Intelligence at scale. Strategy in code.
36 Dunes is a machine learning and AI solutions studio that helps forward-thinking businesses unlock the power of their data — custom models, predictive analytics, and automation pipelines that turn information into strategic advantage. When the job calls for it, we build the software underneath it too: full-stack web apps, iOS, and the cloud infrastructure that keeps it all running.
Two disciplines, one senior team.
Most studios pick a lane: applied AI, or product engineering. 36 Dunes runs both, because the models rarely matter without a product built well enough to put them in front of someone.
Applied AI & Data
Task-specific machine learning models tailored to your domain — from churn prediction to demand forecasting — built to scale with your business.
Not sure where to start? We help you find the highest-impact opportunities for AI adoption across operations, marketing, and finance.
Automation pipelines built on intelligent systems that turn manual workflows into data-driven engines.
Dashboards that surface trends, flag risk, and support decisions at every level of the business.
Software & Product Engineering
Full-stack products built with React, Node.js, and Python on modern cloud infrastructure — from first prototype to production.
Native apps built with Swift, React Native, and Core ML, designed to run fast and stay private, on-device wherever possible.
Infrastructure audits and automation that cut hosting costs and remove deployment bottlenecks.
Legacy systems brought forward without a rewrite-from-scratch gamble — including healthcare and other compliance-sensitive platforms.
Proof, not a portfolio deck.
One recent build, detailed the way we'd hand it to another engineer — not dressed up for a pitch.
PhotoNester: sorting a million photos without a single upload.
A camera roll with years of unsorted photos is a search problem, not a storage problem. We built PhotoNester to solve it entirely on-device: CLIP embeddings converted to Core ML classify each photo's visual content, then k-means clustering groups them into albums — Travel, Pets, Food, Documents — without hardcoded labels and without a single image ever leaving the phone. Getting there meant batching inference to protect battery life, persisting progress mid-scan, and tracking down an App Store rejection to a Core ML compute-unit fallback on older iPadOS builds.
Read the full case study →- Platform
- iOS · Swift + SwiftUI
- Model
- OpenAI CLIP → Core ML
- Method
- Unsupervised k-means on embeddings
- Privacy
- 100% on-device, no server
- Result
- Full library sorted in minutes
How a two-person build stays fast.
No account layer between you and the person writing the code. That's most of it.
Scope the real problem
A short technical audit of what you have and what's actually blocking you — before any model gets chosen or any line of code gets written.
Ship in working slices
You see a running version early and often, not a roadmap slide. Direction changes get absorbed as we go, not billed as change orders.
Leave it maintainable
Documented, tested, and built on infrastructure your own team can operate — not a black box that only we can touch.
One senior engineer, not a bench of juniors.
36 Dunes is built around a full-stack engineer with over ten years of experience shipping web applications on Python, JavaScript, React, and AWS. That includes healthcare platforms connecting patients to clinical trials, predictive machine learning models built from scratch, and DevOps work that measurably cut infrastructure costs — the practical, unglamorous engineering that most AI pitches skip past.
Based on the coast in York, Maine, and working with a small number of clients at a time, by design.
Let's build something worth shipping.
Tell us what's slow, manual, or stuck in a spreadsheet. We'll tell you honestly whether AI is the right tool for it — and build it either way.