ONTO for AI Providers — Prove Your Model's Quality

Your model is capable. ONTO makes it provable. SSE(0) architecture delivers epistemic discipline with zero added latency. One integration covers every model in your fleet. 22+ models tested. 10× quality improvement. Cryptographic proof chain per response.

The provider problem

AI providers compete on benchmarks that measure knowledge, not communication quality. Enterprise clients in regulated industries need verifiable epistemic quality — sources cited, confidence calibrated, unknowns admitted. Without provable discipline, providers are excluded from healthcare, finance, legal, defense, and government markets worth $150B+ by 2030.

SSE(0) architecture

GOLD Core delivered via encrypted SSE stream (AES-256-GCM), cached on provider infrastructure. Zero requests to ONTO per AI response. Not in the inference path. No latency added. No data retained. The only cost is context window size. One integration, every model in the fleet gets epistemic discipline.

What GOLD Core teaches models

Seven capabilities no model has natively: cite real sources with author, year, and DOI. Quantify claims with sample sizes and confidence intervals. Grade evidence quality. Present counterarguments. Calibrate confidence honestly. State falsifiability conditions. Admit unknowns explicitly. 169 files across 7 scientific domains. Not a prompt — a structured epistemic curriculum.

Published results

22+ models tested across 12 reports. Composite quality: 10× improvement. Unknown recognition: 26× (0.04 to 0.96). Source citation: 0 to 3+ per response. Scoring is deterministic — 1073 lines of code, zero AI in evaluation. Same input, same score, every time. All data at github.com/nickarstrong/onto-research.

Certification and competitive advantage

ONTO Certified: A–F grade with Ed25519 proof chain (104 bytes). Replaces marketing claims with cryptographic evidence. Unlocks regulated markets. Compliance-ready for EU AI Act Articles 9, 13, 15. Differentiation that deepens with regulation — not a feature race.

Provider pricing

Provider: $250,000/year — unlimited, SSE delivery, on-premise option. White-Label: $500,000/year — unlimited, own branding. Pilot evaluation with full access. ROI: access to $150B+ regulated market, provable quality for enterprise sales, certification revenue.

HomePitchStandardResearchEvaluate⚡ CTO Summary
WHO MADE WATER WET?
PRODUCT: AI PROVIDERS

Your model is capable.
ONTO makes it provable.

Your model already knows the answer — it just never had a reason to cite it. ONTO adds epistemic discipline at inference time. One integration. Every model in your fleet. Zero weight changes.
SSE(0)
zero requests per response
10×
composite improvement
22+
models tested
$0
pilot evaluation access
FORMAL FOUNDATION

The measurement is the Information Gap Ratio — a bounded [0, 1] score of how grounded each model response is in its cited sources.   IGR(x, y) = 1 − min(I(sy) / I(sx), 1)

Deterministic · same input, same score, every time. Measurable per response, not per model. Certification is reproducible: any reviewer validates a score without trusting ONTO. Derived from five foundations (Landauer · Kolmogorov · Eigen · Shannon · ONTO sufficiency).

Architecture & derivation → Whitepaper §11 — Proposition 3 →
Executive summary for CTO
What it doesEpistemic discipline for any model. Cites sources, quantifies confidence, admits unknowns.
Integration1 SSE endpoint · 1 hour
ArchitectureSSE(0) — 0 req/response
CoverageEntire fleet · all models
Result10× improvement
vs RLHF$0 vs $500K–$2M
ProofEd25519 · 104B
Tested22+ models · 12 reports
RiskZero. Pilot evaluation. No model changes. Reversible in 1 line.
Economics
EU fine€35M per violation
ONTO cost$250K/yr = 152yr per fine
Right now$0 — pilot evaluation
Compute$0.002 model + GOLD = 8.9/A vs $0.03 raw = 8.2/B
HardwareZero training. Zero fine-tuning. Inference-only.
Escalations5× fewer = ~$1M/yr
Deep dive
SSE(0)Zero requests to ONTO per response. Connect once, cache ~4K tokens, inject into system prompts.
GOLD169 files, 900K tokens, 20 years. Activates citation, quantification, uncertainty disclosure.
SecurityDigital watermark. 104-byte Ed25519. Phase 3: AES-256-GCM encrypted SSE.
PrivacyZero data retention. We never see prompts or responses.
ResearchWe publish anomalies, not just results. All findings shared with providers before disclosure. Scripts open source.
# Connect
curl -N https://api.ontostandard.org/v1/gold/stream \
-H "X-Api-Key: onto_sk_..."
# Response
{ "type": "gold_corpus", "tokens_estimate": 4200 }
Technical specification
PROTOCOL
SSE
ENDPOINT
GET /v1/gold/stream
CORPUS
169 files · 900K tokens
KERNEL
~4K tokens · auto-update
SCORING
1073 lines · EM1-EM5
PROOF
Ed25519 · 104 bytes
COMPAT
OpenAI · Anthropic · Google · xAI · Meta · Mistral
OVERHEAD
0 requests/response
MethodPathAuthPurpose
GET/v1/gold/streamKeySSE — GOLD corpus
POST/v1/modelsKeyRegister model
POST/v1/models/evaluateKeyScore + proof
POST/v1/models/evaluate/batchKeyBatch eval
GET/v1/verify/{hash}—Public verify
ONTO CERTIFIED — your marketing asset
Public verification page per model. Ed25519 signed. Independently reproducible. "Certified for epistemic quality by ONTO Standards Council."

