Mathematical Models & Algorithms
Critical operational decisions —how much to produce, at what price to sell, by what route to deliver, to which customer to lend— are frequently made with spreadsheets and expert judgment. Applied operations research and statistical learning document consistent improvements when these decisions are modeled formally: McKinsey reports reductions of 20% to 50% in forecast error, 30% fewer out-of-stocks, and 20% fewer markdowns in retailers that integrate pricing and inventory (McKinsey Retail Practice, 2023).
We build optimization, forecasting, simulation, and quantitative risk models, calibrated with the client's data and validated against historical performance.
Problems We Solve
Imprecise demand forecasting
Errors that translate into excess inventory or stockouts. A case documented by McKinsey at a personal care company achieved 13% higher forecast accuracy, 40% fewer supply shortages, and 35% lower inventory after deploying a machine learning model.
Suboptimal fleet routing
Orders per vehicle, visit sequence, time windows, capacity constraints. Academic literature (Soares et al., 2023; Adaptive Large Neighborhood Search) documents average improvements of 5% to 15% over implemented solutions, with compute horizons of minutes.
Reactive or cost-based pricing
Fixed prices that ignore elasticity, competition, and willingness to pay. McKinsey reports conversion rate increases of 5% to 15% and material margin improvements when dynamic pricing models based on observed elasticity are applied.
Production planning and shift scheduling without formal optimization
Line assignment, order sequencing, labor load balancing. Mixed integer programming and metaheuristics reduce idle time and overtime.
Inventory management with static rules
Fixed reorder points and safety stocks per SKU. Stochastic models with dynamic segmentation reduce inventory levels between 20% and 30%, according to aggregated evidence from McKinsey and SymphonyAI.
Rudimentary credit risk models
Scoring based on limited variables, without incorporating alternative signals. The U.S. Treasury recovered more than 4 billion dollars in fraud and improper payments in 2024 through machine learning models.
Investment and capacity decisions without quantified risk
Point estimates that ignore variance. Monte Carlo simulation delivers outcome distributions, confidence intervals, and sensitivity analysis on the variables that most affect the result.
Rule-based fraud detection
High false positive rates, novel fraud schemes that go undetected. Anomaly detection models combined with network analysis prioritize real alerts and reduce compliance team workload.
Why Hire Baker Agentics
Team trained in operations research, statistics, and machine learning. We combine academic rigor with industrial experience in fintech, automotive, and retail.
Models validated against historical performance before going to production. Backtesting, A/B testing, and out-of-sample error metrics.
Integration with the client's operating systems. Models are delivered as services consumable by ERP, WMS, TMS, e-commerce platforms, or internal applications.
Technical documentation that meets audit and regulatory requirements: variables, assumptions, performance metrics, degradation monitoring. Useful for internal audit, CNBV, CONDUSEF, OCC, FINRA and equivalent regulators.
Cost 40% to 60% lower than global firms for equivalent scope, with success criteria defined up front and fees tied to verifiable milestones.
Training for the client's team to maintain and evolve the models. We deliver code, documentation, tests, and a transfer program.