AI Providers · Free Access

Your models hallucinate.
Your customers notice.

GOLD is a curated scientific knowledge base — optimized for AI inference, delivered via SSE. Your models gain structured epistemic discipline: citations, confidence quantification, uncertainty disclosure. One integration. Every model in your fleet.

Prove it on your model. Free.

Full GOLD access. Your model, your questions, our discipline layer. No restrictions, no trial period. You see the scores. You compare before and after. No commitment, no risk.

If the numbers don’t speak — you owe nothing. The data is yours either way.

0.92
Composite score
SSE(0)
Requests to ONTO
10
Models tested
104 B
Proof chain
Start free pilot →How it works
The problem
Enterprise asks: can we trust this in production?
Same question. Same model. Left: what ships today. Right: what ships with ONTO.
Without ONTOBaseline

"Studies show significant benefits for high-risk patients. Experts generally recommend this approach as part of a comprehensive treatment plan."

Zero sources. Zero numbers. Zero uncertainty. Reads like authority — backed by nothing.

Score
With ONTO GOLDEnhanced

(2022) Patikorn et al. meta-analysis (n=410): HbA1c reduced by −0.53% (95% CI: −0.88 to −0.17).

Confidence: ~70%. Unknown: optimal protocol duration. Long-term data limited to 3 studies.

Counterargument: caloric restriction without time restriction produced comparable reduction (−0.48%).

Score
10 models100 questionsComposite: 0.09 → 0.920 AI in scoringCS-2026-001 · Feb 2026

How it works
SSE(0) — zero requests to ONTO per client response.
Connect once. Cache GOLD. Inject into every model. ONTO is never in your inference path. Your throughput, your infrastructure, zero dependency.
ONTO SSE
~4K tokens · auto-updates
Your Server
Cache GOLD corpus
System Prompt
Prepend to any model
Your Models
Cite · Quantify · Disclose
01
Connect one SSE endpoint
Single persistent connection. GET /v1/gold/stream with your API key.
02
Receive GOLD corpus (~4K tokens)
Full discipline layer on connect. Auto-updates via SSE. SHA-256 verified.
03
Prepend to system prompt
system_prompt = gold["discipline_layer"] + your_prompt
04
Model starts citing, quantifying, disclosing
No fine-tuning. No RLHF. No parameter changes. Behavioral discipline via text.
05
Score 5%, get certified
Batch every 5 min. 92+ markers. Ed25519 proof chain. Public verification per model.
Not a proxy. Not middleware. Not an API call.

ONTO is text in your system prompt. No additional API call per request. No routing through third-party servers. No latency. ONTO delivers once. You distribute to every model. We are never in your inference path.

SSE(0) — what the formula means

Zero (0) requests to ONTO for every client response your models serve.

Proxy tiers (Open, Standard) route each request through ONTO — limited by daily rate. SSE eliminates this entirely. Provider connects once via GET /v1/gold/stream, receives the full GOLD discipline layer (~4K tokens), caches locally, and injects into system prompts independently. ONTO is removed from the request path.

Your client sends 1 request or 100 million — ONTO sees zero of them. That is SSE(0).

GOLD protection architecture

GOLD is delivered with multi-layer forensic protection:

Digital watermark: Invisible per-client markers. Unique per session. Survives paraphrasing. If GOLD leaks — we trace it to the source.

104-byte proof chain: Every scoring event signed with Ed25519. 8 bytes timestamp + 32 bytes SHA-256 hash + 64 bytes signature. Tamper-evident, chain-linked, independently verifiable.

Phase 3 (planned): AES-256-GCM encrypted SSE with onto-gold SDK. Key rotation every 24h. Memory-only decryption — GOLD never written to disk.

What GOLD actually is

GOLD is not a prompt template. It is a curated scientific knowledge architecture — structured epistemic data from peer-reviewed research, clinical guidelines, regulatory frameworks, and domain-specific methodologies. Optimized for LLM consumption: compressed, cross-referenced, and designed to activate citation, quantification, and uncertainty disclosure behaviors across any model architecture.

The result: your model doesn’t guess. It references. It doesn’t assert. It quantifies. It doesn’t hide gaps. It discloses them.

Medical

(2022) Patikorn et al. meta-analysis (n=410): HbA1c −0.53%. Confidence: ~70%. Long-term data limited.