Integration guide → · SSE deep dive → · Full API reference →

Start pilot evaluation → Live demo →
THE PROBLEM

Your model is safe.
Your customers need it to be provable.

Safety was the first frontier. The edge — knowing what your model doesn't know — is the next.

ENTERPRISE DEALS · NOW

Lost contracts — no proof

Regulated industries reject AI without compliance evidence. "Trust us" doesn't close deals.
Your best model loses to a worse one with a certificate.
EU AI ACT · 2025–2027

€35M per violation

Your customers face fines. They need YOUR output to be verifiable.
Your customers need a speedometer. You can provide one.

You solved safety. Now solve provability.

RLHF teaches models to avoid harm. ONTO teaches them to cite, quantify, and disclose. Not a replacement — the next layer.
SSE(0) — ZERO REQUESTS PER RESPONSE

Connect once. Cache GOLD. Inject into every model. ONTO is never in your inference path.

ONTO SSE
~4K tokens
→
Your Server
cache GOLD
→
System Prompt
prepend
→
Your Models
cite · quantify
01
Connect SSE
GET /v1/gold/stream
02
Receive GOLD
Auto-updates. SHA-256.
03
Prepend
system = gold + prompt
04
Ship
Cite, quantify, disclose.
05
Certify
Ed25519 proof chain.

Fleet-wide coverage. One integration. Every model.

GPT, Claude, Gemini, Llama, Mistral, your fine-tunes. No per-model licensing.

Latency: near zero

+4K tokens. ~$0.0001/req. Quality: 10×.

Privacy: absolute

Zero data retention. We never see your prompts or responses.

CapabilityProxySSE (providers)
GOLD deliveryPer-request injectionDirect SSE stream (cache)
ModelsVia our proxyAny model, your infra
Overhead1 call/requestSSE(0) — zero
CertificationPer-account scoringPer-model + Ed25519
ArchitectureONTO in pathNever in path
For whomUsing AIBuilding AI
PROOF — BEFORE / AFTER

Same question. Same model. Left: today. Right: with ONTO.

BASELINE
"Studies show significant benefits for high-risk patients. Experts generally recommend this approach."

Zero sources. Zero numbers. Zero uncertainty.
0.18
F
SAME MODEL + ONTO
(2022) Patikorn et al. n=410: HbA1c −0.53% (95% CI: −0.88 to −0.17). Confidence: ~70%. Counter: caloric restriction: −0.48%.
0.82
A
Try on your model →
MEDICAL
Patikorn (2022) n=410: HbA1c −0.53%. ~70% confidence.
Cites, quantifies, discloses gaps
FINANCIAL
Basel III tier-1: 6%. Surcharges 1–3.5%. Post-2023 reforms may alter.
Quantifies, flags variance
LEGAL
Matrixx v. Siracusano (2011): "substantial likelihood" test. Circuit split.
Cites precedent, identifies uncertainty
PUBLISHED DATA — 22+ MODELS

CS-2026-001: 11 models, 100 questions, 5 domains. 1073-line deterministic engine — zero AI in evaluation.

Source Citation
×27
0.03
0.82
Calibrated Confidence
0→1
0.00
1.00
Unknown Disclosure — the edge
×26
0.04
0.96
Inter-model Variance
−81%
0.58
0.11
RAW vs +GOLD — 5 MODELS
GPT-5.4 Grok 4 DeepSeek Gemini Claude 6.5 9.9 Raw + GOLD
ModelRaw+GOLDΔ
GPT-5.46.5/C9.9/A+52%
Grok 46.7/C9.3/A+39%
DeepSeek V3.17.3/B8.9/A+22%
Gemini 3.1 Pro4.1/D7.8/B++90%
Claude Opus 4.66.5/C9.9/A+52%

Full: 22+ models at /reports/ · GitHub

ECONOMICS

What you lose without. What you gain with.

