LLMs are already in your research workflow. The question is whether the outputs hold up.
Health economists, HTA teams, and medical scheme analysts are using AI for evidence synthesis. Most of it can't be submitted anywhere without a verification layer. We built that layer.
How verification actually works
Four layers between an LLM output and a defensible deliverable.
OpenAlex search
Every research question is grounded in real literature. 240M+ peer-reviewed papers searched directly, not recalled from an LLM's training data.
LLM synthesis
Retrieved papers are summarised and structured by a primary model. The output is treated as a draft, not a deliverable.
LLM council
A Bayesian council of independent models cross-checks every factual claim. Disagreement flags the claim for human review before it proceeds.
TruthSignal
Every surviving claim is DOI-resolved against its source paper. The abstract, methodology, and finding are verified before the citation is included.
The gap between LLM output and submission-ready evidence
AI accelerates research. It doesn't replace the verification that makes it defensible.
The hallucination problem
LLMs generate plausible-sounding citations that don't exist, or accurately quote papers that say something different. In health economics and HTA, that is a liability, not a draft.
Multiple models, one answer
Our LLM council routes claims through independent models via OpenRouter. Claims that survive cross-validation proceed. Claims that don't are flagged before they reach you.
Submission-ready output
ICMJE-compliant citation formatting, PRISMA-structured reviews, and a full audit trail. Built to go to a regulator, an ethics committee, or a journal without revision.