Operational optimization consulting
We provide operational optimization consulting — turning the decisions your operation makes every day into models that make them well. Network optimization, supply–demand matching, vehicle routing, machine learning predictive modeling, scheduling, and capacity planning.
The output is a working model, validated against your own historical decisions and deployed into your own cloud — not a slide deck with a recommendation in it.
What we work on
Network optimization
Facility location, lane design, and flow allocation across plants, hubs, and regions — minimizing landed cost subject to real capacity and service constraints.
Supply–demand matching
Allocating constrained supply against competing demand, with the shadow prices that show what an extra unit of capacity is actually worth.
Vehicle routing
Fleet routing under capacity, time-window, and driver-hour constraints — CVRP and VRPTW formulations solved at operational scale.
Predictive modeling
Demand forecasting, time-series models, and classification feeding the optimization layer, with validation that holds up out of sample.
Scheduling & capacity planning
Production and workforce scheduling, plus the capacity questions that come before them — what to expand, when, and by how much.
Agentic decision layers
LLM agents over solvers so an operator can pose a scenario in plain language and get a deterministic answer back, explained.
See it work
A running optimization model you can change and re-solve — not a screenshot.
Talk to a solver
Describe a supply-chain disruption in plain English. An agent rewrites the optimization spec, CVXPY re-solves it, and the shadow prices say what to do next.
Engagement types
Three shapes of work, and what each one leaves behind.
Optimization under constraints
Network flow, supply–demand balancing, routing, and scheduling — formulated as LP/MIP models that respect the constraints the business actually operates under, and solved to provable optimality.
A validated model, the sensitivity analysis around it, and the code to re-run it.
Decision-support systems
Models wired into the pipelines and interfaces the decision-makers already use, with the validation and monitoring needed to keep them trustworthy after handover.
A deployed service your team can operate, not a notebook.
Agentic workflow automation
LLM agents layered over solvers and models so an operator can pose a scenario in plain language, get it solved deterministically, and see the tradeoffs explained.
An interface to your models that non-modelers can actually use.
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 an engagement runs
- 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.
Case studies
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
Have a decision problem worth modelling?
The first conversation is about whether it is worth doing at all.
Start a conversation