01
OMNICHANNEL_AGENT
One agent runtime coordinates patient conversations across WhatsApp, Telegram, SMS, voice, and email while preserving channel-aware interaction rules.
02 · Healthcare / Agentic AI
MedReclaim AI case study.
A multi-tenant healthcare-recovery agent with omnichannel conversations, compliance checks, and human escalation.
AI Systems Architect2026Implementation-ready
Project overview
What was built and why it mattered.
A concise project view first, then the longer narrative and implementation notes below.
MedReclaim addresses healthcare debt recovery, where an AI interaction must be effective without being careless with patient data, disputed accounts, or sensitive personal context. It was designed as a multi-channel operating system rather than a chat widget.
I architected a separation of concerns: an agent gateway runs conversation and campaign logic; a stateless FastAPI service provides tenant-scoped tools, integrations, identity enforcement, audit logging, and compliance controls. This keeps the AI layer useful without giving it unchecked authority over sensitive data.
The implementation plan covers WhatsApp, Telegram, SMS, voice, and email; payment and document workflows; insurance-remittance analysis; and explicit human escalation. It is a strong example of applied AI systems design in a regulated workflow.
Key systems
The pieces that carried the product.
This is the short implementation map before the fuller markdown write-up.
02
SECURITY_BOUNDARY
A FastAPI tool server independently enforces provider isolation, patient identity checks, PHI filtering, and immutable interaction logging.
03
RECOVERY_WORKFLOWS
Payment plans, promises to pay, billing-dispute pauses, documents, remittance analysis, and escalation are modeled as real operational work.
04
DEPLOYMENT_ARCHITECTURE
Containerized services use private service-to-service networking, PostgreSQL, async Python, external payment and communication providers, and health checks.
Detailed notes
Long-form case study
The fuller project narrative, results, and architecture notes from the markdown source.
The challenge
Healthcare debt recovery is a difficult automation problem. A system needs to work across the channels people actually use, explain bills and insurance remittances clearly, enable payment action, and still protect sensitive data, respect disputes, and hand difficult matters to people.
MedReclaim was designed as a multi-tenant healthcare-recovery platform, not a generic chat assistant. The objective was to make the agent useful while keeping the security and compliance boundary outside the model runtime.
My role
I designed the AI systems architecture: the split between the agent runtime and the FastAPI tool server; tenant and identity enforcement; channel integrations; compliance controls; payment and document flows; and the health-data model required for auditable recovery operations.
Architecture
The key design choice: the agent is not the security boundary
The agent gateway handles conversation state, campaign behavior, and tool invocation. The FastAPI service is deliberately stateless and independently validates every sensitive operation. It scopes data by provider, confirms patient identity against the session context, applies channel-specific PHI filtering, and writes audit records.
That separation is essential because an AI runtime should not be trusted to enforce multi-tenant access simply because its prompt says it should.
Workflow design
Omnichannel by design
The agent can participate across WhatsApp, Telegram, SMS, voice, and email, while the backend handles provider-specific callbacks and normalizes events. The same underlying account, payment, and escalation context can follow a patient across channels.
Recovery, not just conversation
The platform models accounts, itemized charges, insurance information, remittances, payments, payment plans, promises to pay, generated documents, scheduled actions, and escalations. This gives the agent tools that map to actual work instead of a single text-response endpoint.
Safety controls are first-class
Contact rules, PHI filtering, billing-dispute pauses, tenant isolation, patient identity checks, and interaction logging sit in the backend. When a request needs human judgement, the platform creates an escalation instead of improvising a decision.
Implementation foundation
- FastAPI, async SQLAlchemy, PostgreSQL, Alembic, Pydantic, Docker, and health endpoints.
- An agent gateway with tool-oriented skills, session management, scheduled campaigns, and model fallbacks.
- Payment, PDF generation, SMS/voice, TTS, and channel webhook integrations.
- Private service-to-service networking and a multi-tenant data model designed around healthcare providers.
Delivery status
Implementation-ready architecture. The case study reflects the documented product and technical implementation, rather than claiming production outcomes that have not been independently measured.
What this case study proves
MedReclaim demonstrates the AI systems judgment behind a senior engineer’s work: agent orchestration, security boundaries, compliance-aware product design, asynchronous Python services, and practical multi-channel automation.