A production CRM and lead tracking system for a US healthcare client, built with React, Django, Python, AI data extraction, Docker and Tailwind CSS.
200+
agents supported in production
20,000+
policies managed and tracked
Real time
agent, policy and lead monitoring
AI assisted
client data extraction and lead creation
Privacy safe portfolio preview
Screens shown on this portfolio are redesigned and sanitized with dummy data. Private client names, policy information, customer details and production records are not exposed.
Client
US healthcare client
My role
Full Stack Developer and CRM workflow engineer
Project type
SaaS, CRM, Healthcare operations, Lead tracking
This CRM was built to manage healthcare lead generation, agent performance, appointment tracking, policy sales, carrier reporting and operational visibility from one structured system. The platform is used in production by large agent teams and gives administrators, finance, back office users and agents role-based access to the workflows they need.
The business needed one reliable place to track leads, agents, policies, appointments, active time, paused time, attendance, carrier performance and sales activity. Before this system, teams had to double-check multiple places to confirm whether a client already existed, which agent handled the policy, and how each team or group was performing.
I built a centralized SaaS CRM with lead forms, duplicate-client detection, agent tracking, policy tracking, carrier reports, custom statistics, role-based dashboards and realtime monitoring. The CRM uses AI-assisted extraction to read client details from uploaded screenshots and auto-fill lead information, reducing manual entry and mistakes.
Developer notes
As the full stack developer on this project, I connected the frontend experience, backend data flow, permissions and reporting screens into one practical product. The goal was not just to make a good-looking interface, but to create a system that helps real teams work faster, find records confidently and make better decisions from clean operational data.
Screens shown on this portfolio are redesigned and sanitized with dummy data. Private client names, policy information, customer details and production records are not exposed.
This project uses practical automation patterns to reduce manual work, improve review speed and keep business data easier to manage. The exact private implementation is protected, but the portfolio preview explains the production value clearly.
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