Confidence when it matters.

Patent-pending architecture that improves recommendation accuracy by 30% while correctly handling uncertainty. Binary confidence gating activates advanced features only when confident.

+77.4%Hit Rate improvement

p < 0.001Statistical significance

OOD-safeOut-of-distribution detection

✓ Validated on MovieLens • ✓ RecSys 2026 paper in preparation • ✓ API access available now

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Validated results

+77.4%

77% Better Accuracy

Validated +77.4% improvement in Hit Rate@10 on normal sequences (p < 0.001) on MovieLens recommendation data.

Hard gate

Confidence Gating

Binary threshold mechanism—activates advanced features only when confident, falls back to baseline when uncertain.

OOD-safe

Out-of-Distribution Detection

Correctly refuses activation on non-recommendation data (LIGO, financial markets)—maintains epistemic humility.

How it works

Lattice combines temporal sequence modeling (LSTM) with behavioral archetype priors. Unlike soft weighting or always-on fusion, it uses binary confidence gating—a hard threshold that completely disables archetype-based scoring when confidence falls below a threshold. This prevents false activation and enables out-of-distribution detection.

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Use cases

Recommendation systems (e-commerce, content, media, marketplaces)
Next-item and next-click prediction (personalization, feeds)
Trading and market signals (crypto, equities, prediction markets)
Financial services (risk management, fraud detection)
Scientific and medical applications (time-series analysis, anomaly detection, research)
Sequential prediction (demand forecasting, user journeys)

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