{"id":31,"date":"2026-10-23T19:14:44","date_gmt":"2026-10-23T18:14:44","guid":{"rendered":"https:\/\/zunbitai.com\/inside-zunbit-ai-engine-explained\/"},"modified":"2026-10-23T21:10:38","modified_gmt":"2026-10-23T20:10:38","slug":"inside-zunbit-ai-engine-explained","status":"publish","type":"post","link":"https:\/\/zunbitai.com\/hr\/inside-zunbit-ai-engine-explained\/","title":{"rendered":"Inside the ZunBit AI Engine: The Models, Signals and Guardrails Explained in Plain English"},"content":{"rendered":"

You deserve to know what’s inside the box. Here is a plain-English, no-fluff breakdown of the ZunBit AI engine \u2014 the models, the data, the guardrails and the kill-switch.<\/p>\n

Step 1 \u2014 Raw data<\/h2>\n

We ingest tick-level price data for 1,400+ instruments across major exchanges from SOC-2 certified providers (Refinitiv, Polygon, ICE). We also pull macro feeds (central-bank calendars, CPI releases), news feeds (Bloomberg, Benzinga) and corporate-action feeds. Nothing scraped. Nothing bootlegged. All licensed.<\/p>\n

Step 2 \u2014 Feature engineering<\/h2>\n

From the raw data we derive ~240 engineered features per instrument: volatility clusters, order-book pressure, cross-asset correlations, regime indicators, news sentiment, options skew and more. Each feature has a documented lineage \u2014 our quants can reproduce it from scratch.<\/p>\n

Step 3 \u2014 The model ensemble<\/h2>\n

We run three models in parallel: a gradient-boosted base learner (fast, interpretable), a transformer-based sequence model (captures temporal patterns), and a Bayesian meta-learner that reconciles the two and outputs a signal, confidence score and attribution vector. Nothing proprietary we can’t explain \u2014 just well-engineered, well-tested machine learning.<\/p>\n

Step 4 \u2014 Guardrails<\/h2>\n

Every candidate signal passes three gates before publication:<\/p>\n