HealthTech AI Analytics & Regulatory Compliance
An AI-driven clinical intelligence and regulatory compliance platform that accelerates medical research analysis while ensuring strict adherence to US healthcare standards.
- Client
- Under NDA
- Industry
- HealthTech / Clinical research
- Platform
- Web
- Services
- Solutions architecture, Full-stack development, UI/UX design, AI integration
The challenge
This project is protected by a non-disclosure agreement: some product details, clinical specifics, and visuals cannot be disclosed. We describe our work without revealing the client's product.
The product is an AI-driven clinical intelligence and regulatory compliance ecosystem for tier-one healthcare enterprises. It automates literature cross-referencing, multi-modal study evaluation, and regulatory alignment for scientific teams.
The domain set a demanding bar: massive volumes of unstructured medical data - journals, clinical trials, charts, and trial logs - that must be processed fast, visualized to scientific standards, and kept inside strict FDA, HIPAA, and GxP compliance boundaries.
What we built
OKET worked as a full-stack product engineer and solutions architect, owning the product end to end: user journeys and high-fidelity interfaces designed in Figma, the backend infrastructure, and the frontend architecture - translating intricate medical validation flows into seamless user experiences.
The backend is a secure, high-performance Python system with optimized data pipelines that ingest, parse, and clean multi-modal healthcare datasets - PDFs, clinical charts, and trial logs - with sub-second processing latency on massive documents.
On the AI side, we architected a Retrieval-Augmented Generation pipeline on vector databases that extracts, summarizes, and cross-verifies facts across millions of medical articles and clinical papers. An automated compliance-matching subsystem cross-references research methodology with FDA guidelines, GxP standards, and US healthcare licensing mandates to eliminate non-compliance risks.
The web application is a pixel-perfect, data-intensive Next.js frontend built to rigorous data-visualization standards: interactive analytics charts, real-time citation trees, and dynamic diff-viewers for comparing conflicting research conclusions. Underneath it all sits a Zero-Trust data-governance model: HIPAA-aligned security rules, leak prevention, and role-based access control.
End-to-end product engineering and design
One owner across the whole lifecycle - from user journeys in Figma to Python microservices and the Next.js frontend - cutting cross-departmental alignment friction.
AI-powered literature disambiguation
A Retrieval-Augmented Generation pipeline on vector databases extracts, summarizes, and cross-verifies facts across millions of medical articles and clinical papers.
US healthcare regulatory alignment engine
An automated compliance-matching subsystem cross-references research methodology with FDA guidelines, GxP standards, and US healthcare licensing mandates.
High-performance Python data orchestration
Optimized pipelines ingest, parse, and clean multi-modal healthcare datasets - PDFs, clinical charts, trial logs - with sub-second latency on massive documents.
Scientific dashboards and visualizations
A data-intensive Next.js application with interactive analytics charts, real-time citation trees, and dynamic diff-viewers for conflicting research conclusions.
Zero-Trust security and data integrity
Strict data-governance models keep processed research and user data within HIPAA security rules, preventing leakage and enforcing role-based access control.
Results
~50%
Less cross-departmental alignment friction with one owner across design and engineering
<1s
Processing latency for massive medical documents
1M+
Medical articles and clinical papers covered by the RAG pipeline
Technologies
- Python
- Next.js
- React
- TypeScript
- LLM & AI APIs
- RAG Pipelines
- Vector Databases
- Semantic Search
- Zero-Trust Architecture
- RBAC
- Figma
Services used
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