Experience.

Models & technologies

ARIMA and exponential smoothing forecast demand for established SKUs. Random forests and gradient-boosted trees modeled nonlinear demand and operating signals; comparable-product and product-attribute features supported planning for new SKUs.

A constrained scheduler translated forecasts into equipment start/stop times and SKU changeovers. Separate capacity and cost scenarios supported location investment and workforce planning.

Data to decision

Inputs

SKU demand history, product attributes, equipment availability, changeover requirements, labor capacity, currency movements and inflation.

Model outputs

Demand forecasts, equipment-load estimates, feasible production schedules and location-level capacity and cost scenarios.

Decisions

Scheduled production and changeovers under equipment, labor and delivery constraints; compared staffing and capital-allocation options against expected demand and cost.

Evaluation

Forward-period forecast error, held-out product launches, schedule feasibility, throughput, delivery performance and changeover time.

Models & technologies

Poisson generalized linear models estimated billing-code volumes from geography, demographic cohort, population exposure and time. Time-series forecasting and cohort analysis tracked specialty utilization trends.

A separate capacity and economics analysis translated projected service volumes into departmental staffing and investment scenarios.

Data to decision

Inputs

Historical billing-code activity, service dates, patient geographies and demographic cohorts, aligned to information available at each forecast date.

Model outputs

Forecast code volumes and specialty utilization trajectories by geography and cohort.

Decisions

Compared staffing and departmental investment priorities using projected utilization, existing capacity and service-line economics.

Evaluation

Held-out-period forecast error by cohort, prediction-interval coverage, claims-reporting delays and sensitivity to coding or reimbursement changes.

Models & technologies

TF-IDF and word2vec supported semantic retrieval and query expansion. LambdaMART ranked job–resume matches using textual relevance, skills, geography and behavioral signals; percentage match scores expressed normalized relevance.

Separate role-and-region demand models and conversion-value models informed advertising opportunity estimates.

Data to decision

Inputs

Job descriptions, resumes, titles, skills, location constraints, search activity, advertising bids and conversion history.

Model outputs

Ranked matches, expanded search coverage, role-level demand estimates and estimated advertising value per posting.

Decisions

Broadened discovery across existing inventory, ranked relevant jobs and candidates, and informed advertising allocation by role and region.

Evaluation

nDCG@K, precision@K and recall@K against relevance judgments; coverage at a consistent relevance threshold; conversion-model calibration and forward-period performance.

Models & technologies

TF-IDF and linear models classified legal documents by subject and recurring language. GPT-2 representations fed a task-specific classification workflow for federal labor-law exemption labels.

Separate supervised models estimated case outcomes from pre-decision information and historical judicial features. Compliance-learning analysis used assessment-item performance and cohort comparisons to identify and test new training areas.

Data to decision

Inputs

Statutes, labeled exemption examples, employment facts, case metadata, prior judicial opinions and historical outcomes; assessment responses and cohort attributes for the separate learning analysis.

Model outputs

Legal-taxonomy labels, exemption classifications, case-outcome estimates and patterns in assessment performance.

Decisions

Supported legal review and litigation-funding analysis using information available before the target decision; used assessment patterns to prioritize new compliance-training topics.

Evaluation

Per-class precision and recall, temporal holdouts, false exemption classifications, outcome-model feature ablations and performance across assessment cohorts.

Models & technologies

XGBoost supported separate supervised objectives for mortgage credit events, homeowner-insurance premiums and home values. Credit-event outputs used probability calibration; premium and valuation models used regression.

Each product used its own features, labels and workflow destination. Property-information distribution connected valuation outputs to agent-facing tools.

Data to decision

Inputs

Underwriting-time borrower and loan data; property, coverage and premium data; historical closed transactions and property attributes for valuation.

Model outputs

Mortgage credit-risk probabilities, insurance premium estimates and property-value recommendations.

Decisions

Delivered risk scores to mortgage workflows, premium estimates to insurance workflows and valuation recommendations to real-estate agent tools.

Evaluation

Out-of-time and held-out-region performance, credit-probability calibration, premium and valuation error, and evaluation against mature credit outcomes.

Models & technologies

FTRL-trained logistic models estimated click probability from sparse advertising features. Separate conversion and value models estimated downstream returns and informed bidding and budget allocation.

Buyer-side policies controlled bids, pacing and campaign budgets. Exchange-side models evaluated auction timing and reserve-price floors for self-hosted inventory. Campaign analytics connected routing and copy performance to conversion.

