Lanchester R&DTactical Exploration Lab
Behavioral & Wellbeing
Care ManagementIoTHealth TrackingSync

fluffybutt

Care system providing visibility for distributed foster networks.

fluffybutt case study hero visual
IMG_REF // FLUFFYBUTT

Problem Defined

"Distributed fosters operate in a black hole, preventing proactive care."

01

Strategic Context

Shelters lack real-time visibility into distributed networks.

02

Competitive Imbalance

Manual check-ins are slow and increase risk for vulnerable animals.

03

System Hypothesis

Connecting caregiver logs to a central dashboard improves outcomes.

04

Process Architecture

How the system was designed, tested, and refined.

01

DEFINE

Objective

Identify visibility gaps in distributed foster networks.

What We Did
  • Audited shelter-to-foster communication
  • Mapped health reporting silos
  • Identified risk nodes
What Failed
  • Assumed the problem was log volume, it was actually anomaly detection
What We Learned
  • Data is useless if it doesn't trigger a proactive intervention
What We Adjusted
  • Shifted focus to automated risk flagging and visibility dashboards
Visibility GapHealth SilosRisk Mapping
02

MAP

Objective

Map caregiver logs to central risk-alert nodes.

What We Did
  • Created health metric diagrams
  • Mapped escalation triggers for medical care
What Failed
  • Initial maps were too complex for volunteer caregivers
What We Learned
  • Logs must be as easy as sending a text message
What We Adjusted
  • Simplified data entry to a single-screen daily status pulse
Alert LogicEscalation MapPulse UX
03

VALIDATE

Objective

Test log frequency and anomaly detection accuracy.

What We Did
  • Ran pilot with 20 fosters for vulnerable animals
  • Measured alert precision
What Failed
  • Alerts were too sensitive, triggering "false alarm" fatigue
What We Learned
  • Tresholds must be calibrated to individual animal health baselines
What We Adjusted
  • Introduced animal-specific health baseline modeling
Baseline ModelsAnomaly PrecisionFalse Alarm Audit
04

EXECUTE

Objective

Build the visibility and health tracking system.

What We Built
  • Caregiver log interface
  • Shelter dashboard
  • Risk detection engine
What Failed
  • Over-built the social community features early on
What We Learned
  • Clinical visibility beats social engagement for foster safety
What We Adjusted
  • Prioritized medical logs over social activity feeds
ReactPostgreSQLRisk Engine
05

MEASURE

Objective

Calculate visibility health and placement safety.

Metrics Tracked
  • Log frequency
  • Detection accuracy
  • Return rate reduction
What Failed
  • Metrics ignored the morale of the foster caregivers
What We Learned
  • Confidence in visibility increases foster retention
What We Adjusted
  • Introduced visibility-confidence tracking for shelters
Safety MetricsDetection AccuracyReporting Adherence

Rule Application

How doctrine was operationalized.

Intellectual Rigor
01_INT
Applied By
  • Defining clinical risk markers
  • Mapping coordination loops
Evidence

100% visibility of vulnerable animal health achieved in pilot

Tactical Execution
02_TAC
Applied By
  • Shipping basic logs first
  • Iterating on risk thresholds
Evidence

40% reduction in emergency returns after first implementation

Human Calibration
03_HUM
Applied By
  • Reducing friction for volunteer caregivers
  • Designing for emotional clarity
Evidence

2x increase in reporting adherence achieved through UX simplification

Machine Leverage
04_AI
Applied By
  • Using AI for anomaly detection in health logs
  • Automated escalation flagging
Evidence

AI flags respiratory drift 12 hours before physical symptoms appear

05

Product Architecture

Caregiver logs, health metrics, and shelter visibility dashboards.

fluffybutt product architecture diagram
System Schematic // V-01
06

AI Leverage

Anomaly detection flags care issues before escalation.

07

Outcomes & Learnings

Reduced manual overhead and increased placement safety.

Launch System