Powering Generative AI with an Agentic Framework

At Everlign, we believe the foundation of a high-performing AI platform for healthcare payers lies not just in the large language models (LLMs) it uses, but in the quality, structure, and intelligence of the data that feeds those models. For health insurance companies managing millions of claims, members, and contracts, this distinction is everything.

That's why we've developed a powerful agentic framework as a core component of our healthcare payer data platform. This framework is designed to convert raw, unstructured payer data from claims documents to provider contracts into structured, actionable source data that drives our generative AI applications.

What Is the Agentic Framework?

Think of it as a modular and extensible system of intelligent AI agents, each responsible for a specific step in the data preparation pipeline. At the core is a foundational Entity Extraction Agent, which parses unstructured text and extracts domain-specific entities and terms.

What’s a Rich Text element?

The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.

Static and dynamic content editing

A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!

How to customize formatting for each rich text

Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.

Building Source Data for Healthcare Payer AI

Our agentic framework powers the data ingestion and source data generation pipeline for our Retrieval-Augmented Generation (RAG) architecture a critical layer within our revenue cycle of AI automation. This pipeline transforms unstructured payer documents into a rich, structured representation of question-and-answer pairs, page-level summaries, and metadata references. This structured data becomes the backbone of our payer analytics platform AI, enabling the system to trace, retrieve, and respond with accuracy and transparency across claims, contracts, and care programs.

Specialized Agents for Precision and Performance

By extending the base entity extraction class, we've developed specialized agents that directly serve healthcare payer operations

• Q&A Generation: Automatically generating likely payer and member questions with accurate, grounded responses fuelling smarter AI healthcare member support experiences.
• Keyword Generation: Producing high-quality keywords that enhance the precision of retrieval models within our healthcare data analytics platform.
• Summary Creation: Creating focused page-level summaries used directly by LLMs when responding to payer-specific queries around billing, contracts, and care programs.
• Domain Specific Agents: Extracting high-value information across payer domains from AI in claims processing for health insurance and prior authorization automation AI to healthcare fraud detection AI and multi-agent orchestration across member programs.

These specialized agents are orchestrated as part of a coordinated ingestion pipeline enabling automated and domain-specific source data generation tailored to each payer use case. The structured output feeds our retrieval models for both semantic and keyword search as part of our ensemble modeling approach, while page summaries power the generative side of our RAG pipeline.

Why It Matters for Healthcare Payers

n many AI solutions, the spotlight falls on the language model. But for AI for health insurance companies, what truly determines real-world success is the underlying data infrastructure. Our agentic framework automates and standardises the most critical parts of that pipeline ingesting, enriching, and structuring payer data at scale. Whether enabling value-based care analytics or powering intelligent claims decisions, it's this upstream innovation that allows Everlign to build GenAI solutions for health insurance companies that are trustworthy, traceable, and enterprise-ready.

‍

Want to see our Agentic Framework in action? Contact us to learn how Everlign's AI platform for healthcare payers can drive actionable insights and performance for your health plan.