A model built around your data, not the other way around.
A generic API call is the right answer for most AI features. Custom development is for the rest: when your data, your constraints, or your accuracy bar don't fit what an off-the-shelf model was trained for.
Task-specific models, shipped in a real product.
Not a notebook that proves a concept — a model in production.
- Task-specific models for churn prediction, demand forecasting, classification, or clustering — trained on your domain, evaluated against your actual failure modes.
- On-device and privacy-constrained inference where it matters — Core ML on iOS, quantized models, or anything that can 't leave a server you don't control.
- The data pipeline underneath it: ingestion, labeling, feature engineering, and versioning — the part that determines whether the model still works in six months.
- Integration into the actual product — API, dashboard, or mobile app — so the model ships with something people use, not a standalone script.
Specific tools, chosen for the problem.
PyTorch and scikit-learn for training, Core ML when a model needs to run on-device, and cloud infrastructure sized to actual load — not a GPU cluster for a problem that fits on a laptop. We'd rather ship a k-means clustering model that works than a transformer that impresses in a demo and times out in production.
For pure feasibility work — figuring out whether a model can hit the accuracy you need before committing engineering time — see machine learning consulting. Once a model's in production, keeping it that way is MLOps.
One recent build, in detail.
PhotoNester sorts a million photos on-device using CLIP embeddings converted to Core ML and unsupervised k-means clustering — no server, no uploads. Here's how it was built, including the part that got it rejected from the App Store once.
Read the case study →Tell us what you're trying to predict, classify, or automate.
We'll tell you honestly whether a custom model is the right tool for it — and scope it if it is.