01 · Framing & design
Clarify decisions, constraints, and success metrics. Turn business questions into modelable problems everyone can see.
- • Problem decomposition & feasibility
- • Data + system discovery
- • Model & experiment design
Two PhDs, one applied research studio.
FewshotAI Consulting is a small, senior-only practice at the intersection of machine learning, data science, and operations research. We help teams turn messy, high‑dimensional reality into tractable models and decision systems that can be trusted in production.
Core lenses
ML + OR + experimentation as one toolbox.
How we work
Few high‑leverage projects, deeply embedded with your team.
You get
Research‑grade thinking, production‑minded execution.
How we engage
We usually plug in where the problem is too open‑ended for a vendor and too cross‑disciplinary for a single internal team.
02 · Modeling & iteration
Build candidate models and policies, stress‑test them and iterate with your domain experts.
- • ML & OR modeling
- • Simulation & what‑if analysis
- • Offline & pilot evaluation
03 · Landing in production
Partner with engineering and operations to launch, monitor, and iterate safely.
- • Handoff‑ready artifacts
- • Guardrails & observability
- • Playbooks for future teams
Problem silhouettes
Capacity & routing
"We have people, vehicles, or machines and a backlog of work. How do we allocate and route to hit SLAs without burning cash?"
Pricing & policy
"We control levers like price, limits, or incentives. How do we design policies that manage risk while respecting demand?"
Signals & targeting
"We observe lots of weak signals about users or assets. Which ones truly matter, and how should we act on them?"
Inventory & fulfillment
"We balance stockouts against holding costs across a network of locations. How do we set policies that adapt to uncertainty?"
Risk & anomaly detection
"We need to surface rare but costly events early. How do we combine statistical models and business rules in a robust way?"
Experimentation at scale
"We ship changes constantly. How do we design experiments and metrics that tell us what's actually working?"
Capacity & routing
"We have people, vehicles, or machines and a backlog of work. How do we allocate and route to hit SLAs without burning cash?"
Pricing & policy
"We control levers like price, limits, or incentives. How do we design policies that manage risk while respecting demand?"
Signals & targeting
"We observe lots of weak signals about users or assets. Which ones truly matter, and how should we act on them?"
Inventory & fulfillment
"We balance stockouts against holding costs across a network of locations. How do we set policies that adapt to uncertainty?"
Risk & anomaly detection
"We need to surface rare but costly events early. How do we combine statistical models and business rules in a robust way?"
Experimentation at scale
"We ship changes constantly. How do we design experiments and metrics that tell us what's actually working?"
Have a problem that sounds like this?
Send a short note about your context, constraints, and what "better" would look like. We'll reply with a concrete point of view on whether and how we can help.