Everlign AI
Developer of a security-first, enterprise generative AI platform and integrated agent framework designed for regulated organizations (healthcare payers, government, defense). Offers on-premise and air-gapped deployments, unified ingestion of structured and unstructured data, retrieval-augmented generation with ensemble retrieval, prebuilt domain agents for payer workflows (claims, HEDIS, prior auth), and compliance, traceability, and human-in-the-loop controls.
Industries
Nr. of Employees
small (1-50)
Products
Integrated AI orchestration layer for payer workflows
Orchestration layer that unifies member data sources, routes agent outputs, and produces audit-ready workflows for payer operations (member 360, HEDIS automation, OCR ingestion, and real-time decisioning).
Integrated AI orchestration layer for payer workflows
Orchestration layer that unifies member data sources, routes agent outputs, and produces audit-ready workflows for payer operations (member 360, HEDIS automation, OCR ingestion, and real-time decisioning).
Services
Secure, on-premise generative AI platform for building, fine-tuning, and deploying AI applications on proprietary data with full traceability and compliance controls.
Integrated platform and prebuilt autonomous agents for payer workflows including claims analytics, HEDIS gap closure, prior authorization automation, member engagement, contract extraction, and payment integrity.
Architecture, deployment, and integration services for secure, compliant AI systems including air-gapped deployments, connector configuration (FHIR/HL7/REST), and governance implementation.
Secure, on-premise generative AI platform for building, fine-tuning, and deploying AI applications on proprietary data with full traceability and compliance controls.
Integrated platform and prebuilt autonomous agents for payer workflows including claims analytics, HEDIS gap closure, prior authorization automation, member engagement, contract extraction, and payment integrity.
Architecture, deployment, and integration services for secure, compliant AI systems including air-gapped deployments, connector configuration (FHIR/HL7/REST), and governance implementation.
Expertise Areas
- Enterprise generative AI for regulated industries
- Healthcare payer automation (claims, prior authorization, HEDIS automation)
- Data integration and interoperability (FHIR, HL7, CCDA)
- Retrieval engineering and RAG architectures
Key Technologies
- Open-source large language model fine-tuning (e.g., LLaMA, Mistral)
- Retrieval-augmented generation (RAG) with ensemble retrieval
- Vector embeddings and semantic search
- OCR and document entity extraction
News & Updates
Description of a three-layer benchmark (retrieval, generation, user experience) and methodology for testing AI knowledge assistants in regulated environments.
Results from the three-layer benchmarking framework, including retrieval hit rates, generation quality metrics, latency and cost implications of retrieval design.
Introduces a Model Context Protocol (MCP) that enables live, auditable queries to source systems (EHRs, claims) at inference time to address freshness and auditability gaps in standard RAG approaches.
Describes a modular agentic ingestion framework that converts unstructured payer documents into structured source data for RAG and downstream payer analytics.
Overview of a generative AI approach that integrates structured and unstructured enterprise data into a unified image to generate context-aware responses.
Application of virtual sensors and generative AI to predictive maintenance and fleet management, including LLM-based interfaces for diagnostics and prioritized maintenance actions.
Description of a three-layer benchmark (retrieval, generation, user experience) and methodology for testing AI knowledge assistants in regulated environments.
Results from the three-layer benchmarking framework, including retrieval hit rates, generation quality metrics, latency and cost implications of retrieval design.
Introduces a Model Context Protocol (MCP) that enables live, auditable queries to source systems (EHRs, claims) at inference time to address freshness and auditability gaps in standard RAG approaches.
Describes a modular agentic ingestion framework that converts unstructured payer documents into structured source data for RAG and downstream payer analytics.
Overview of a generative AI approach that integrates structured and unstructured enterprise data into a unified image to generate context-aware responses.
Application of virtual sensors and generative AI to predictive maintenance and fleet management, including LLM-based interfaces for diagnostics and prioritized maintenance actions.