Faster. Lighter. More Accurate.
v12: 560 domain-specialized models · 71.4M data points/sec ingestion (Apache Arrow) · 10,000+ inferences/sec (ONNX) · OTTO L5 Swarm Byzantine consensus · <3s AIIC cyber cascade · 2,288+ IB event types across 20 platforms.
3–5× Faster Inference on CPU
BrainPredict's 560 AI models are exported to ONNX format and run via onnxruntime's optimised graph execution engine. Graph-level optimisations, operator fusion, and INT8/FP16 quantisation deliver 3–5× lower CPU latency with 4× less RAM — without sacrificing accuracy.
Guaranteed Confidence Intervals
BrainPredict wraps every model output with Split Conformal Prediction — a distribution-free calibration method that provides mathematically guaranteed confidence intervals without assuming Gaussian distributions.
EU AI Act Art. 13 requires explainability on high-risk AI systems. Conformal intervals satisfy this requirement directly — regulators can audit the coverage guarantee, not just a probability score.
12× Faster — 71.4M Data Points/Second
BrainPredict replaces Pandas with Apache Arrow's columnar memory format. 1M rows × 50 columns loads in 0.7 seconds — that is 71.4 million data points per second, 12× faster than Pandas, with 50% less memory and zero-copy slice semantics.
Parquet format (columnar, compressed) reaches 55.6M data points/second. Streaming chunked ingestion handles files of any size with constant memory footprint.
How BrainPredict’s 525 Models Actually Work
The 560 models are domain-specialized — each built for a specific enterprise prediction task (churn, supplier risk, OEE, cash flow, threat detection…). They are not general-purpose LLMs. They use industry-validated algorithms (XGBoost, LSTM, Prophet, TFT, GRU, CatBoost) with Fast Learning Engine that adapts them to each customer’s specific data patterns incrementally — no full retraining needed.
Domain priors built in. TabPFN cold-start model gives strong baseline with <50 customer samples. Already above random and above generic LLM wrappers.
Fast Learning Engine (River partial_fit) has processed thousands of customer-specific events via the IB Learning Bridge. <5ms overhead per IB event.
Full personalization to customer's unique data distribution. Matches or beats custom-built models that took 18 months and €5M+ to build — at zero marginal cost.