The model is not the bottleneck.
Every AI product making health claims is doing the same thing under the hood. Retrieving from PubMed. Scraping guideline websites. Asking a large language model to “be careful” about medical accuracy.
That approach breaks at scale.
GPT-4 with verified clinical guidelines hits 96.4% accuracy with zero hallucination. The same model with unstructured sources leaves 30% of medical references unsupported.
Same model. Different infrastructure.
If Google's most advanced clinical AI is manually curating guideline documents one by one, the problem is not capability. It's the absence of a standardized evidence layer underneath.
Built for teams shipping health features without a medical research org.
Clinical AI companies
CDSS, diagnostic agents, treatment recommendation systems. You need verified evidence behind every output. Building that pipeline in-house takes 18 months and a team of MDs.
Consumer health hardware with AI
Wearables, sleep trackers, continuous glucose monitors, HRV coaches. The moment your product says “your data suggests X,” you own the accuracy of that claim.
Health and wellness apps
Symptom checkers, supplement recommenders, longevity platforms. Users expect answers grounded in real research, not generic LLM output.
One API. Every layer of the evidence stack.
Structured primary literature
Full-text clinical papers parsed into verified claims, evidence grades, and citation chains. Not snippets. Not abstracts.
Guideline integration
Major clinical guidelines structured into machine-readable evidence entries. Updated on release, not quarterly.
Multilingual coverage
Licensed access to Chinese academic databases through direct partnerships. The only English-accessible evidence feed that includes Chinese clinical research at scale.
DOI-traceable output
Every claim links to a specific paper. If we can't source it, we don't return it. Your compliance team gets the audit trail for free.
Backed by the research it's built to structure.
Two independent studies this year measured what happens when you give the same AI model different infrastructure layers.
Outperforming human physicians on preoperative assessments.
Same class of model. No structured evidence layer underneath.
The gap is not in the model. It's in what sits underneath it.
How teams use XEvidence.
Ground an LLM response in verified literature
Query our API alongside your LLM call. Returns structured claims with citations. Replace hallucinated references with real ones.
Validate health recommendations before they ship to users
Check your product's output against structured evidence in real time. Flag claims that can't be sourced.
Build evidence-grounded features faster
Skip the 18-month effort of curating your own medical knowledge base. Integrate in a week.
Early adopters.
- Used in clinical research workflows at Peking University Clinical Medicine.
- Adopted by a Johnson & Johnson business unit for regular literature investigation.
- Recognized by a fast-growing EdTech company as core research infrastructure.
- Paying users across 4 continents acquired with zero marketing spend.
Customer logos available under NDA. Contact us.
API access. Transparent pricing.
Usage-based after baseline. SLA, support, and access to the full evidence stack.
Get API accessHigh-volume inference, dedicated support, and custom integrations.
Contact us