Data to decision

Inputs

Impressions, clicks, conversions, placement context, creative variants, campaign spend and auction data, joined within defined attribution windows.

Model outputs

Click and conversion probabilities, expected value per opportunity, campaign-performance signals and exchange-floor scenarios.

Decisions

Allocated campaign budgets and bids; tuned exchange auction settings separately; used copy and conversion performance to improve marketing-automation routing.

Evaluation

Probability calibration, conversion rate, cost per acquisition and controlled ROAS comparisons, with delayed conversions and attribution windows accounted for.

Models & technologies

Generative models produced advertising content. Stateful tool orchestration connected creation, publishing and deployment, with a separate bidding policy governing spend decisions.

Execution used validated tool arguments, bounded retries, duplicate-action protection and reconciliation after timeouts. Trace evaluation compared model and prompt versions across complete workflows. Air-logistics evaluation tracked operational workflows separately.

Data to decision

Inputs

Campaign briefs, creative context, tool schemas, workflow state and spend constraints; model inputs, outputs, tool results and operational traces for evaluation.

Model outputs

Generated advertising assets, validated tool actions, execution records and task-level evaluation results.

Decisions

Published and deployed advertising through controlled workflows; adjusted bidding through a separate policy; used trace failures, quality, latency and cost to guide model optimization.

Evaluation

Task completion, invalid or duplicate actions, recovery behavior, latency and cost per completed task; operational outcomes evaluated separately from model-call success.

Models & technologies

Apache Flink processed timestamped events using stateful, event-time windows. Watermarks and late-event handling kept rolling features aligned to event time; online model inference scored those features for the reading-platform application.

Replay, duplicate handling and checkpoint recovery supported the feature pipeline. Model versions and feature freshness were tracked alongside inference latency.

Data to decision

Inputs

Timestamped platform events, consistent entity identifiers, replayable event history and versioned model inputs.

Model outputs

Entity-level rolling features and online predictions delivered to the consuming application.

Decisions

Fed current model outputs into downstream application workflows, with explicit handling for late events, stale features and recovered processing state.

Evaluation

Event lag, feature age, late and duplicate events, state recovery, inference latency and forward-time model performance.

Models & technologies

Threat classifiers identified known patterns, while anomaly-detection models scored deviations in security telemetry. Monte Carlo simulation estimated attack-and-defense risk under sampled assumptions.

White-hat penetration testing assessed observed defenses. Detection scores, simulation results and testing findings informed triage, remediation and patch-deployment workflows.

Data to decision

Inputs

Security telemetry, threat labels, authorized penetration-testing findings and attack/control scenario assumptions.

Model outputs

Threat and anomaly scores, prioritized findings and simulated risk distributions.

Decisions

Prioritized investigation and remediation; separated patch recommendations, deployment and post-deployment verification.

Evaluation

Detection precision and recall, false-positive volume, detection delay, observed penetration-test findings and remediation verification; simulation sensitivity evaluated separately.

Models & technologies

Workload forecasts and constrained optimization informed storage, compute and service deployment in AWS, Azure and GCP environments.

Separately, sensor-driven neural networks modeled facility consumption from IoT telemetry and operating conditions. Capacity and thermal constraints shaped facility operating recommendations and cost scenarios.

Data to decision

Inputs

Cloud utilization and workload histories; building-level energy, water and operating telemetry, associated emissions data and resource costs.

Model outputs

Resource-demand forecasts, cloud-capacity scenarios, facility-consumption predictions and operating-cost estimates.

Decisions

Planned resource deployment within service and capacity constraints; compared facility operating recommendations against thermal limits, consumption and site-level P&L.

Evaluation

Forecast error, capacity headroom, service performance, workload-adjusted energy and water consumption, and cost per workload and facility.

Models & technologies

NLP classification and information extraction converted job orders into validated fields. Demand and service-duration forecasting estimated workload by role, region and period.

A constrained routing optimizer assigned work using skills, shifts, travel and appointment requirements. A separate pricing model evaluated service effort, cost and customer-level commercial inputs.

Data to decision

Inputs

Work-order text, service history, job types, required skills, locations, worker availability, travel times and service costs.

Model outputs

Structured job requirements, demand and duration forecasts, feasible assignments and customer-level pricing estimates.

Decisions

Planned staffing and dispatch under skill, shift and service constraints; evaluated pricing against expected effort and commercial requirements.

Evaluation

Field-extraction accuracy, demand and duration error, schedule feasibility, on-time completion, travel time and margin per job.