Vetted AI engineers alongside healthcare-savvy functional experts, so your AI initiatives move from idea to production without a year-long hiring cycle.
Hiring AI talent is slow, expensive, and risky. Even a strong engineer often doesn't understand payer rules, EDI transactions, or why a CO-197 denial matters.
Many AI projects fail in the gap between the people who build the model and the people who run the workflow.
Every pod blends builders with translators, so what gets built fits how your revenue cycle actually works.
AI, ML, data, automation, and integration engineers who ship production systems.
RCM analysts, product owners, and change leads who turn payer rules and workflows into requirements, and results into adoption.
Sixteen roles, five stages. Your pod draws from each stage as the work moves forward.
Designs target architecture, selects models and platforms, and plans integration with your PM/EHR and clearinghouse.
Maps workflows, denial patterns, and payer rules into clear requirements.
Owns the backlog, prioritizes by ROI, and ties delivery to business outcomes.
Builds pipelines from 835/837 files, PM systems, and payer portals into usable datasets.
Creates dashboards and baselines to measure impact.
Handles PHI, access controls, and HIPAA-aware data practices.
Builds predictive models such as denial risk scoring, propensity-to-pay, and underpayment detection.
Builds appeal letter generation, summarization, and AI agents with human-in-the-loop review.
Automates portal work such as eligibility, claim status, and prior auth checks.
Connects AI to EHR/PM systems, clearinghouses, and payer APIs (FHIR, HL7, X12).
Tests accuracy, edge cases, and bias before anything touches a live claim.
Handles deployment, monitoring, versioning, and cost control.
Checks outputs against payer policy, HIPAA, and audit requirements.
Prepares teams, redesigns roles, and drives adoption.
Trains staff to use new tools confidently, linked to the AI Academy.
Tracks KPIs after launch and finds the next opportunity.
Start small, prove value, then add capacity. Each pod builds on the one before it.
Build Pod plus MLOps, Change Management Lead, and Enablement Specialist. For organization-wide rollout.
Starter Pod plus Data Engineer, ML/LLM Engineer, and QA. For multi-use-case programs.
AI Solutions Architect, RCM Business Analyst, and AI/Automation Engineer. One use case, proof of value in 8–12 weeks.
From first call to a working pod, with BAA and security setup done along the way.
We understand your goals, stack, and gaps.
We propose roles, seniority, and timeline.
You interview pre-vetted profiles.
Your pod starts working within 2–3 weeks, with BAA and security setup completed.
Technical assessments on real healthcare data scenarios, domain screening on RCM concepts, communication checks, and background verification.
US time-zone overlap is part of how every pod is structured, not an afterthought. Standups, sprint reviews, and escalations happen during your working hours.
We will design a pod around your goals, stack, and timeline.
Build your pod