🧠 Recursive Predictive Logic Engine (RPLE) v1.3

“It doesn’t guess. It learns — and then it loops.”
A deterministic, self-refining insight engine that transforms raw operational data into structured, actionable intelligence.
No cloud dependencies. No black-box AI. Just Python, Flask, and clarity.

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🚀 Overview

The RPLE ingests structured metadata (CSV), identifies emerging patterns, filters noise, and outputs continuously improving predictions and insights.
Each cycle compounds analytical clarity — meaning the more you use it, the smarter it gets.

Built for transparency, not mystery: every decision is logged, scored, and explainable.

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⚙️ Core Capabilities

• 📊 CSV Ingestion & Normalization — feed any structured operational dataset
• 🔁 Recursive Insight Loop — learns from every cycle, compounding precision
• 🧩 Constructive Feedback Engine — outputs clear insights, not just numbers
• 🧠 Memory Reservoir — tracks persisting vs. novel patterns over time
• 🪶 Lightweight Deployment — pure Python + Flask, no external APIs or services

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🧩 Architecture

graph TD
A[CSV Upload] --> B[Data Normalization]
B --> C[Pattern Detection & Scoring]
C --> D[Insight Generation]
D --> E[Human Feedback + Memory Reservoir]
E --> C

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💻 How It Works

1. Upload a CSV file (any dataset with value_1, value_2, and optional risk columns).

2. The engine analyzes:

   • Trends
   • Correlation shifts
   • Anomalies
   • Risk alignments

3. It generates insight cards with:

   • Confidence
   • Novelty
   • Severity
   • Suggested actions
   • Status (🆕 new / ♻️ persisting)

4. The engine stores each insight in its memory reservoir for future comparison.

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🔍 Example Insights

Domain	Insight	Confidence	Novelty	Status
Primary	Primary metric trending up	0.86	0.82	🆕
Correlation	Relationship between value_1 and value_2 strengthened	0.72	0.75	♻️
Anomaly	Anomaly burst detected (3 spikes)	0.91	0.88	🆕
Risk	Risk and primary metric are aligned (corr=0.52)	0.67	0.64	🆕

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🧮 Installation & Run

git clone https://github.com/<your-handle>/recursive-logic-engine.git
cd recursive-logic-engine
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python app.py

Then open your browser at http://127.0.0.1:5000 and upload your dataset.

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📈 Example Dataset

sample_input.csv

timestamp,value_1,value_2,risk
2025-10-01,10,12,0.20
2025-10-02,11,12,0.21
2025-10-03,13,13,0.22
2025-10-04,15,14,0.25
2025-10-05,18,14,0.30
2025-10-06,16,13,0.28
2025-10-07,19,15,0.33
2025-10-08,21,16,0.36
2025-10-09,24,17,0.40

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🧠 Memory & Learning

Each loop stores a hashed summary of every insight in insight_memory.json. Future runs detect whether insights are:

• 🆕 New: unseen patterns
• ♻️ Persisting: confirmed patterns continuing across cycles

This creates a real-time feedback model that grows smarter with use.

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🧱 Tech Stack

• Python 3.9+
• Flask 3.x
• Pandas + NumPy
• JSON + Local Storage (no external API)
• SHA-256 integrity for insight memory

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👨‍💻 Author

Joseph Wells 📍 Indianapolis, IN 📧 joepwells95@gmail.com 🔗 Foxhunter Labs

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🧩 Related Systems

• 🦊 Foxhunter Pro — Human-Gated Reconnaissance & Ethical Autonomy System
• 🧬 Enigma² — Safety & Kill-Switch Engine
• 🛰️ Swarm — Deterministic Multi-Agent Coordination Framework

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⚖️ License

MIT License © 2025 Joseph Wells Use freely for educational and research purposes. Attribution required.

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🧭 Tagline

“Predictive clarity doesn’t just happen — it compounds.”