We benchmark your operating decisions, then build production ML systems that improve them. Forecasting, optimization, anomaly detection, recommenders, pricing, and reinforcement-learning policies — engineered as systems, not notebooks.
Most ML pilots stall in PowerPoint. The Pentaaxis machine-learning practice is structured around the discipline of getting models out of notebooks and into industrial production — with proper monitoring, drift detection, retraining cadence, and clear handover to operations. Our advisors benchmark first to identify which decisions actually pay back; our engineers ship code.
We are not religious about a single approach. Gradient-boosted ensembles for tabular forecasting. Bayesian hierarchies where uncertainty matters. Time-series transformers when long-range dependencies dominate. Reinforcement learning where decisions compound. Causal models when intervention requires it. The technology is selected for the problem.
A working catalogue of the systems, models, and platforms our engineers ship within this practice — selected for industrial reliability, observability, and scale.
Hierarchical, probabilistic, multi-horizon forecasting for demand, capacity, energy, and pricing with reconciliation.
Multivariate anomaly detection for sensor streams, transactions, network telemetry, and quality data.
MIP, constraint solvers, and metaheuristics for scheduling, routing, blending, and resource allocation.
Industrial recommenders for spare parts, replacement materials, supplier alternatives, engineering reuse.
Dynamic pricing, price elasticity modelling, and offer optimization with bandit-based learning loops.
Reinforcement-learning agents and contextual bandits that learn optimal control under uncertainty.
Representative deployments across our industrial client base. Each grounded in production engineering — not concept slides.
Probabilistic forecasts that respect business hierarchy, incorporate exogenous signals (price, promotion, weather, macro), and reconcile across temporal and product dimensions.
Joint policies across DCs, regional warehouses, and points-of-sale that minimize working capital while protecting service levels under stochastic lead times.
RL agents for dispatch, load consolidation, and carrier allocation that adapt to traffic, weather, and order arrivals in real time.
Recommend price points for industrial-customer quotes informed by win-rate models, competitor signals, cost-to-serve, and strategic-account policies.
Site-level load forecasts at 15-minute granularity that drive demand-response participation and tariff optimization.
Graph neural networks that model multi-tier supplier exposure to financial, geopolitical, and operational risk with early-warning on disruption.
Closed-loop RL agents that optimize setpoints in batch and continuous processes, increasing yield while staying inside safety envelopes.
Constraint-aware scheduling balancing skill, certification, fatigue, fairness, and demand — outperforming legacy heuristics.
Tools, frameworks, and platforms our engineers use day-to-day in this practice.
Every Pentaaxis engagement starts with a structured benchmarking conversation — no obligation, a senior engineer in the room, and a calibrated view of where AI moves the needle for your organization.