Operational decisions, turned into models that decide — not dashboards that suggest.
An independent analytics practice building optimization and decision-support systems for supply chain and operations, and the agentic layers that put them in the hands of the people running the work.
See it work
A running optimization model, 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.
Services
Three kinds of problem, and what each engagement 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.
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
All work →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?
Routing, network design, capacity planning, or an agentic layer over models already in place — the first conversation is about whether it is worth doing at all.
Start a conversation