Nomi

Nomi is a voice-first companion that transforms daily symptoms, triggers, emotions, and medications into structured patterns healthcare organizations can act on.

Capturing lived experience at scale through natural conversation

Nomi uses natural speech to create structured, longitudinal symptom intelligence for chronic condition populations. Rather than forcing patients into forms, Nomi listens as they describe their day — extracting symptoms, triggers, medications, and behavioral patterns into a unified intelligence layer that generates real-world evidence healthcare organizations need.

Voice-First Logging

Natural speech capture without forms

Provider Sharing

Share reports with providers, family, and care team

Pattern Detection

AI-powered trend identification

Trigger Insights

Correlation mapping over time

Medication Tracking

Automated dosage logging

Community Support

Connect with similar journeys

Any pain flareups today?

The Problem

  • Chronic patients struggle to explain symptoms and recall patterns during brief visits
  • Clinicians get incomplete information for diagnosis and treatment
  • Digital health platforms lack real-world, longitudinal symptom data
  • Tracking apps have high friction and poor adherence
  • No scalable voice-first layer exists to capture patient experience

The Solution: Nomi

  • Voice-first logging through natural conversation
  • AI extraction to structured symptom, medication, and behavioral data
  • Pattern detection identifying symptom-trigger-medication relationships over time
  • Clinician summaries distilling months of experience into actionable insights
  • Real-world symptom dataset that scales for outcomes research

IP & Defensibility

Nomi's defensibility comes from three core assets that compound with usage: a proprietary symptom intelligence schema designed for chronic conditions, mapping symptoms, triggers, medications, emotional markers, and environmental factors; a longitudinal pattern graph that surfaces correlations over time (which triggers precede symptoms, how medications modulate patterns, which behaviors predict flares); and a domain-specific extraction pipeline trained for chronic condition language, handling medical nuance and patient vernacular. Together, these create a platform that becomes more valuable with scale — both for individual pattern detection and population-level insights.

Use Cases

Care Navigation

Monitor patients between visits

Pain/Neuro/GI Clinics

Longitudinal summaries for diagnosis

Digital Health

Embed voice tracking in platforms

Behavioral Health

Mood & trigger pattern capture

Employers

Support chronic condition populations

Pharma/RWE

Real-world evidence for research

Why Now

  • LLM reliability — extraction now accurate for healthcare with domain tuning
  • Strategic gap — orgs actively seeking longitudinal symptom intelligence
  • Rising chronic conditions — more patients need continuous monitoring
  • Voice-first moat — captures richer context with lower friction

Milestones

Platform

Native app rebuild

Infrastructure

HIPAA AWS backend

Intelligence

Speech → extraction → patterns

Clinical Layer

Clinician dashboards

Validation

B2B pilots (100–200 patients)

Outcomes

Engagement & clinical utility

Funding Ask

We are raising $500K–$1M in a pre-seed SAFE.

This capital funds 12–18 months of development, deployment, and initial B2B pilots.

Target: functional MVP within 90 days of funding.

Use of Capital

Product & Engineering

  • • Frontend rebuild (React Native)
  • • HIPAA backend infrastructure
  • • STT + LLM integration and tuning
  • • Pattern detection engine

Go-to-Market

  • • Clinician dashboard and summaries
  • • Compliance and security audits
  • • B2B pilot deployment (3–5 orgs)
  • • Outcomes measurement

Luanne K. Vreugdenhil, Founder

Luanne is a senior product and engineering leader with deep experience architecting AI-driven health applications, compliant AWS data infrastructures, and scalable multi-vendor ecosystems. She has built and led product and engineering teams across multiple healthcare and data organizations, consistently turning ambiguity into simple, high-impact systems. Her work combines hands-on technical credibility with strategic leadership, shaped by firsthand insight into chronic-care challenges through her own family’s experience. Operating at the intersection of product, engineering, AI, and business outcomes, she builds fast, leads with clarity, and delivers platforms that create measurable value.

A simple voice-first companion that helps people understand their bodies — and gives healthcare organizations the real-world insight they've never had.