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NetGuard

NetGuard provides AI‑native formulary validation that automates the review of payer contracts for pharmaceutical companies, reducing gross‑to‑net revenue leakage caused by rebate errors. Its agents ingest unstructured payer data, retain operational memory of payer behaviors and error patterns, and continuously improve with each cycle, creating a documented, auditable knowledge base for compliance‑sensitive contract operations.

Updated 27 days ago

Funding

Funding not disclosed

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

Pharma contract operations lose 2–3% of gross‑to‑net revenue due to manual, error‑prone formulary validation and rebate verification across heterogeneous payer data sources.

Solution

NetGuard deploys AI‑native agents that ingest and normalize unstructured payer information, then validate formulary eligibility and rebate calculations. The agents retain operational memory, continuously learning payer behaviors, error patterns, and resolution decisions to improve accuracy over time. Each validation cycle produces an auditable record, enabling compliance reporting and regulator confidence. By automating the end‑to‑end workflow, NetGuard reduces revenue leakage and frees analysts to focus on higher‑value tasks.

Target Audience

Primary customers are pharmaceutical companies’ contract operations teams and rebate management groups responsible for formulary validation and revenue assurance.

Features

  • AI agents that parse diverse, unstructured payer formats and portals without custom rule sets
  • Operational memory that captures and codifies institutional knowledge across validation cycles
  • Automated normalization and validation of formulary and rebate data with built‑in audit trails
  • Continuous model improvement through recursive training on resolved errors and payer responses
  • Real‑time monitoring dashboard displaying leakage metrics, validation status, and compliance logs
This profile is AI-generated and may contain inaccuracies.