AreaWithout ONTOWith ONTO
Regulatory risk€35M fine per violation$250K/yr — compliance built in
Enterprise salesMonths proving safetyCertificate shortens review to days
Market accessLocked out of finance, healthcare, govEntry ticket to $150B+ regulated markets
Compute costVerbose hedging burns tokens15% shorter responses — discipline = precision
Support load"AI was wrong" escalations5× fewer tickets — ~$1M/yr saved
Pricing powerRace to bottomPremium for verified output
New modelsRetrain RLHF per model ($500K–$2M)Works immediately. One integration. All models.
Starting cost—$0 — pilot evaluation. Full access.
GREEN INTELLIGENCE — THE CFO ARGUMENT
⚡
−15-20%
compute per request
shorter answers = fewer GPU cycles
🔋
$0 retraining
frozen energy costs
inference-only, no RLHF cycles
🌱
ESG-ready
carbon tax compliance
EU carbon levies by 2030
Disciplined models don't hedge, don't ramble, don't repeat. Same quality in fewer tokens = same fleet serves 20% more requests. Zero retraining means frozen energy spend while competitors burn megawatts on RLHF cycles per model.
Research integrity
We publish anomalies, not just results. All findings shared before disclosure. Open source: GitHub.
WHY THIS CHANGES YOUR BUSINESS

ONTO is not a feature. It's a structural shift in how your company makes money.

🏛 B2B & B2G lock-in
Governments and enterprises won't deploy AI that can't pass audit. Your disciplined model is the only one with cryptographic proof of quality. Competitors sell black boxes — you sell verifiable answers. Finance, healthcare, gov contracts default to you.
🇨🇳 Survive the price war
Chinese providers compete on price. Big Tech competes on capital. You can't win either race. But you can win on efficiency: a $0.002 model + GOLD beats a $0.03 model without it. Discipline is the only lever that lets you set competitive prices and keep margins.
🔄 Risk → Revenue
Right now safety is a cost center — $2M per model on RLHF, compliance teams, legal reviews. With ONTO, safety becomes a product. You sell "verified output" at a premium. Compliance spend flips from expense line to revenue line.
🤝 Trust as asset
Every response graded A-F, signed Ed25519, independently verifiable. After 12 months of zero critical failures, your model earns trust that compounds. Enterprise clients stop evaluating alternatives. Trust becomes your moat — not your model weights.
📈 Scale without degradation
Classical systems degrade at scale — more users, more errors, more support load. Discipline doesn't tire. Your millionth request is graded the same as your first. This means linear revenue growth with flat error rates. The economics of scaling reverse: bigger = more profitable, not more fragile.

You pay $250K not for software. You pay for a new revenue structure.

Compliance becomes product. Trust becomes moat. Safety becomes profit.
10-YEAR PROVIDER ECONOMICS

What $250K/yr buys over a decade. Conservative estimates — no regulatory mandates assumed until noted.

YearWhat happensEconomic effectCompetitive position
1Integration. Fleet-wide discipline. First B2B contracts.RLHF savings: $500K–$2M. Compute: −15-20%.First mover — only certified provider in pipeline.
2Trust track record begins. Enterprise renewals. Second product line (verified output tier).Premium pricing on certified tier. Support tickets −5×.Competitors still selling black boxes.
3EU AI Act enforcement. Regulated sectors require proof.Gov/healthcare/finance contracts — default winner.Non-certified providers locked out of regulated markets.
5ONTO grading becomes industry expectation. R8-R18 available.New revenue: Human AI API licensing. Trust compounds.Switching cost for clients = years of verified history.
7Carbon tax enforcement (EU). ESG audits require AI efficiency data.ESG-compliant fleet = tax savings. Non-compliant competitors penalized.5+ years of verified track record. Unassailable trust moat.
10Discipline is infrastructure. AI in medicine, law, defense requires certification.Total 10yr investment: $2.5M. Market access: $B+.Provider without discipline = provider without market.
$2.5M
total 10yr investment
$0
retraining cost per model
∞
trust doesn't depreciate

⚠ All figures are projections. Regulatory timelines based on published schedules (EU AI Act, proposed carbon levies). Actual results depend on market adoption.

ONTO CERTIFIED
ONTO CERTIFIED — your marketing asset
Public verification page per model. Ed25519 signed. Independently reproducible. "Certified for epistemic quality by ONTO Standards Council." Your customers see proof, not promises. Your sales team gets a compliance shortcut.
HOW TO START
01
Email us
council@ontostandard.org
02
Access (24h)
Full GOLD — proxy or SSE.
03
Test
Compare with/without. Deterministic scoring.
04
Decide
Numbers speak — we build. If not — data is yours.
Prove it on your model. Free.
Full GOLD access. Your model, your questions, our discipline layer.
No restrictions. The data is yours either way.
Start pilot evaluation →Live demo — 30 seconds →

If the numbers don't speak — you owe nothing.

Free access · Full GOLD · All models · Signed proof chain
ONTO Standards Council
Independent research initiative · Est. 2024
ontostandard.org
Standard · Research · Evaluate