CodeSync RCM & CodeSync Capture
Work across CodeSync RCM and CodeSync Capture at Neuraoak / CodeSync Health, Inc., including Insurance AR queues, claim-check integrations, and charge-posting automation.
- Next.js
- TypeScript
- Supabase
- PostgreSQL
- RLS
- Healthcare operations
The product tension
Insurance AR operators needed faster access to claim queues and fewer repetitive checks before moving work forward. My contribution across CodeSync RCM and CodeSync Capture included those workflows, related integrations, and charge-posting automation.
What I built
At Neuraoak Technologies Private Limited / CodeSync Health, Inc., I work as an Associate Product Engineer across CodeSync RCM and CodeSync Capture. I owned Insurance AR workflows and queues, integrated payer checks and ERA/EOB extraction, and built Tebra charge-posting automation using API and UI paths.
For queue performance, I worked on selective field fetching, TTL caching, queue optimization, and warm/cold-start handling. Those changes addressed the path from requesting claim information to making it available to the operator.
How the system fits together
The work uses React, Next.js, TypeScript, and Supabase. Insurance AR page-load improvements included selective field fetching, TTL caching, queue optimization, and warm/cold-start handling. Payer APIs and ERA/EOB extraction support claim checks. Tebra charge posting combines API and UI automation.
The calls that shaped the build
- 01
Follow the complete queue-loading path
Selective field fetching, TTL caching, queue optimization, and warm/cold-start handling all contributed. The published result describes their combined effect rather than attributing it to one query or library.
- 02
Connect repetitive checks to integrations
Payer API integration and ERA/EOB extraction supported claim checks; Tebra charge posting combined API and UI automation.
- 03
Keep tenant access part of the data model
Supabase RLS-based tenant isolation was implemented across development, staging, and production environments.
Trust boundaries
I implemented Supabase RLS-based tenant isolation across development, staging, and production environments. This public account describes the work at a category level.
What I chose—and accepted
The outcomes below are previously reported project results, not a new controlled benchmark. They do not isolate the effect of each optimization, and platform volume and uptime are not individual output, capacity guarantees, or an SLA. This case study keeps employer implementation details and operational records private.
Why the work matters
- Insurance AR page-load time: approximately 15 seconds → 2 seconds, approximately 87% lower elapsed time.
- Manual claim checks: reduced by 70% through payer API integrations and ERA/EOB extraction.
- Feature-development time: reduced by 30% through shared-component consolidation.
- Observed platform volume: 2,500+ claims per month for one client. This is platform volume, not individual output or a load-tested capacity.
- Platform uptime: 99.2%. This is a platform-level result, not an SLA or a claim of sole responsibility.
What I would improve next
Add approved measurement context, including the compared workflow and test conditions, before presenting a more detailed performance breakdown.