An honest read on where ML fits, before you spend on it.
Most machine learning consulting companies show up already selling a model. We show up to answer one question first: does this problem actually need one — and if so, what's the smallest version that proves it?
A technical audit, not a pitch.
Two to four weeks, depending on how much is already instrumented. You get a written assessment either way.
- Audit your existing data — volume, quality, labeling, and whether it's actually sufficient for the model you have in mind.
- Map the highest-impact opportunities for ML or AI adoption across your operations, ranked by effort vs. payoff, not by what's trendy.
- A build/buy/skip recommendation — sometimes the right answer is a $20/month API call, and we'll say so.
- If it's worth building, a scoped plan: model approach, data pipeline, infrastructure, and a realistic timeline.
Consulting doesn't make sense for every stage.
If you already know exactly what you need to build and just need engineering hands, skip the audit and go straight to custom AI development. And if the honest answer is "your data isn't ready yet," we'll tell you that in the first call — not after a signed statement of work. Our AI Readiness Checklist is a free way to get a rough read on that yourself first.
See the engineering, not just the strategy.
PhotoNester: how we scoped, built, and shipped an on-device ML app — CLIP embeddings, Core ML, unsupervised clustering, and the App Store rejection that came with it.
Read the case study →Get a technical opinion before a proposal.
Tell us what you're considering. We'll tell you honestly whether ML consulting is even the right first step.