Flock Overview

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Key Features

• Schema-aware — Connects to your database, analyzes the schema, and generates data that fits your table structures
• Epidemiologically grounded — Age, gender, race, and geographic distributions match real US Census data
• Comorbidity correlation — Diabetes correlates with hypertension, obesity with sleep apnea
• Temporal coherence — Admissions before discharges, lab orders before results
• Family linkage — Realistic household structures and family relationships
• Multiple outputs — SQL INSERT (FK-ordered), CSV, HL7 streams, FHIR Bundles

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Use Cases

• QA and integration testing with realistic volumes
• Development environment seeding
• Demo environments for sales and training
• Load testing with production-scale datasets
• Training new staff on EHR systems

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Workflow

# 1. Connect to your database
pidgeon flock connect --provider postgres --connection-string "Host=localhost;Database=ehr;..."

# 2. Optionally learn from existing data
pidgeon flock learn --tables patients,encounters --sample-size 500

# 3. Generate synthetic population
pidgeon flock generate --count 1000 --format sql

# 4. Seed the database
pidgeon flock seed

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Pricing