Specialized AI integrated into your existing technology

Custom models, built on your data, to make recurring decisions more profitably.

Veni, vidi, scrolli.

How do you get from data to better decisions?

AI is sprawling

The Landscape

What is AI?

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.

AI is a broad 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.

What is machine learning?

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.

What is general-purpose AI?

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.

What is specialized AI?

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.

What is agentic AI?

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.

Claude

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

Visit claude.ai ↗

ChatGPT

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.

Visit chatgpt.com ↗

Gemini

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

Visit gemini.google.com ↗

Viz.ai

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.

Visit viz.ai ↗

AlphaFold

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.

Visit alphafold ↗

Waymo

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.

Visit waymo.com ↗

Upstart

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

Visit upstart.com ↗

Parallel Attention

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

Get in touch ↗

Answer three questions and see what your own data could predict.

Already know what you want to predict? Scope your project.

Want to brainstorm further, or have a specific implementation in mind? Let's get to work.

We start with the data you already have. You get a straight answer on which of these your systems could support now, and what would have to change for the rest.

Scope your project

Or read how a custom, specialized AI engagement runs, from scoping through to monitoring.

Things you're probably wondering.

What's the difference between ML and traditional analytics?

With analytics you start with a hunch and check it against the data. A model works the other way round: you hand it the outcome you care about and it finds the combination of factors that predicts that outcome, including combinations nobody would have thought to test. Once it is trained it scores every record you have, every day, which is the part a person doing this by hand cannot keep up with.

What kind of data do we need?

It depends on what you want to predict. During scoping we go through what you already have: the systems it lives in, how clean it is, and where the holes are. Most companies are sitting on more than they realise. Usually the work is getting it into one usable shape, and only occasionally is it going out and collecting something new.

Why not just use ChatGPT or Claude?

ChatGPT and Claude are generalists. They handle an enormous range of things and they have never seen your business, so nobody can tell you in advance how well one will do on your particular problem. A model trained on your own history can be measured: we score it against records it has never seen before it goes anywhere near production, and you get that number. There is also the governance question of routing operational data through a third-party API, which a dedicated environment avoids.

How long until we see results?

You see a validated model, scored against data it was never shown during training, in the first few weeks. Getting from there to predictions arriving inside your systems depends mostly on how ready your data is, which is why scoping comes first. Development, which ends with the model connected to your systems, typically runs four to six weeks. The first operating cycle runs eight to twelve.

See all FAQs about data, cost, and ownership

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