Workflow capture · 3 clinics
7,900
hours of consented clinic workflow footage captured and mined for automation
Each dot is 100 hours of footage
Open to full-time & part-time roles · Remote (US)
I sit with the people doing the work, map it, and build the agents, integrations, evals, and guardrails that take it over. At BAM that means prior authorization, payer calls, and EHR/FHIR integration for specialty medical practices, under HIPAA, with a live pilot and code in production.
AI Solutions Architect & EngineerForward Deployed AIHealthcare AI
01 Approach
Most AI never leaves the demo. I start where the work actually happens: on the clinic floor, on hold with payers, inside the EHR. I map it, then build agents that take it over, with evals, with guardrails, in production.
02 Proof
Workflow capture · 3 clinics
7,900
hours of consented clinic workflow footage captured and mined for automation
Each dot is 100 hours of footage
Voice agent · payer calls
~5×
lower modeled cost per payer call than commercial voice-agent platforms
Paid models stay idle through phone menus and hold: 54% of call time
Prior authorization · live pilot
56
prior-auth requests resolved in the first 13 days of a live clinic pilot, each with payer proof on file
8 approved · 48 confirmed not required · each square is one flagged request
Program leadership · since 2012
13+
years of global technical program leadership, from localization and IP operations to production AI
27-country rollouts · 9,000+ patent projects a year
03 Systems
Each started with discovery alongside the people doing the work and ended in production code, evals, and guardrails. Details are generalized to protect clients and patients.
Keep scrolling →
Routes every flagged case to the channel most likely to resolve it — clearinghouse e-filing, a payer-portal browser agent, an AI voice caller, published payer policy, or fax — under a deterministic state machine with an append-only evidence ledger.
Payer calls are mostly waiting. A gatekeeper state machine handles dialing, phone menus, and hold without paid models, then wakes a realtime voice model the moment a human picks up — and verifies the outcome in code.
A signed, MDM-deployed capture agent on clinic workstations, and a pipeline that turns the footage into PHI-redacted process maps, task inventories, and role specs.
ModMed’s certified HL7 FHIR R4 API via SMART Backend Services (JWT client assertions, JWKS), plus Availity eligibility (X12 270/271) and prior authorization (278) with a shared OAuth token cache.
A support copilot inside the admin console (RAG, live SQL, tool calling), and a 13-model pipeline that turns messy client files into structured app-build specs.
04 Under the hood
Every system I ship is observable: each model call, tool call, and guardrail is a span you can inspect. These are illustrative replays with synthetic data, modeled on the systems above.
Inspect a span
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modeltoolguardrailcode / ioidle (no model)
05 Capabilities
Every skill connects to the roles where I used it. Hover to trace the connections, click a node to read the résumé lines behind it, drag to rearrange.
Evidence
Pick a skill or a role
AI Solutions Architect and hands-on engineer who takes agentic AI from technical discovery to production in regulated, customer-facing settings. At BAM, a healthcare revenue cycle management (RCM) startup, designs and ships AI agents (voice, browser, and large language model (LLM) pipelines), EHR/FHIR and clearinghouse integrations, evals, and HIPAA guardrails, including a prior-authorization agent now live at a specialty practice. Brings 13+ years of global technical program management to stakeholder alignment, adoption, and measurable ROI.
06 Experience
The same content as the PDF résumé, line for line: 13+ years of operating discipline (vendor networks, quality programs, regulated delivery), now pointed at production AI.
AI-native revenue cycle management (RCM) platform automating eligibility, billing, and prior authorization for specialty practices.
Membership, loyalty, and commerce platform for medical aesthetics practices.
07 Ask the résumé
Ask anything about my background. A small retrieval engine (BM25 with synonym expansion) runs entirely in your browser over every line of this page, then quotes the matching résumé lines verbatim and links to each source.
No model writes the answer, so it can’t hallucinate, and nothing you type leaves the page. It’s the retrieve-then-cite pattern I build into production agents, scaled down.
Ask anything about my background. Claude answers using only the passages on this page, cites every claim with a numbered link to its source line, and says so when the page doesn’t cover something.
Your question goes through a small Cloudflare Worker to Claude, and this site doesn’t log or store it. If the model is unavailable, an in-browser BM25 search answers instead. It’s the retrieve-then-cite pattern I build into production agents.
08 Colophon
The same way I build agents: one source of truth, specialist agents, automated checks, and a human approving what ships.
Mined my own repositories for evidence, and 255 live job postings for the language hiring teams use.
The résumé is a single file. The PDF, the Word version, and this page are generated from it, so they can’t disagree.
Nine languages, every line fingerprinted: if the English changes, an outdated translation falls back to English instead of contradicting it.
Playwright for layout and interactions, axe-core for accessibility, and PDF text-layer checks so résumé parsers read every word.
Every claim is traced to evidence, and nothing ships until I approve it.
Build receipttyleryoung.org
Reviewed and approved by a human
09 Contact
Forward deployed, solutions architecture, applied AI, and AI enablement roles. Based in Tucson, Arizona, working remotely across US time zones.