The evidence layer for biomedical AI
Medical research, gathered and processed into structured, source-grounded AI-ready evidence.
Bring a research question. We find the relevant studies, read everything including the tables and figures, and turn it all into structured evidence, with every value traced back to the exact place it came from. Plug the data into Claude, ChatGPT, Excel or your own AI stack.
- Papers
- Clinical trials
- Tables
- Figures
- Supplementary materials
Private early access for pharma, biotech, research and healthcare-AI teams.
Table 2: Efficacy outcomes, page 8
Median progression-free survival was 11.2 months (95% CI, 9.8–12.6) in the Treatment A arm versus comparator, with overall survival of 19.8 months (95% CI, 17.2–22.4).
Plug into your AI stack
What we gather
Every place a result can hide.
One comparison can depend on a single table row, a point on a survival curve, and a subgroup analysis on page 143 of an appendix. Noetica gathers all of it for your research question and prepares it for AI use.
Papers and preprints
Full text, methods and results prose, including the findings that never make it into the abstract.
Clinical trials
Arms, populations, interventions, endpoints and effect sizes, reconciled between the publication and the trial registry.
Tables
Baseline characteristics, efficacy endpoints, hazard ratios, confidence intervals and adverse-event counts, extracted row by row.
Figures
Kaplan-Meier curves, forest plots and dose-response charts, with values recovered from the plot itself, not just the caption.
Supplementary materials
Subgroup analyses, full statistical methods and extended safety tables, often running to hundreds of pages.
Your corpus, or ours
Bring your own PDFs and internal documents, or give us the research question and we assemble the literature for you.
Medical evidence is still trapped in documents.
Important findings are distributed across prose, tables, figures and supplementary files. Research teams extract the same information by hand, over and over, while general-purpose AI tools can produce answers that are difficult to verify.
Evidence is fragmented
Outcomes, populations and safety data are spread across multiple formats and publications, so a single comparison means opening a dozen papers.
Answers are difficult to audit
A citation to an entire paper is not enough when the relevant value came from one table row or figure.
The same work is repeated
Researchers and commercial teams rebuild the same evidence tables by hand, then rebuild them again when a new trial publishes.
How it works
From publication to evidence object.
Gather
We find the relevant papers, trials, tables, figures and supplementary materials for your question, or you bring your own.
Understand
Identify trials, populations, interventions, endpoints, outcomes and adverse events.
Structure
Convert findings into consistent, machine-readable evidence objects.
Trace
Preserve links to the exact passage, page, table, figure or source region.
Every extracted result becomes a structured evidence object: consistent enough to query, and still connected to exactly where it came from.
{
"trial": "Example Phase III Trial",
"population": "Advanced NSCLC",
"endpoint": "Progression-free survival",
"value": "11.2 months",
"confidence_interval": "Illustrative",
"source": {
"paper": "Example publication",
"page": 8,
"table": "Table 2"
}
}Built for people and for AI systems.
Use it through Claude or ChatGPT
Give your team access to source-grounded biomedical evidence through the tools they already use. Ask research questions in natural language and inspect the evidence behind every answer.
Across the two Phase III first-line NSCLC trials you selected, Treatment A shows longer median PFS and OS, with a lower rate of grade 3+ adverse events than Treatment B.
Build with the API and MCP server
Add structured biomedical evidence to your own products, research agents and internal workflows.
Request
search_evidence({
"condition": "advanced NSCLC",
"phase": 3,
"endpoints": [
"PFS",
"OS"
],
"include_sources": true
})Response (illustrative)
Example Publication A, Table 2, p.8
Example Publication B, Figure 3, p.11
Example Publication A, Table 2, p.9
One evidence layer. Multiple research workflows.
Pharma and biotech
Systematic literature reviews, HTA and market-access dossiers, competitive trial landscapes and safety literature monitoring, with every number a reviewer can audit.
Medical research
Screen the literature, build evidence tables and run meta-analyses without hand-extracting a single value from a table or a survival curve.
Healthcare-AI teams
Ground agents and applications in structured biomedical evidence with provenance you can inspect and show to a regulator.
More than search. More than a chatbot.
Structured evidence
Trials, populations, interventions, comparators, endpoints, results and safety data represented consistently.
Source-level provenance
Trace every extracted result to its original passage, page, table, figure or supplementary file.
Human-reviewable
Inspect the source behind an answer before relying on it.
Integration-ready
Use the evidence through a web workflow, Excel export, API, MCP, Claude or ChatGPT.
See what source-grounded evidence looks like.
“Compare efficacy and safety outcomes across Phase III first-line trials in advanced NSCLC.”
Publications considered: Example Publication A: Phase III NSCLC Trial · Example Publication B: Phase III NSCLC Trial
Help shape the research layer biomedical AI needs.
We are working with a small group of pharma, biotech, research and healthcare-AI teams. Design partners receive private early access and help determine which evidence types, workflows and integrations we build first.
- Test Noetica on a real research question
- Influence the product and evidence schema
- Receive early API, MCP or AI-assistant access
No payment or long-term commitment is required during the validation phase.
Bring us a real research question.
Tell us what your team is trying to understand. We’ll explore how the relevant literature could become structured, reviewable and source-grounded.