GoML offers a generative AI platform that integrates with existing data ecosystems to provide natural‑language, real‑time analytics and decision support for healthcare, pharma, and financial enterprises. By ingesting structured and unstructured data into a secure, compliance‑first pipeline and leveraging large language models on AWS Bedrock, the platform delivers AI‑driven summarization, risk prediction, and anomaly detection through role‑based dashboards and APIs, enabling faster, data‑driven decisions without extensive engineering effort.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises in healthcare, finance, and pharma struggle with fragmented data, manual analytics, and slow decision-making, leading to delayed insights, higher operational costs, and suboptimal outcomes.
Solution
GoML provides a generative AI platform that integrates with existing data ecosystems to deliver natural‑language, real‑time analytics and decision support. By leveraging large language models (e.g., Claude 3.5) on AWS Bedrock, the platform ingests structured and unstructured data, applies AI‑driven summarization, risk prediction, and anomaly detection, and presents results through lightweight, role‑based interfaces such as Streamlit dashboards. The solution includes secure data processing pipelines, automated masking and compliance controls, and scalable serverless infrastructure, enabling clinicians, analysts, and risk teams to query longitudinal patient records, detect disease patterns, or monitor financial transactions instantly without engineering bottlenecks.
Target Audience
Primary customers are large healthcare providers and hospital networks, pharmaceutical compliance teams, and financial institutions (banks or fintech firms) that need AI‑enhanced analytics on complex, high‑volume data.
Features
- End‑to‑end data pipeline: ingestion from EHRs, financial systems, or imaging sources into Amazon S3 data lake with automated validation, masking, and anonymization
- Generative AI copilot with natural‑language query capability for real‑time cohort analysis, diagnosis trend detection, and risk prediction
- Pre‑built domain models (e.g., retinal image analysis, transaction anomaly detection) fine‑tuned on AWS Bedrock and deployed on EC2/Lambda for low‑latency inference
- Role‑based, Streamlit‑powered dashboards that visualize longitudinal patient journeys, disease timelines, or fraud alerts
- Compliance‑first architecture: HIPAA‑grade security, IAM governance, audit logging, and data residency controls
- Scalable serverless compute (AWS Lambda, Docker containers) enabling 75%+ improvement in processing throughput and sub‑second response times
- API and integration layer for seamless embedding into existing clinical, asset‑management, or banking workflows