Droxi builds an AI-powered inbox layer for EHR systems, targeting the high-friction workflows that consume clinician time—refills, patient messages, lab results. The tech stack reveals a mature ML/LLM platform: Python + LangChain + Hugging Face + RAG, deployed on Kubernetes + AWS EKS, with observability via Prometheus/Grafana/ELK. Engineering-heavy hiring (3 engineers for 5 total open roles) signals active infrastructure scaling; the project list (CI/CD, server-side ML infrastructure, back-end/front-end complexity) confirms they're building production AI systems, not just wrapping existing APIs.
Droxi is a Boston-based healthtech company (51–200 employees) that develops an AI inbox companion for electronic health record users. The product targets clinician burnout by automating repetitive EHR tasks—message triage, refill processing, lab result handling—without requiring workflow changes or adding UI complexity. The company sells into hospital and clinic networks. Deployment spans the United States and Israel, with active infrastructure work on high-availability systems and continuous deployment pipelines.
Python, Angular, AWS (EKS), Kubernetes, Docker, Terraform. ML layer: LangChain, Hugging Face, RAG, LoRA, MLflow. Observability: Prometheus, Grafana, ELK stack (Elasticsearch, Logstash, Kibana).
Boston, Massachusetts. Active hiring in the United States and Israel.
Droxi's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.