Comparative Intelligence Pilot

How different types of residents interact with intelligent environments.

This cross-property pilot examines how automation, comfort routines, robotics cycles, and environmental patterns shift across different residency types.

The analysis spans all three homes within the Micro-Edge Neighborhood Node™.

Purpose

To identify how different resident lifestyles generate different environmental signatures—and how the Baccous Intelligence Stack™ can learn from these variations.

Residency Types Analyzed

  1. Short-term guests

  2. Mid-term residents

  3. Long-term residents

  4. Aging-in-place residents

Variables Studied

Environmental Patterns

  • temperature preferences

  • lighting behavior

  • movement pathways

  • occupancy timing

Automation Interaction

  • adoption frequency

  • override behavior

  • consistency of automation routines

Robotics Cycles

  • time of day

  • efficiency

  • navigation friction points

  • zone coverage

Energy Behavior

  • window AC usage

  • heating variation

  • lighting consumption

  • multi-device load patterns

Early Insights

  • Short-term residents adopt automation fastest due to “set it and forget it” preferences.

  • Mid-term residents maintain the most stable comfort patterns.

  • Long-term residents generate the richest environmental signatures for Baccous Intelligence™ learning.

  • Aging-in-place environments benefit most from safety lighting and predictable automation.

  • Robotics behavior differs significantly across residency types, revealing new optimization opportunities.

Roadmap

  • expand residence-type comparison

  • introduce cross-home local AI learning

  • enhance automation profiles based on residency patterns

  • robotics-cycle prediction

  • aging-in-place optimization pathways