Data and AI solutions for complex decisions.
Gateway brings together quantitative modeling, data science, machine learning engineering, and cloud data architecture to solve problems that are too important for generic software.
Service Areas
Quantitative Analytics
Forecasting, simulation, optimization, pricing, risk modeling, and decision frameworks for high-stakes business questions.
- • Time series forecasting and scenario analysis
- • Risk, sensitivity, and stress testing models
- • Optimization for pricing, allocation, and operations
Data Science Consulting
Applied analytics that connect business hypotheses, messy data, and statistical evidence.
- • Experimentation, causal inference, and measurement
- • Segmentation, propensity, churn, and lifetime value
- • Executive dashboards and decision intelligence
Machine Learning Engineering
Production-ready ML systems, from model design to deployment, monitoring, and iteration.
- • Predictive models, NLP, computer vision, and ranking
- • MLOps, model registries, monitoring, and drift checks
- • APIs, batch inference, and real-time inference services
Data Engineering & Platforms
Data infrastructure that makes analytics and AI reliable, governed, and repeatable.
- • ETL/ELT, lakehouse, warehouse, and semantic layers
- • Streaming, batch pipelines, APIs, and data quality checks
- • Cloud architecture across AWS, Azure, and modern data stacks
AI Workflow Automation
LLM and automation systems that reduce manual work while keeping human judgment in the loop.
- • RAG, document intelligence, and knowledge assistants
- • Agentic workflows for research, operations, and reporting
- • Internal tools that connect models to business processes
Governance & Enablement
Practical guardrails so AI and analytics systems can be trusted, adopted, and maintained.
- • Model documentation and evaluation frameworks
- • Data governance, privacy, and responsible AI practices
- • Team training, handoff, and operating playbooks
Problems We Solve
Forecast and Optimize
Demand, revenue, risk, inventory, staffing, pricing, and operational planning when historical data alone is not enough.
Detect and Prioritize
Fraud, anomalies, churn, customer intent, document signals, process bottlenecks, and cases that need human attention first.
Automate and Scale
Repetitive analysis, reporting, research, data preparation, document review, and internal workflows that slow teams down.
How We Deliver
Diagnose
Clarify the business objective, users, decision points, data assets, constraints, and success metrics.
Design
Choose the right analytical, engineering, and AI approach with a roadmap that fits the operating environment.
Build
Develop the model, data pipeline, application, dashboard, or automation workflow with production standards.
Operationalize
Deploy, monitor, document, train users, and leave teams with systems they can keep improving.
Bring us your hardest data problem.
We can help scope the opportunity, test feasibility, and build the path from analytics idea to working AI system.