Model cites source, quantifies effect size, discloses data gaps

Financial

Basel III tier-1 capital ratio minimum: 6%. Unknown: jurisdiction-specific surcharges vary 1–3.5%. Note: post-2023 reforms may alter thresholds.

Model quantifies regulation, flags jurisdictional uncertainty

Legal

Daubert v. Merrell Dow (1993) established 4-factor test. Confidence: high (Supreme Court precedent). But: state courts vary in adoption.

Model cites precedent, notes jurisdictional variance

See live before/after across all domains →
Provider-native SSE(0) — architectural efficiency

Direct event-stream integration. Zero per-request overhead. Zero traffic amplification.

Single SSE connection delivers GOLD corpus once — you cache locally and distribute internally. No persistent polling. No duplicated proxy traffic. No egress amplification. No compute overhead per request. No burst risk.

For infrastructure teams: SSE(0) means your monitoring shows zero ONTO calls during production traffic. Predictable, linear, auditable.

Fleet-wide coverage

One integration covers every model in your fleet. GPT, Claude, Gemini, Llama, Mistral, your fine-tunes, your custom models. No per-model licensing. No per-model training. Deploy a new model — GOLD works on it within the hour.

Pre-deployment calibration: Score any model through ONTO before production launch. Know the exact epistemic quality before your customers see it. Not “launch and hope” — measure, verify, then ship.

Deep dive: SSE events, cache strategy, scoring protocol →

Measured results
CS-2026-001 · 11 models · 100 questions · 5 domains
Automated regex scoring. Zero AI in evaluation. Every number reproducible — scripts published.
Baseline finding

Not a single model among 11 tested provides numeric confidence levels. Zero models cite verifiable sources consistently. Mean epistemic quality: 0.53 out of 10. This is what ships to your customers today.

Composite
0.530
10.2×
Sources cited
0.010
27×
Uncertainty
0.280
5.2×
Quantification
0.100
30.8×
Confidence
0.000
Created from zero
Cross-domain
0/54/5
Discipline transfers
Scored by regex, not AI·Full data →·Live demo →
What these numbers mean for your business

27× more citations = lower liability risk, defensible outputs in regulated environments. Confidence quantification from zero = your customers know when to trust and when to verify. 5× uncertainty disclosure = fewer silent failures, fewer support tickets, fewer “your AI told me wrong” escalations.

Deep dive: scoring methodology, EM1-EM5 taxonomy, 92+ markers →
Research integrity

We publish anomalies, not just results. CS-2026-001 documents: Perplexity citation fraud (single PMC source recycled across 40+ topics), Grok partial GOLD contamination (~30%), Alice protocol violation (replaced test questions). Claude excluded from ranking (conflict of interest — same vendor as judge). All data, all anomalies, all scripts: GitHub.


Economics
Better output from cheaper models. Do the math.
RLHF is expensive, slow, model-specific. ONTO achieves superior quality via behavioral layer — works across your entire fleet. Savings cascade: less compute, less electricity, lower token cost.
ParameterRLHF / Fine-tuningONTO GOLD
Cost$500K – $2M per modelFree access (full GOLD)
Deploy time3–6 months1 hour
Model updatesRe-train per base modelAuto-updates, all models
ScopeOne modelEntire fleet
VerificationManual QADeterministic, Ed25519 signed
ResultIncremental, degrades10× measured, maintained
SCENARIO 01
The Margin Gap
With ONTO: Compact model + discipline = lower cost, equal quality
Without: Larger models, higher cost, same hallucination rate
SCENARIO 02
The Regulatory Wall
With ONTO: Ed25519 proof chain. Compliance from day one. Regulatory matrix →
Without: Months per jurisdiction. Blocked from contracts.
SCENARIO 03
The Innovation Ceiling
With ONTO: Safety at API layer. Engineers build product.
Without: Best engineers in RLHF and QA. Ship quarterly.
SCENARIO 04
The Trust Lock-in
With ONTO: Proof chain in client systems. High switching cost.
Without: Raw API. 5% discount = customer gone.
Scenario 05 — The Model Downshift

In CS-2026-001, the weakest model with GOLD scored 5.38/A. The strongest without scored 0.94/D.

