AI Audience Modeling

We put the models where the decisions happen: in dashboards, in your systems, and in tools your teams actually open.

The problem

A model that lives in a notebook changes nothing. Value appears only when a prediction reaches the person or the system that acts on it, in a form they trust.

What we do

Build custom models for your specific question, rather than forcing a generic tool onto your data
Deploy them into production: APIs, scheduled jobs, or direct writes into your CRM
Build KPI dashboards that show what the models are doing and what changed
Build internal AI tools and assistants that answer questions over your own data
Monitor performance, drift and data quality, with alerts when something breaks
Retrain on a schedule and document every version

How it works

Start from the decision and the person who makes it
Prototype fast, validate against real outcomes
Deploy into the tools your teams already use
Monitor, retrain, improve

What you get

Production models with monitoring, dashboards for your teams, and documentation that lets your own engineers take over whenever you want.

Where AI comes in

Beyond predictive models, we use large language models where they earn their place: querying your data in plain language, summarizing customer interactions, classifying free text, and automating repetitive analysis.

b: AI Audience Modeling · First-Party Data Foundation

FAQ

Do we need our own data team?

No, but if you have one we work alongside it and hand everything over cleanly.

Which cloud do you build on?

Yours, if you have a preference. Otherwise we recommend based on what you already run. (to confirm: preferred stack)

Can models be explained to non-technical stakeholders?

Yes. Every model ships with the drivers behind its predictions, not just a score.

What happens if performance drops?

Monitoring flags it, and retraining is part of the engagement, not an extra project.