AI-powered patient billing and revenue cycle automation for healthcare providers
Collectly automates patient financial workflows across intake, engagement, and payment collection using Python, FastAPI, React, and integrations with Epic and Salesforce. The stack signals a traditional healthcare enterprise play (EHR/PM connectors, Salesforce CRM), but the presence of Temporal, Puppeteer, and LLM-based work reveals a push toward asynchronous process automation and AI-driven support—anchored by Billie, an AI billing agent. Sales-heavy hiring (4 open roles) paired with simultaneous engineering and product scaling indicates a land-and-expand motion in mid-market health systems.
Collectly provides an AI-powered revenue cycle management (RCM) platform designed for healthcare providers—ambulatory groups, acute care systems, RCM vendors, and behavioral health organizations. The product spans digital intake, pre-service financial engagement, payment processing, and post-visit collections, with Billie functioning as an automated billing support agent that handles patient inquiries across chat, email, SMS, and voice. The company integrates with major EHR and practice management systems (Epic, Salesforce) to embed financial workflows into existing provider operations. Collectly targets cost reduction in collections, faster payment capture, and improved patient satisfaction across the revenue cycle. Founded in 2017, the company operates from San Francisco with 51–200 employees.
Collectly runs on Python, FastAPI, Flask, React, and TypeScript on AWS/GCP infrastructure, with PostgreSQL and Redis for data, Celery for task queuing, and Temporal for workflow orchestration. EHR/PM integrations use Epic Systems and Salesforce connectors.
Current projects include the Billie AI billing agent product line, LLM-based patient support automation, enterprise revenue motion and sales enablement, new product launch GTM, and implementation of SaaS solutions across healthcare facilities.
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Collectly'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.