01
Multi-Variable Eligibility Testing
Audit model performance across complex numerical lab thresholds—eGFR, ANC, LFTs, and washout windows—where single-variable parsers silently fail.
Enterprise AI Evaluation for Life Sciences
The Trust & Evaluation Engine for Biomedical AI.
We benchmark, stress-test, and audit domain-specific AI models for Clinical Operations, Regulatory Filings, and R&D—ensuring citation-level fidelity and zero hallucinations before models touch high-stakes decisions.
🔒 Enterprise privacy ready. No internal data exposure.
The Biomedical AI Precision Benchmark
Standard LLMs fail on complex domain logic. We evaluate AI models against high-stakes biomedical reasoning tasks—so you know exactly where your pipeline stands.
02
Adverse Event & Safety Signal Audit
Stress-test LLM reasoning against FAERS and concomitant drug risk datasets to catch hallucinated safety signals before they reach regulatory review.
03
Cross-Source Discrepancy Detection
Measure model ability to catch endpoint revisions, date shifts, and claim mismatches across CT.gov registrations and PubMed publications.
04
Structural & Protocol Logic Evaluation
Identify hallucinated assumptions in study design, surrogate endpoint validity, and statistical power liabilities before they derail a trial.
Why Standard AI Fails Biomedical Reasoning
Generic models collapse on compound eligibility, unstructured clinical intent, and cross-document logic. Our evaluation engine surfaces those failures before deployment.
Generic LLM / Billing-Code NLP
"Patients with eGFR ≥45 mL/min/1.73 m² and no prior anti-PD-1 therapy within 6 months."
Misread / Missed: Misinterprets the 6-month washout rule and relies on ICD/CPT billing codes, missing eligible candidates documented only in unstructured physician notes.
BioSyntactic Evaluation Engine
"Patients with eGFR ≥45 mL/min/1.73 m² and no prior anti-PD-1 therapy within 6 months."
Exact Intent Extraction: Parses clinical rationale from unstructured notes, links constraints to Protocol Section 4.2, and outputs verified eligibility signals with full citation lineage.
Evaluated Enterprise Workflows
We partner with life science teams to audit and improve AI performance across their highest-risk domains—not sell pre-built integrations that don't exist yet.
Clinical Operations
Protocol feasibility and eligibility evaluation. Audit RAG retrieval accuracy, inclusion/exclusion parsing, and compound lab-threshold reasoning.
Regulatory Filings
Regulatory dossier and citation lineage audits. Validate MLR cross-references, eCTD module consistency, and global submission accuracy.
R&D & Discovery
Target and literature context validation. Stress-test extraction from PubMed, patent literature, and genomic databases for noise-free signal.
Domain-Exact Signal. Zero Context Noise.
Our evaluation layer filters, benchmarks, and validates biomedical context before it reaches your reasoning engines.
Uncompromising Accuracy
Pre-filter and compress complex scientific and regulatory syntax before it reaches your models, cutting hallucination rates to near zero.
Accelerated Validation Cycles
Slash weeks off AI pipeline validation, regulatory review prep, and competitive landscape mapping while cutting inference costs.
Air-Gapped Enterprise Security
Connect proprietary internal pipeline data and sensitive submission files safely to evaluation workflows without exposing core IP.
Partner with BioSyntactic
We are selecting a limited cohort of biopharma and clinical teams to benchmark internal AI pipelines and audit domain models. Apply to join the Design Partner Program.
🔒 Enterprise privacy ready. No internal data exposure.