We build and ship production ML for e-commerce, logistics, and fintech teams: pricing, forecasting, routing, fraud, and retention. We get measured against the metric we agreed on.
For online retailers and marketplaces where margin lives or dies on pricing, demand, and whether customers come back.
For operators moving physical goods, where a few percent of route cost or warehouse space is real money.
For lending, exchange, and trading teams where models decide risk, price, and trust in real time.
Every support conversation is data you are not using.
The teams behind Delta Labs have delivered production systems at Amazon, Mercedes-Benz, Crypto.com, Morpho, and BlueFin, some under NDA.
Experience of the founders, not a claim of client endorsement.
This is for you if:
Most engagements start in one of these shapes and adjust if the work calls for it. Timelines and budget signal are starting points - we quote against the actual problem, not a template.
Senior data science capacity embedded with your team. Reporting layer, basic forecasting, dashboard work, model prototyping. The gap most growing teams hit before they're ready for a full in-house DS function.
A scoped engagement to ship one specific thing. A model, a forecast, a dashboard suite, a churn or pricing analysis. Tied to a clear business metric on day one. No drift, no scope creep, no perpetual discovery phase.
When the answer is a system, not a model. Optimal pricing engines fed by scraped competitor data, ML classification and product matching, real-time response to market signals, custom client-side apps to operate it all. Built, deployed, monitored, supported.
Replace the manual decisions that shouldn't be manual. The system runs autonomously and only escalates to a human when confidence drops below threshold - eliminating the operational tax of low-leverage work while preserving human judgment where it matters.
Some specifics are under NDA. Where we can name numbers, we do - and where we can't, we describe the shape of the work honestly.
Five stages, regardless of size or complexity. Skipping any of them is, in our experience, how data science work quietly fails - usually because the modeling started before the problem was understood.
For a fixed-scope project, a usable v1 in 4–6 weeks is normal. Complex systems take longer - 3 months to a deployed system, more to wring out the operational edges. The intro call is where we tell you which one your problem actually is.
Fixed-scope projects start around $25k. Embedded engagements run $8k–$15k per week depending on time commitment. Complex systems are quoted against scope and usually land between $80k and $300k. We'll give you a range on the intro call before any proposal.
Routinely. Two of the case studies above are under NDA. We can sign yours before the intro call if your problem description requires it.
You do. Default contract: all work product, models, and code are assigned to the client on payment. We retain the right to describe the engagement at a high level - and even that we'll skip if you ask.
Python and SQL by default. We've shipped on AWS, GCP, and bare-metal corporate environments. For greenfield: Postgres + Python + AWS unless there's a reason not to. For your stack: whatever you've already got, we'll adapt.
Yes, after the first call - once we both think there's a fit. We don't burn reference calls on cold prospects.
Fixed-scope projects bill in two stages: a deposit and a delivery payment. Embedded engagements bill weekly with a two-week notice clause. We don't believe in lock-in - if we're not earning the next invoice, you shouldn't be paying it.
If your problem is ambiguous, complex, or sits somewhere between the data and the code - that's our zone. Send a paragraph; you'll get a real reply.