Investor summary
Premium menopause intelligence for modern provider organizations
Pause-Health.ai transforms fragmented menopause care into an elegant, measurable, and clinically explainable workflow built for provider excellence.
- Focus cohort: women ages 40-60 navigating perimenopause and menopause with nuanced, evolving symptom profiles.
- 67% are initially misdiagnosed; the average path to accurate diagnosis can extend to 2.5 years.
- Pause target: 89% AI-assisted triage accuracy with transparent, evidence-linked rationale.
- FHIR-native data and wearable biomarkers create a real-time menopause intelligence layer.
- Commercial strategy: provider-first B2B model delivering measurable ROI and durable ARR growth.
Part 2 · Deep dives
Customer Selection
Health system and value-based payer ICPs, buying committee, and market sizing.
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Customer Insights
Themes from 32 provider and 47 patient interviews — what they said, in their words.
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Data Inventory
Available menopause datasets, our data strategy, and the moats we accrue over time.
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Competition
DTC, employer benefits, EHR AI, generalist LLMs — landscape and where Pause wins.
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Digital Strategy
Architectural pillars, go-to-market motion, and defensibility flywheel.
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Technology Choices
Stack, AI approach, evaluation framework, and safety stance.
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JupyterHealth Integration
How Pause composes with JupyterHealth — open FHIR substrate and customer-controlled deployment.
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DBDP Feature Engineering
Wearable feature engineering via the Digital Biomarker Discovery Pipeline (Duke) — production-grade HRV, EDA, and accelerometer signals.
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Menopause Society
How Pause composes with The Menopause Society and the MSCP credential — referral, partnership, and earned trust.
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Provider Graph
A defensible menopause provider graph from CMS NPPES and state board data — ToS-clean, closed-loop, compounding.
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Agentforce Intake
Patient intake on Salesforce Agentforce Service Agent — runs on the substrate our health-system customers already operate.
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MuleSoft Integration
Integration plane on MuleSoft Anypoint — three-tier API-Led Connectivity stitching JupyterHealth, DBDP, and wearables into a single FHIR substrate.
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MCP Server
Pause as a tool surface for AI agents — a Model Context Protocol server fronting the MuleSoft Experience APIs for Claude Desktop, Cursor, and Agentforce.
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Agent Fabric
Four agents, two open protocols, one governed control plane — Agentforce + Anthropic Claude + Pause MCP + MuleSoft, orchestrated by Agent Fabric.
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Data 360
Unified patient memory grounded in Salesforce Data 360 — zero-copy federation across JupyterHealth, DBDP, and the EHR-of-record, making every agent visibly smarter.
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