About the practice
Optimization & Decision Systems
ZenAlphaLab works on the operational problems that resist a spreadsheet: network and supply–demand optimization, vehicle routing, capacity planning, and the messy constraint sets that come with them. The output is a model that holds up in production, not a slide deck.
The practice increasingly focuses on integrating AI and agentic workflows into traditional data-informed decision-making — combining optimization, machine learning, and LLM agents to move past systems that generate recommendations toward systems that interpret context, coordinate tasks, and improve with use.
Engagements are delivered against the client's own cloud, data, and processes: Azure Data Factory, Databricks, SQL, and Python analytics, with feature engineering and model validation handled as part of the work rather than left as an exercise.
Who it fits
- —Operations and supply chain teams whose planning has outgrown spreadsheets but does not warrant an enterprise platform.
- —Data teams with models that produce good recommendations nobody acts on, because nothing connects them to the decision.
- —Organizations exploring what agentic AI can do beyond chat — where the answer has to be correct, not just plausible.
How engagements run
- 01
Scope
Establish what decision is being made, how it is made today, and what a better one is worth. Short, and it ends with a written problem statement rather than a proposal.
- 02
Model
Formulate and validate against historical decisions. The model has to beat the status quo on data you already have before anything gets built around it.
- 03
Deploy
Ship it into your cloud and your data, with the pipeline, tests, and monitoring that make it survivable.
- 04
Hand over
Documentation, the reasoning behind the formulation, and enough transfer that your team owns it. No lock-in to the consultant.
Toolkit
Optimization & OR
- Linear & mixed-integer programming
- Network and supply–demand optimization
- Vehicle routing (VRP / CVRP)
- CVXPY
- Google OR-Tools
- Sensitivity & duality analysis
Machine Learning & Statistics
- Probability & statistical modeling
- Time-series analysis
- Feature engineering
- Model validation
- Scikit-learn
- Pandas / NumPy
AI & Agentic Systems
- LLM-driven workflows
- Agentic task orchestration
- Natural-language → structured model translation
- Tool-using agents
- AI-assisted development pipelines
Data & Cloud Engineering
- Azure Data Factory
- Databricks
- SQL / PostgreSQL
- Google Cloud Run & Cloud Build
- Python
- TypeScript / Next.js
Practice led by Liang Wang.