Data becomes evidence, with intelligence you can verify.
The interpretive layer of the ecosystem. AI-generated evidence, domain-specific LLMs and responsible-AI guardrails, built for scrutiny.
Raw data is not evidence. Evidence is data plus method, provenance and defensibility.
Every dataset we work with is captured with structured methodology, ontology-backed coding and an immutable audit trail, so what leaves the study is defensible at regulatory, editorial and clinical level.
Analytics, AI and reporting sit on top of that foundation. The intelligence is only as trustworthy as the pipeline underneath, that's why ECS® and AI Applied are engineered as one.
Where classic evidence stops, methods extend the reach.
Synthetic cohorts
Model-generated populations that mirror real-world distributions, for feasibility, power calculation and sensitivity analyses.
External control arms
Historical and real-world data assembled into comparator arms where randomised controls are infeasible, applied responsibly, in line with emerging regulatory precedent.
Clinical Intelligence
Domain-specific LLMs trained on curated corpora, for extraction, coding, literature synthesis and dossier drafting.
Deep Dialogue Technology
Study-specific NLP that reads unstructured EHR data and delivers the variables the protocol needs directly into the eCRF (see Real-World Evidence).
Verifiability over explainability.
An answer that cannot be traced is not evidence, regardless of how well it explains itself. Every AI output in Evidilya ships with the chain of provenance that produced it.
Six commitments on every study.
Provenance
Every output traces back to source records, prompts and model versions.
Verifiability
Answers ship with the evidence chain that produced them.
Bias monitoring
Subgroup performance is measured, reported and mitigated.
Human-in-the-loop
Clinical and methodological experts sign off critical outputs.
Audit trail
Immutable logs for every generation, edit and approval.
Privacy by architecture
Compute-to-data via ECS®, records stay where they live.
Verifiability over explainability.
Bring us your evidence question.
We'll show you what data, method and AI can, and cannot, do for it.