PulseSync offers a no‑code, agentic platform that lets health plans, TPAs, and stop‑loss brokers automate workflows with pre‑trained AI skills, machine‑learning triggers, and LLM inference.
Funding
Funding not disclosed
Founders
Product
Problem
Healthcare AI initiatives often fail to reach production because they rely on black‑box models, require extensive data‑science expertise, and involve fragile integrations with legacy claim systems. This leads to long deployment times, regulatory concerns, and limited ROI for health plans, TPAs, and stop‑loss brokers.
Solution
PulseSync provides a no‑code, agentic platform that equips operations teams with over 148 pre‑trained, healthcare‑specific AI skills. Users assemble guided workflows that combine machine‑learning triggers, rule‑based logic, and LLM inference without writing code or managing models. Each decision is fully explainable, delivering plain‑language evidence trails that satisfy audit and compliance requirements. The platform connects directly to existing claim and clinical data sources, enabling real‑time fraud, waste, and abuse detection, cost‑containment analytics, and stop‑loss risk assessment. Deployments are completed in days rather than months, allowing health plans, TPAs, and brokers to realize AI‑driven efficiencies quickly.
Target Audience
Primary customers are health insurance plans, third‑party administrators, and stop‑loss brokers seeking to automate claim‑related workflows and improve fraud, waste, and abuse detection without investing in data‑science resources.
Features
- Library of 148+ pre‑trained AI skills for fraud detection, cost containment, stop‑loss, and denial prevention, ready for instant use
- No‑code workflow builder that lets ops staff configure machine‑learning triggers, rule sets, and LLM inference without data‑science expertise
- Glass‑box explainability with plain‑language evidence trails for every AI‑generated flag, supporting auditability and regulatory compliance
- Seamless integration with legacy claim processing and clinical systems via pre‑built connectors, avoiding custom development
- Real‑time processing of claims to surface fraud, overpayments, and coding errors as they occur
- Adaptive agents that continuously learn from new claims, improving detection accuracy without manual model retraining