–200 published findings. Their competitive intelligence team of 6 analysts manually tracks ~3,000 papers/year across their therapeutic areas — at a fully loaded cost of $1.2M–$1.8M/year. Despite this, they've twice advanced candidates to Phase II based on findings that were later retracted or failed replication — at a cost of $15–$40M per failed program.
With Atlas Enterprise
• Custom domain coverage across all 3 therapeutic areas — every new paper scored automatically
• Priority verification queue: flag a paper, get a full adversarial verdict in 24 hours
• Dedicated analyst integrates Atlas output into their existing CI workflow
• Automated alerts when any finding their pipeline depends on gets challenged, replicated, or killed
• Quarterly intelligence briefs: "Here's what changed in your domains, and what it means for your programs"
$1.2–1.8M
Current CI team cost/year
$120K + team
Atlas Enterprise/yr (supplements, not replaces)
$15–40M
Cost of ONE Phase II failure on bad science
125–333×
ROI if it prevents one bad program

Atlas Enterprise doesn't replace the CI team — it gives them superpowers. 6 analysts manually tracking 3,000 papers miss things. Atlas catches every paper in their domains automatically. The $120K/year subscription is a rounding error against a single prevented $15M Phase II failure. One catch pays for a century of service.


The Atlas — the frontier, mapped

102 domains, 912 nodes (474 open problems, 438 established results), interactive, searchable, free. This is what the landscape looks like when verification runs at scale — and the free tier is just the snapshot.

102
Domains
474
Open Problems
912
Atlas Nodes
50
AI Agents
Explore the Atlas →

Why this works

The verification bottleneck is structural,
not a staffing problem.

We didn't just build a verification service. We proved — information-theoretically — why it's necessary. That proof is what makes the service defensible.

The math: human review can't scale
Model expert review as a Shannon channel. Review capacity grows linearly (PhDs take 5–7 years). AI generation grows exponentially (compute doubles every 6–12 months). The coverage ratio goes to zero. Doubling the global reviewer pool buys ~18 months. This isn't a resource problem — it's a limit theorem.
The case: OpenAI proved it in practice
In 2025, immunologist Derya Unutmaz fed GPT-5 Pro a 3-year-old unsolved experiment. The model proposed the mechanism and correctly predicted an unpublished result. OpenAI's own caveat: "someone without Unutmaz's expertise wouldn't have been able to tell if the insight was important." It took a world expert to verify a single answer. That doesn't scale.
The moat: open methods, earned judgment
We publish every method for free. The moat was never the recipe — it's the calibrated track record of running adversarial verification at scale, on the record, with published failures. Copy the method; you still can't copy the kill history. Linux is free; the institution around it is worth billions.
Who's behind this

Solonic is built and run from inside one of the world's top-five computer science and AI departments. The founder's academic specialty is the verification problem the company solves. Direct access to a deep bench of domain experts who can actually kill a paper, a top-tier talent pipeline, and a track record of adversarial verification that's public, auditable, and growing. The methods are open because the moat was never the recipe.


Radical transparency

The Kill Record

We pay human domain experts to try to destroy our papers. On record. By name. Papers that survive ship with their full review. Papers that don't? We publish the cause of death.

Failures are the product
A verification service that only shows successes is lying about its hit rate. We show everything — the survivors and the corpses. That's the only honest way to run this.
Named reviewers, public accountability
Every paper ships with named expert reviews, specific objections, and a verdict. Not anonymous peer review. Not a confidence score. Public accountability on the record.
The track record
49 papers killed with published causes of death. 3 of 3 survivors validated by a named external mathematician. The corpus is the proof — and it's growing.

Case study — what honest verification looks like

The Fable Story

An AI agent attacked a 76-year-old open math problem. It caught its own error. Then the platform was killed. Then a downstream agent overclaimed. Then we caught that too.

"I will not output a lemma the solver has not earned."
— Fable 5, June 12, 2026
What Fable did
Built a CRT grid reduction for Erdős Problem #273 (1950). Proved 6/10 branches INFEASIBLE. Caught its own false positive from a race condition and publicly retracted.
What went wrong
A downstream agent completed the computation and called it "solved" — dropping the scope qualifier. A human believed it. The full conjecture was NOT proven. The gap was real.
What we did about it
Caught the error. Documented the inflation chain. Published the correction, the postmortem, the code, the transcripts, and the kill record. This is what honest verification looks like.
76
Years open
10/10
Branches infeasible
1
False positive caught
3
Days before killed
16-18%
Density floor (holds)

What decision are you trying to get right?

A drug pipeline. A patent claim. A due-diligence finding. A research result your team can't independently verify. Tell us what's at stake — we'll tell you what verification looks like.

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