Healthcare AI advisory & implementation
Independent AI advisory, workflow design, and targeted implementation for healthcare organizations. Start with the operational problem, work with the systems you already run, and build around a clear business purpose.
Services
Focused engagements shaped around the operational problem, the available data, and your organization’s capacity to change.
Identify useful AI applications, assess workflow and data readiness, and prioritize opportunities against expected value, risk, and implementation effort.
Typical output: a prioritized opportunity assessment and phased roadmap.
Map and improve claims, enrollment, authorizations, patient access, and revenue cycle workflows. Identify where automation, AI, and human judgment belong.
Typical output: current and future workflows, handoffs, and measures of success.
Design data flows and integration requirements across existing platforms, including EDI, Salesforce, PBM, and CMS data feeds.
Typical output: solution architecture, data flows, and integration requirements.
Define requirements, prototype solutions, and lead delivery with contract engineering support where needed. Plan review controls, acceptance criteria, and the operating model for adoption.
Typical output: a scoped build or prototype, delivery plan, and adoption approach.
How engagements work
Begin with a bounded problem. Agree the scope, responsibilities, and success measures before expanding the work.
Start with the workflow, baseline performance, systems, data, and the people who do the work.
Develop the architecture, a focused prototype where useful, review checkpoints, and a phased delivery plan.
Coordinate engineering and operational delivery, define acceptance measures, and plan training and ownership.
Working demonstrations
Synthetic-data examples used to explore requirements and support scoping. Each is labeled by its current stage.
Architecture combining document intelligence, X12 EDI parsing, a data layer, and LLM reasoning, with human-review checkpoints. Used to explore how structured data and AI-assisted review can work together.
A prototype exploring Medicare Part D adherence reporting, at-risk worklists, and operational follow-up. Used in solution scoping to make reporting and workflow requirements concrete.
About Averra
Founded by Amanda North in June 2026, Averra is an independent healthcare AI advisory and implementation practice.
Amanda brings 20+ years of healthcare operations and transformation leadership across payer and provider organizations, including global teams, commercial agreements, revenue cycle, and platform-led delivery.
Meet Amanda North ↗ (opens in a new tab)Architecture and workflow design account for data access, human review, customer requirements, and the organization’s governance responsibilities.
Early scoping and demonstrations use synthetic data. Client-data handling is agreed separately, including appropriate access controls, security arrangements, and contractual requirements.
Start a conversation
Tell Amanda about the operational constraint and the outcome you want. The first conversation will help identify whether an assessment, design engagement, or targeted build is the right next step.
Please keep initial inquiries free of patient information and confidential data.