Specialized AI integrated into your existing technology.

AI is an umbrella term: a set of tools that make computers behave intelligently, performing practical tasks that normally require human thought.

A spam filter and a self-driving car both sit under this one term.

Machine learning is the engine underneath the vast majority of modern AI. Within machine learning, some models are built for many tasks and some for a single one.

Click any region for a definition.

Machine learning works out its own rules from data. Nobody writes the logic down; the model infers it from examples and applies it to cases it has not seen.

Given ten years of quotes and which ones closed, a model learns what a winning quote looks like.

One large model, trained on an enormous amount of public text and images, ready for almost any question you can type. What to cook tonight, how to word an awkward email, what a piece of code is doing.

Its training covers the public internet, so its knowledge ends where your business begins.

A single-purpose model, trained on highly relevant data, built to help get key decisions right. It is also referred to as custom machine learning.

Its training covers your own records, so its knowledge starts with how your business actually works.

An agent is given a goal and left to work toward it over many steps, checking its own results and correcting course as it goes. You ask once and it keeps going until the job is done.

Working a warranty claim through to a decision, or booking a holiday end to end. Give one a bounded job and it will finish it.

Anthropic's general-purpose assistant. One model, trained on a broad sweep of public text and code, used for writing, analysis, and engineering work.

OpenAI's general-purpose assistant, and the product that made this category legible to everyone. Same shape: one large model, asked to do almost anything.

Google's general-purpose model family, wired into Search, Workspace, and Android. Broad by design.

Medical imaging. Their models read a scan, flag a suspected stroke, and page the specialist who can act on it. That is the whole product.

Google DeepMind trained AlphaFold on the Protein Data Bank to predict how a protein folds from its amino acid sequence. It solved a problem biology had worked on for fifty years.

Perception and prediction models trained on the driving record of their own fleet. The model reads a road and forecasts what everything on it does next.

Credit models trained on applicant and repayment history to estimate default risk, in place of a generic credit score.

We build models on your records and put the predictions in the tools your team already uses. Your CRM, your BI dashboards, your ERP.