The factchecked knowledge layer every AI application needs. Verified scientific papers, structured for AI consumption — at a fraction of the cost of crawling the web and running LLM-based factchecking pipelines.
Every Claude, GPT, or Gemini call wastes context window space filtering out junk and reasoning over unreliable sources. Hallucinations creep in. Costs explode. Accuracy suffers — especially when the stakes matter.
AIDOCERE delivers the factchecked knowledge layer AI was missing — and unlocks new value for everyone who creates or consumes information.
YouTubers, TikTokers, and influencers earn from their content. Researchers should too. AIDOCERE is building the layer that pays scientists, doctors, and experts every time AI cites their verified work.
Blind LLM-judge evaluation. 8 topics. 2 baselines (Wikipedia + Google). 2 judges (Claude Sonnet 4.6 + Opus 4.7). Total cost: $5.56. Reproducible.
Live LLM-judge blind evaluation: AIDOCERE vs Wikipedia full-text AND vs Google top-5 web pages. Each evaluated by both Claude Sonnet 4.6 and Claude Opus 4.7. Measured 2026-04-25.
Same blind eval, broken down by topic. Click any of the 8 topics below to see actual measurements: tokens, speed, cost, accuracy, hallucinations.
| Path | Avg tokens | Daily cost (Sonnet) | Yearly cost |
|---|---|---|---|
| Google top-5 raw fetch + LLM | 19,355 | $65,565 | $23,931,225 |
| AIDOCERE API | 604 | $9,312 | $3,398,880 |
| Savings | −96.9% | $56,253 / day | $20,532,345 / year |
aidocere.com/api/aidocere?topic=X&tier=L2. Each topic answered by Claude (Sonnet 4.6 or Opus 4.7) using each source, then a separate Claude instance acts as blind judge — source masked — scoring 0-100 factual accuracy and counting hallucinations. AIDOCERE facts are paper-backed via real DOIs (UKPDS 1998, DPP 2002, Polack NEJM 2020, Vaswani 2017, AlphaFold2 Jumper 2021, Cipriani Lancet 2018, etc.). All sources CC-BY or CC-BY-SA. Total benchmark API cost: ~$3.70 across 4 cells × 8 topics.
Two views of the same response. The left side is the simplified UI presentation; the right side is the actual JSON returned by the live API call shown above each.
https://aidocere.com/api/aidocere?topic=diabetes&tier=L2{
"q": "diabetes",
"tags": {
"diabetes": "endocrine_disease",
"metformin": "drug_antidiabetic",
"insulin": "hormone_therapeutic",
"hba1c": "biomarker_clinical"
},
"sources": [
{ "i":1, "title":"UKPDS 34",
"journal":"Lancet", "year":1998,
"doi":"10.1016/S0140-6736(98)07037-8" },
{ "i":2, "title":"DPP",
"journal":"NEJM", "year":2002,
"doi":"10.1056/NEJMoa012512" },
{ "i":3, "title":"Standards of Medical Care 2024",
"journal":"Diabetes Care (ADA)", "year":2024 }
/* ...5 sources total */
],
"facts": [
{ "claim":"metformin: first-line oral for T2",
"v":"FACT", "s":0.98, "refs":[1,3,5] },
{ "claim":"reduces HbA1c by 1-2 points",
"v":"FACT", "s":0.91, "refs":[1,4] },
{ "claim":"reduces all-cause mortality (UKPDS 34)",
"v":"FACT", "s":0.94, "refs":[1] },
{ "claim":"lifestyle cuts prediabetes 58% (DPP)",
"v":"FACT", "s":0.97, "refs":[2] }
/* ...9 facts total */
],
"verdict_summary": {
"fact_count": 9,
"false_count": 0,
"confidence": 0.962,
"sources_aligned": "multiple_independent"
},
"qa_ready": {
"metformin_role": "first-line, HbA1c 1-2%, ...",
"prevention": "lifestyle 58% risk reduction (DPP)"
},
"license": "CC-BY-SA + CC-BY"
}The left card is a presentation summary. The right card is the literal response from a live API call — every field shown is publicly callable today. Both surface the same verified facts grounded in real peer-reviewed DOIs.
We're onboarding a small number of design partners and enterprise customers before wider release. If you're building with AI and need verified data, get in touch.
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