core analysis module

Drift & Dwell Engine

Detect and contextualize execution drift and dwell as they develop.

The Drift & Dwell Engine identifies when movement gradually departs from expected patterns, when activity stalls or lingers, and when those behaviors persist long enough to signal risk, fatigue, uncertainty, or degraded execution, while correction is still possible.

KEY SIGNALS SURFACED
  • Drift onset timing relative to mission intent
  • Magnitude and direction of sustained drift
  • Dwell duration at non-progressing locations
  • Repeated drift-dwell patterns across sessions
  • Recovery latency after drift or dwell events
  • Sustained drift without corrective movement
readiness intelligence

Why This Signal Matters

Most mission degradation does not come from a single wrong decision. It develops through gradual drift away from intent, prolonged stalling or circling, and hesitation that compounds unnoticed over time. These behaviors often go unflagged because they don’t trip discrete alerts. Teams are left guessing whether hesitation was environmental, situational, or instructional.

The Drift & Dwell Engine isolates gradual misalignment and sustained non-progress as measurable signals. It makes time spent outside expected movement patterns visible while correction is still possible. This allows teams to intervene before hesitation compounds into failure and before context is lost.

What Becomes Visible

This module turns time-based movement behavior into actionable evidence by isolating when progress slowed, stalled, or drifted away from expected execution patterns.
Execution Signals Exposed
  • Where movement began drifting from intent, not just where it failed
  • Where progress stalled or lingered without correction
  • How long drift or dwell persisted before recovery
  • Whether drift or dwell was environmental, instructional, or behavioral
  • Repeated drift–dwell patterns across missions or trainees
Decisions This Enables
  • Earlier instruction intervention
  • Objective identification of hesitation or overload
  • Reduced post-hoc narrative reconstruction
  • Clear separation of momentary error vs systemic friction

How Teams Use This Signal

During Operations or Training
  • Detect sustained drift or dwell before it compounds
  • Flag hesitation without stopping the exercise
  • Preserve context around why progress slowed
During Review and Evaluation
  • Compare dwell duration across scenarios
  • Identify environmental or instructional bottlenecks
  • Anchor feedback in observable time-based behavior
Integration snapshot

Integration & Deployment

Designed for direct adoption inside existing mission systems.

Data Interface
Inputs
  • Executed movement telemetry (CSV, JSON, Parquet)
  • Mission timestamps and segment markers
  • Optional spatial constraints or zones
Outputs
  • Structured dwell events
  • Time-based deviation summaries
  • Readiness-aligned dwell flags
  • JSON outputs for downstream systems, with optional PDF mini-after-action extract
Execution & Control
Deployment Models
  • Standalone Python module
  • Containerized microservice
  • API-ready integration
  • Air-gapped compatible
Configuration
  • Dwell thresholds defined in configuration files
  • Operator-adjustable timing parameters
  • Core algorithms remain fixed, deterministic, and auditable across deployments.
Security
Deployment models and licensing are tailored by environment and mission constraints.
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