Blaise F. Labriola — Co-Founder

Technical Architecture: Zoonova AI Financial Intelligence Zoonova.com utilizes a multi-layered machine learning stack designed for high-frequency financial data synthesis and predictive modeling. The system architecture is anchored by a Quad-Ensemble integrating Temporal Fusion Transformer (TFT), XGBoost, Random Forest, and CatBoost to execute multi-horizon price and alpha forecasting. Core Technical Specifications The Quad Ensemble Layer: This ensemble calculates Price and Alpha predictions by processing over 150 features per stock. Data inputs include Factor Analysis, Regression Analysis, Statistics, Metrics, Financials, Fundamentals, Volatility, Ratios, Macro data, and Sentiment. Specialized Modeling: * Vader: Utilized for high-precision sentiment quantification across 3,000 global news and social feeds. Birch: Employed for pattern recognition and clustering across technical indicator charts, volatility, and indexes. Alpha Probability: Specifically driven by XGBoost (XGB) and CatBoost to identify non-linear relationships within the high-dimensional feature space. Gemini 3 Flash Integration: The Gemini 3 Flash API functions as the reasoning and automation layer. It facilitates high-speed data extraction and real-time LLM reasoning to enhance the interpretive depth of the data pipeline. Pipeline Maintenance: All machine learning outputs are recalculated twice daily, both pre-market and post-close. The entire model ensemble undergoes full retraining at the end of each trading week to mitigate data drift and maintain structural stability. Inference, Synthesis, and Workflow The platform employs a hierarchical prompting structure where Prompt 16 unifies the outputs of 15 prior logic chains. This delivers a comprehensive, actionable Investment Analysis containing: Scenario Modeling: Base, Bull, and Bear scenarios with associated confidence bands. Risk Quantification: Explicitly identified risk drivers and dated catalysts. Monitoring: Probabilistic price ranges and real-time anomaly detection. The integration of these specialized models with the reasoning capabilities of Gemini 3 Flash provides an institutional-grade, explainable workflow for financial analysis across web and mobile platforms.

Stackforce AI infers this person is a Fintech expert specializing in AI-driven financial analytics and predictive modeling.

Location: Park City, Utah, United States

Experience: 11 yrs 4 mos