Sprint Examples

Context
A funded startup needs a fully operational QA environment that exercised real external integrations (clearinghouse, EMRs, payer validation, LLMs). The existing QA setup was unstable: async database errors, failing authentication, brittle tests, and incomplete EMR coverage prevented reliable end-to-end testing.
 

Sprint Goal (20 hours)
Deploy a production-like QA environment and restore confidence in end-to-end system behavior using real sandbox APIs.
 

What Shipped

• Deployed a fully functional QA environment with PostgreSQL, Redis, API, Celery workers, OAuth/JWKS services, and external sandbox integrations

• Fixed critical SQLAlchemy async pool misconfiguration, eliminating runtime errors across all environments

• Seeded QA authentication data to restore JWT-based auth flows and unblock downstream testing

• Wrote regression test suite to exercise real API endpoints instead of integration code, validating the system exactly as the frontend and users interact with it

• Expanded QA coverage from a single EMR to four FHIR-based EMR systems (Epic, Oracle Health, athenahealth, Meditech)

• Achieved 100% pass rate across infrastructure, clearinghouse, payer validation, EMR, claim generation, medical coding, and claim submission tests

Result
The QA environment became fully operational and demo-ready, with reliable end-to-end validation of all major external integrations. The team regained confidence in deployments, testing reflected real user flows, and the system was ready for customer pilots and demos — all within a single sprint.

Context
A funded startup needed faster, more reliable Dev and QA workflows. Environment setup was manual, image builds were inconsistent across machines, regression testing wasn’t environment-aware, and switching between dev (mock APIs) and QA (real sandboxes) was slow and error-prone.
 

Sprint Goal (20 hours)
Automate environment setup and regression testing so engineers could move quickly while maintaining confidence in real integrations.
 

What Shipped

• Built a Docker automation pipeline to consistently build and push Dev, QA, and mock service images, eliminating local build drift

• Introduced environment-specific regression test suites: fast mock-based tests for Dev and real sandbox tests for QA

• Updated startup scripts to pull pre-built images, start the correct services, and automatically run regression tests on startup

• Created a clean Dev/QA switch with predictable behavior (mock APIs for dev, real integrations for QA)

• Fixed a production-blocking eligibility endpoint bug and seeded realistic test data for end-to-end validation

• Reorganized test and documentation structure, enforcing clear “directory laws” to prevent future entropy

Result
Startup and test cycles dropped to seconds instead of minutes, environments became predictable across the team, and regression tests reliably validated real clearinghouse, EMR, and coding integrations. The team gained a repeatable Dev to QA workflow that scaled with new engineers and supported confident releases.

Context
A funded startup needed to automate medical coding from clinical notes while maintaining compliance, testability, and the ability to swap LLM providers. The system had to support modifier suggestions, compliance checks, and realistic evaluation without depending on live LLM calls during development.
 

Sprint Goal (20 hours)
Deliver a production-ready AI coding pipeline with clear interfaces, test coverage, and API endpoints.
 

What Shipped

• Implemented clinical note submission and processing pipeline

• Designed a clean LLM provider interface supporting pluggable backends

• Built a mock LLM using pattern matching to enable fast, deterministic testing

• Implemented code and modifier suggestion engines with specialty-aware logic

• Added compliance flagging to surface potential coding risks

• Created a feedback service to capture downstream corrections and learning signals

• Exposed functionality via REST API endpoints for frontend and integrations

• Added Celery background tasks and metrics for async processing

• Designed database schema and migrations for notes, suggestions, and feedback

• Wrote 25+ unit tests and 15+ integration tests with realistic fixtures

• Implemented support for multiple medical specialties with extensible rules

• Authored documentation explaining system behavior and tradeoffs

Result
The team shipped a complete, testable AI-assisted coding system that worked end-to-end in production-like environments. Developers could iterate quickly using mock LLMs, swap providers without refactors, and rely on strong test coverage — enabling safe expansion to new specialties and models.