MLR review automation. Medical writing assistants. RWE/RWD pipelines. Clinical-trial AI. Pharmacovigilance signal detection. Built with FDA context-of-use credibility, EMA/FDA joint guidance, and EU AI Act high-risk framing in mind from day one.
Each is a system we've shipped or have direct shipped credibility for. We don't take engagements outside this surface area unless we can name an adjacent build we've already done.
AI-assisted medical/legal/regulatory review on promotional content, with audit trails and citation grounding. Medical writing assistants that respect referencing discipline and HCP voice.
PRIMARY KPI · MLR cycle time · Reviewer agreement
Real-world evidence and real-world data pipelines for late-stage development and post-launch evidence generation. Cohort definition, confounding analysis, and submission-ready outputs.
PRIMARY KPI · Evidence cycle time · Submission-ready
Protocol design assist, site selection models, patient identification, and trial-document automation. Designed with FDA AI/ML guidance and ICH E6 in mind.
PRIMARY KPI · Enrollment velocity · Protocol deviations
ICSR processing, signal detection across spontaneous reporting and RWE sources, and case-narrative summarization. Built to survive a PV audit.
PRIMARY KPI · ICSR throughput · Signal-to-confirmation
from the asset
precedent match
vector DBflag conflicts
prior approvals
human-approved
Representative builds — the technology patterns we bring to life sciences. Where we cite results, they’re from work our team has shipped; everything else is what we’d build for you.
LS.T1 · LLM + RAG
Claims-library retrieval, prior-approval precedent matching, flagging against label language.
Stack · Claude/GPT · vector DB · eval harness
LS.T2 · Databricks
OMOP-standardized real-world data on Delta Lake, cohort building at scale, Context-of-Use-aware lineage.
Stack · Databricks · Unity Catalog · MLflow
LS.T4 · NLP/LLM
Case triage from unstructured AE reports, MedDRA coding assistance, signal-detection features.
Stack · LLM extraction · Snowflake or Databricks
FDA's January 2025 draft guidance on AI/ML credibility evidence. EMA/FDA joint statements on AI in medicines regulation. EU AI Act high-risk classification for medical AI. HIPAA / GDPR / DPDP for health data.
We design every life sciences engagement with a context-of-use statement and a credibility evidence plan from day one — not retrofitted at submission.
Our founding team has shipped NLP and GenAI systems in regulated industries adjacent to life sciences — including healthcare AI on GCP/Vertex AI integrated into clinical workflows, and NLP-driven process mining in regulated financial services.
Most first conversations are 30 minutes with a senior partner. We'll tell you whether it's a fit, whether AI is the right answer, and what we'd build first.