March 2026 Architecture Advances

What it does

BrainCode™ is a Tabular Autoencoder trained specifically on each customer’s dataset. It compresses tabular data 60–80%, removes noise, and mathematically seals the dataset so that predictive signal is preserved while storage footprint shrinks drastically. Customers running BrainPredict on-premises eliminate the need for large local NAS or SAN arrays.

How it works

The encoder (PyTorch, 3-layer MLP) maps raw tabular rows to a compact latent space. The decoder reconstructs them for model training. The fidelity check (≥ 98% roundtrip accuracy) runs automatically in the Installation Wizard Shadow Mode step before go-live.

API usage

# Compress a dataset
POST /api/v1/braincode/compress
{ "tenant_id": "acme", "dataset_id": "sales_2024", "data": [...] }

# Returns
{ "compressed_id": "bc_...", "original_rows": 50000,
  "compression_ratio": 0.28, "fidelity_score": 0.991 }

Compliance

BrainCode compression events are logged to the EU AI Act Art.12 audit trail with cryptographic signature (SHA-3 + Dilithium-3). Complies with GDPR Art.25 (data minimisation by design).

What it does

All BrainPredict models (scikit-learn, XGBoost, LightGBM, PyTorch) are exported to ONNX format at activation time and registered in a session pool. ONNX Runtime uses kernel fusion, quantisation, and graph optimisation to deliver 3–5× faster inference on CPU and reduce RAM usage by 4× vs. the Python-native stack.

Hardware impact

Minimum server spec for a full BrainPredict deployment drops to 8 GB RAM (from 32 GB). This directly reduces infrastructure cost for on-premises customers — no GPU required for standard deployments.

API usage

# Benchmark ONNX session for a model
POST /api/v1/performance/onnx/benchmark
{ "model_id": "demand_forecast_v3", "sample_rows": 1000 }

# Returns
{ "latency_ms_onnx": 4.2, "latency_ms_native": 18.7,
  "speedup_factor": 4.45, "ram_mb_onnx": 120, "ram_mb_native": 480 }

What it does

Conformal Prediction wraps every BrainPredict model prediction with a mathematically guaranteed confidence interval. The coverage guarantee (default α=0.10 → 90% coverage) holds regardless of the data distribution — no Gaussian assumptions. This is the only statistically valid approach to uncertainty quantification for enterprise AI.

EU AI Act compliance

EU AI Act Art.13 requires High-Risk AI systems to communicate uncertainty to users. Conformal Prediction is the only approach that provides a distribution-free, post-hoc guarantee. BrainPredict ships this natively — no configuration required. The calibration set is automatically held out (20%) during Shadow Mode validation.

API usage

# Get a prediction with guaranteed interval
POST /api/v1/conformal/predict
{ "model_id": "revenue_forecast", "features": {...}, "alpha": 0.10 }

# Returns
{ "prediction": 2450000, "interval": [2180000, 2720000],
  "coverage_guarantee": 0.90, "method": "RAPS" }

What it does

Causal AI v2 identifies what actually drives business outcomes — not just correlation. Three estimators cover the full causal inference toolkit: Granger causality for time-series leading indicators, Propensity Score Matching (PSM) for observational study bias removal, and Difference-in-Differences (DiD) for policy/campaign impact measurement.

Business value

Customers learn which levers to pull: which marketing channel actually causes revenue (not just correlates), which supplier action causes defects, which workforce policy causes attrition. This transforms BrainPredict from a prediction engine into a decision engine.

API usage

# Granger causality test
POST /api/v1/causal/granger
{ "cause_series": [102, 98, 115, ...], "effect_series": [88, 92, 107, ...],
  "max_lag": 4, "significance_level": 0.05 }

# Returns
{ "is_causal": true, "p_value": 0.018, "optimal_lag": 2,
  "interpretation": "marketing_spend Granger-causes revenue with lag=2 weeks" }

What it does

All BrainPredict connectors now emit Apache Arrow RecordBatches instead of row-by-row Python dicts. Zero-copy memory transfers between ingestion and the AI model layer eliminate serialisation overhead. Throughput exceeds 100 K rows/second on standard hardware. An automated quality check runs at ingestion time: null rates, type consistency, and a 0–1 quality score.

API usage

# Load Arrow data and get quality report
POST /api/v1/ingestion/arrow/load
{ "connector_id": "sap_s4hana", "table": "sales_orders",
  "batch_size": 50000 }

# Returns
{ "job_id": "arr_...", "rows_loaded": 240000,
  "throughput_rows_per_sec": 118400, "quality_score": 0.97,
  "null_rate": 0.008, "type_consistency": 1.0 }

What it does

Compliance SaaS generates cryptographically signed audit reports for every AI decision and data processing event. Supports EU AI Act Art.12 (technical documentation), GDPR Art.30 (ROPA), NIS2 (incident registers), and ISO 42001 (AI management system). Reports are signed with SHA-3 + Dilithium-3 (post-quantum).

API usage

# Generate a compliance report
POST /api/v1/compliance/report
{ "framework": "eu_ai_act_art12", "tenant_id": "acme",
  "period_start": "2026-01-01", "period_end": "2026-03-31" }

# Returns
{ "report_id": "rpt_...", "framework": "eu_ai_act_art12",
  "signed": true, "signature_algo": "Dilithium-3",
  "download_url": "/api/v1/compliance/report/rpt_.../download" }

What it does

Benchmark Intelligence compares BrainPredict model accuracy against industry baselines and competing approaches in real time. Results include MAE, RMSE, MAPE, and hit-rate metrics per platform. Used to evidence accuracy claims for sales and to drive continuous improvement loops.

API usage

# Run a benchmark
POST /api/v1/benchmark/run
{ "platform": "supply", "model_ids": ["demand_forecast_v3"],
  "test_dataset_id": "holdout_2025_q4" }

# Returns
{ "benchmark_id": "bm_...", "status": "running",
  "estimated_duration_sec": 45 }

What it does

Every AI decision — who asked, which model answered, what Truth Score, what human approved — is logged and can be exported as a cryptographically signed PDF or JSON. Dilithium-3 signatures are post-quantum resistant. Required for EU AI Act Art.12 High-Risk AI systems and accepted by EU regulatory bodies.

API usage

# Export a signed audit trail
POST /api/v1/audit/export
{ "tenant_id": "acme", "from": "2026-01-01", "to": "2026-03-31",
  "format": "json", "include_signature": true }

# Returns
{ "export_id": "exp_...", "records": 4821,
  "signature": "MHCM...", "algo": "Dilithium-3",
  "download_url": "/api/v1/audit/export/exp_.../download" }