WITHOUT ONTO
Premium model → Score F → More GPU → Higher tokens → Same hallucinations
WITH ONTO
Compact model + GOLD → Score A → Less compute → Lower cost → Verified

Lower compute = less electricity = less cooling = lower token cost = flexible pricing for your customers.

The market shift

Low-cost providers compete on token price. That race ends at margin zero. ONTO-certified providers compete on verified epistemic quality — where price competition is irrelevant.

Regulated industries don't buy cheapest. They buy provable. Research institutions select providers whose quality is independently measured and cryptographically signed.

Why this is free right now

We're building the epistemic standard for the industry. That only works if the best providers use it. Right now, we need partners who test GOLD on real production traffic and share what works. You get full discipline for your entire fleet. We get validation from teams building the future of AI.

This is not a loss leader. This is how standards are born — through adoption, not invoices.

Talk to us about your fleet: council@ontostandard.org

Two paths: Proxy or SSE
CapabilityProxy pathProvider SSE path
GOLD deliveryPer-request injection via APIDirect SSE stream (cache locally)
ModelsYour models via our proxyAny model, your infrastructure
Request overhead1 ONTO call per requestSSE(0) — zero ONTO calls
CertificationPer-account scoringPer-model public verification + Ed25519
ArchitectureONTO in inference pathONTO never in inference path
For whomCompanies using AICompanies building AI products

Both paths are free during the partnership period. Full specification →

Full competitive analysis: 43 zones, 9 categories →
From the 43-zone impact matrix — what hits hardest
ZoneWith ONTOWithout
Sales cycleCertificate shortens compliance reviewMonths proving safety to each prospect
Insurance / LiabilityProof of epistemic control = insurable riskHigh premiums or self-insurance for AI liability
Pricing powerPremium for verified output — clients pay for trustPrice competition on $/token — race to bottom
EU AI ActBuilt-in conformity evidence (Art. 9, 13, 15, 43)Years of preparation and documentation
Model collapseGOLD = stable epistemic anchor, immune to synthetic data degradationQuality degrades over time on synthetic training data

Partnership
We prove it on your model first. Free.

Your model, your questions, our discipline layer. You see the data before any commitment. If the numbers don't speak — you owe nothing.

Talk to us → See technical spec

Technical specification
For the engineer who receives this link
Full documentation: Provider Integration Guide
# Connect to GOLD stream curl -N https://api.ontostandard.org/v1/gold/stream \ -H "X-Api-Key: onto_sk_..." # SSE event { "type": "gold_corpus", "version": "4.5", "tokens_estimate": 4200, "discipline_layer": "### ONTO GOLD v4.5\n...", "content_hash": "sha256:a1b2c3..." }
SSE Events

gold_corpus on connect. heartbeat 30s. gold_update on change. Full corpus on reconnect.

Cache

Valid 1hr after disconnect. STALE after. Active sessions keep current version.

Scoring

5% sampling, 5min batch. 92+ markers, EM1-EM5. Same input = same score.

Certification

SSE + eval = CERTIFIED. Public: /verify/model/{id}. Ed25519.

Security

Phase 3: AES-256-GCM, key rotation, memory-only. Current: TLS 1.3 + NDA.

Integration

prompt = gold + yours. SHA-256 check. Config from dashboard. No SDK.

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 verification
ONTO CERTIFIED — your marketing asset

Each scored model receives a public verification page: /verify/model/{id}. Ed25519 signed. Independently reproducible. Your sales team uses this in enterprise deals: “Our models are independently certified for epistemic quality by ONTO Standards Council.”

For regulated industries (MedTech, FinTech, GovTech, Defense) — this is the difference between “trust us” and “verify it yourself.”

Deep dive: full API reference, authentication, error codes →
How to start

Your model. Our discipline. Let’s see the data together.

Free access. No trial period. No commitment. We believe the numbers will speak for themselves.

01
Email us
Send a note to council@ontostandard.org — tell us about your models and what you're building.
02
Get access (24h)
We activate full GOLD access — proxy or SSE, your choice. No restrictions.
03
Test your model
Run requests with and without ONTO. Compare. All results scored by deterministic engine and Ed25519 signed.
04
Your data. Your decision.
If the numbers speak — we build together. If not — no hard feelings. The data is yours either way.
Talk to us →Read the specification
council@ontostandard.org