Enterprise machine learning, without the enterprise.

The data was never the problem

Retailers have forecast demand with machine learning for thirty years. Banks have scored risk with it for longer. It works, it is well understood, and it quietly compounds for whoever can afford to run it.

What it costs is not data. A ten-person distributor already has the order history a demand model needs. A services firm already has the invoice history a collections model needs. What they lack is the two data scientists, the training pipeline and the year of platform work that used to sit between that data and a usable prediction.

That is the part we built. Bring an export from the system you already use, pick the outcome you want predicted, and get a model trained on your own rows, with a report that shows exactly what it gets right and wrong. What you do with the answer is still your call.

The team

EasyDeploy AI is built by engineers who spent their careers building this machinery inside large companies. They build it for small businesses now, so a team of twelve can forecast as well as a team of twelve hundred.

Emanuel Castañeda

Founder & Chief Executive Officer

Studied

Princeton University

Computer Science

Previously

Audible

Emanuel started EasyDeploy AI after a decade of building the kind of prediction infrastructure only large companies could afford. At Audible he worked on the machine learning behind the recommendations that decide what millions of listeners hear next. Before that he wrote foreign-exchange trading software at BlackRock. In both places the pattern was the same: a team of specialists, a platform built from scratch, and a budget that put the whole capability out of reach of anyone smaller.

He built EasyDeploy AI to close that gap. Small and mid-sized businesses hold the same data the Fortune 500 forecasts on: invoices, deals, orders, subscriptions. What they do not hold is the machinery to use it. EasyDeploy turns that data into a trained model in minutes and ships the performance report with it, so the person making the decision can see what the model gets right before relying on it.

LinkedInPersonal site

Talks, features and open source

Conference stages, engineering features, and the code we publish.

Small Business Expo, Javits Center NYC

Speaker: turning business data into predictions, with no technical team

Hands-on workshop for small business owners on lead scoring and demand forecasting.

Read it

Founder featured

Audible · 2023

Life Inside Audible: Machine Learning & Data Science

Audible's engineering feature on the recommendation systems behind what listeners hear next, filmed while Emanuel was a machine learning engineer there.

Read it

EasyDeploy AI

EasyDeploy AI on YouTube

Recorded walkthroughs, including the full Small Business Expo workshop end to end.

Take a look

EasyDeploy AI on GitHub

easydeploy-ai-mcp

Open-source MCP server exposing the platform as tools for Claude and other agent hosts.

Take a look

See it on your own data

Bring one export from the system you already trust. We will show you what a model trained on it can tell you.