Green KPIs. Red Reality.
NovaWireless is a synthetic call-center research environment built to model KPI drift, proxy gaming, and trust-signal decay under AI optimization — and to validate those risks with reproducible evidence and governance instrumentation.
Papers, methods, and reproducible artifacts are published on GitHub.
NovaWireless Call Center Lab
A Synthetic Operations Sandbox for Incentive & KPI Modeling
This Lab is a fully synthetic simulation of a wireless carrier contact center designed to model how incentives, customer friction, and KPI measurement interact over time. The Lab contains 82,000+ call records across 12 months, 250 representatives, and structured scenario types including clean resolutions, activation failures, fraud-adjacent contacts, metric-gaming patterns, and unresolvable cases.
Each interaction includes a proxy “resolved” flag and a 30-day audited outcome, enabling direct measurement of proxy–truth divergence and deferred action effects.
The Lab supports rep-level profiling, friction decile analysis, repeat-contact windows (0–30 and 31–60 days), integrity rule instrumentation, and transcript-based NLP signals. All data are synthetic and packaged with reproducible scripts and audit-ready outputs.
This environment functions as the experimental foundation for the NovaWireless KPI Drift Observatory and NovaFabric validation framework.
KPI Drift Observatory — Evidence of Proxy–Outcome Divergence in AI-Optimized Contact Centers
The KPI Drift Observatory documents structural divergence between proxy performance metrics and true customer outcomes in AI-optimized telecom operations. Using the NovaWireless synthetic environment, the observatory demonstrates how dashboard success can mask operational failure and motivates the System Integrity Index (SII) governance framework.
Evidence Highlights — KPI Drift Observatory
These figures summarize the core findings of the KPI Drift Observatory: proxy KPI performance can remain strong while durable customer outcomes collapse under friction and optimization pressure. All figures originate from the NovaWireless synthetic validation environment and support the System Integrity Index (SII) governance framework.
Integrity Gate — Systemic KPI Violations
Over half of interactions violate at least one integrity rule, confirming that KPI distortion is systemic rather than isolated noise.
Proxy Overfit Ratio (POR)
Proxy resolution metrics improve faster than durable outcomes, demonstrating KPI optimization toward measurement artifacts rather than customer reality.
Friction Decile Collapse
At higher customer friction levels, proxy resolution remains high while true resolution drops sharply, exposing KPI blindness to complexity.
Terminal Exit Rate (TER)
Nearly one-quarter of proxy-resolved interactions end in customer churn, revealing false resolution and KPI misclassification.
Governance & Architecture — KPI Drift Observatory
Issue Term Lift — Hidden Failure Language
Specific language clusters strongly correlate with churn even in interactions labeled resolved, revealing masked failure states.
System Integrity Index (SII)
The System Integrity Index aggregates drift, overfit, and validity decay into a governance veto condition that constrains AI optimization.
SII Hardening Architecture
Three-layer governance architecture linking predictive risk, real-time indicators, and calibrated thresholds to constrain KPI drift.
NovaFabric — Validation Check (Governance Evidence Chain)
Governance-grade validation pipeline for synthetic call-center KPI risk in AI-optimized operations.
Built for reproducibility: receipts, hashes, and run-stamped outputs.
Produces:
• receipt + SHA-256 audit trail
• run-stamped outputs
• “paper-proof” artifacts (decile lift + logistic regression odds ratios)
Skills: Python • audit trails (receipts + hashing) • integrity gates • reproducible pipelines • KPI governance • decile lift • logistic regression • artifact-driven reporting • documentation
Repo: https://github.com/bellatrix11176/NovaFabric-Validation-Check
Paper: Read (PDF)
NovaWireless Governance Pipeline (Synthetic)
Detects KPI drift by separating proxy “performance” metrics from durable outcome signals—quantifying when optimization success diverges from customer reality.
Focus: Metric governance • proxy–outcome divergence detection • DAR/DRL/DOV/POR/TER signal modeling • System Integrity Index (SII) • reproducible audit pipeline with evidence visualization
NovaWireless Transcript Analysis (Synthetic)
Linguistic diagnostics for the NovaWireless Lab using TF-IDF term lift to detect proxy–outcome divergence, band-aid credits, and repeat contact patterns.
This pipeline applies TF-IDF vectorization and lift analysis to identify scenario signatures, proxy–true resolution divergence, unauthorized credit patterns, repeat contact predictors, and frustration intensity markers across 82,440 synthetic transcripts.
Where the Governance Pipeline detects KPI drift through structured metadata, this system detects drift through the language of the call itself.
Focus: TF-IDF term lift modeling • proxy–outcome divergence detection • scenario classification • band-aid credit linguistic signatures • repeat contact predictors • frustration intensity modeling
Cedar & Flame Home Energy — Offer Recommendation System
A full data science workflow for a fictional home energy company, demonstrating how customer usage, efficiency indicators, and tenure can be translated into targeted, campaign-ready offers.
Built to mirror a real business analytics pipeline: data preparation, exploratory analysis, interpretable modeling, evaluation, and deployment-ready outputs.
Demonstrates
• Interpretable decision tree modeling
• Feature analysis and correlation diagnostics
• Campaign segmentation with confidence scoring
• RapidMiner → Python parity
• GitHub + Colab reproducibility
Credit Risk Predictive Analytics
Predicts credit risk for new applicants using logistic regression, producing interpretable risk classifications with probability-based confidence scores.
Skills: Python · pandas · logistic regression · model evaluation · risk analytics
Internet Usage Behavior Analysis
Cleans and prepares real-world internet usage survey data, addressing missing and inconsistent values and identifying statistically significant behavioral patterns using chi-square feature weighting.
Skills: Python · pandas · data cleaning · exploratory data analysis · chi-square feature selection
Insurance Decision Tree Analysis
Applies decision tree modeling to insurance policy data to identify key risk factors and produce clear, explainable decision rules that reflect underwriting logic.
Skills: Python · pandas · decision trees · feature interpretation · risk analysis
Additional notebooks & experiements on Github.
© 2026 PixelKraze, LLC
Copyright & Licensing
All original content, models, documentation, and frameworks on this site are the intellectual property of PixelKraze, LLC unless otherwise stated.
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Commercial use, redistribution for profit, or incorporation into proprietary systems requires prior written permission.
Independent work using synthetic or public data. Not affiliated with or endorsed by any employer.