Adaptive rupture detection and epistemic diagnostics for dynamic systems.
Policy-calibrated intelligence that learns from volatility.
QSI is a decision-intelligence engine that detects ruptures in forecast vs. actual performance, quantifies preventable losses, and provides epistemic diagnostics such as drift, threshold breaches, stability scope, and policy sensitivity.
It is designed for board-level clarity and field-level adaptability, aligning with volatile domains ranging from supply chains to finance, cyber, and pharma.
Minimal, calibrated, and transparent — QSI surfaces actionable intelligence without black-box opacity.
- Rupture Detection — Tracks forecast vs. actual drift, thresholds, and breach events.
- Loss Quantification — Converts drifts into monetary loss using unit cost.
- Epistemic Diagnostics — Scope score, PSI, and breach ETA forecasting.
- Cognize Meta-Policy — Optional adaptive mode with exploration and policy promotion.
- Segment Graphs — Coupled dynamics across multiple SKUs or regions.
- Dynamic Configurability — Every knob is exposed for user calibration, no statics hard-coded.
- Custom Models — Plug in enterprise-specific threshold policies via registry.
QSI is domain-agnostic. Example applications include:
- Supply Chains — Prevent procurement losses by catching over/under-forecast drifts early.
- Finance — Stress-test trading strategies against volatility thresholds.
- Healthcare & Pharma — Detect demand misalignments in critical drug or equipment supply.
- Cybersecurity — Monitor deviations in expected traffic or anomaly baselines.
- Operations & Strategy — Track policy adherence, systemic drift, and rupture clusters.
- Try it live: Launch QSI on Streamlit
- Upload your data: CSV with columns →
Date, Forecast, Actual, Unit_Cost. - Explore outputs:
- Ruptures flagged with drift × cost loss quantification
- Policy-calibrated thresholds & diagnostics (Scope, PSI, ETA)
- Interactive drift vs threshold plots & volatility bands
- Dive deeper:
QSI outputs are calibration-dependent.
The toggles and parameters exist for a reason: to adapt the system to the volatility profile of your domain.
Misuse without domain calibration may lead to misleading results.