What We Build — and How We Deliver It

The Problem

You know AI agents could help your business, but you don't know where to start — or you've had a bad experience with tools that overpromised.

What We Do

Over two weeks, we map your business operations, audit the data you have (and identify what's missing), and pinpoint the two or three places where an AI agent would deliver immediate, measurable value.

The Problem

You have a specific workflow — contract review, lead qualification, client reporting, customer support, inventory monitoring — that is eating your team's time.

What We Do

We design and build an AI agent grounded in your proprietary data. That means connecting to your actual systems — documents, databases, CRMs, spreadsheets — and building an agent that knows your business, not just general knowledge from the internet.

The Problem

Your data lives in multiple places — a CRM, spreadsheets, an old database, scanned documents — and it's not clean or connected. You want an AI agent but the foundation isn't there yet.

What We Do

We build the full stack. That means assessing, cleaning, and connecting your data sources, then building the agent layer on top. This is where our data engineering background matters most — we've spent decades doing exactly this at enterprise scale.

The Problem

You have an AI tool or agent that isn't performing the way you expected. It gives generic answers, makes things up, or fails on edge cases specific to your business.

What We Do

We diagnose the root cause — which is almost always a data problem, not an AI problem — and fix it. That might mean improving data quality, restructuring how the agent retrieves information, or redefining the agent's scope.