The Embedded Model: Why Advisory AI Fails

Two Models

There are two ways to bring AI into an organization.

The first is the advisory model. A consulting firm runs a 6-month discovery phase, produces a 200-page strategy document, and hands it to your internal team to implement. Average cost: $500K+. Average outcome: the document sits on a shelf.

The second is the embedded model. AI engineers join your team, attend your standups, work in your codebase, and ship production code. Weekly. From day one.

Why Embedded Wins

Advisory firms optimize for billing hours. Embedded teams optimize for shipped outcomes. The incentives are completely different.

When our engineers sit with your team, they see how work actually flows — not how a process document says it should flow. They catch the workarounds, the edge cases, the unofficial workflows that no discovery questionnaire would surface.

What Embedded Looks Like

• Daily async updates in your Slack or Teams. No waiting for a weekly status call.
• Weekly syncs with leadership to align priorities and demonstrate progress.
• Code lives in your repo. Not ours. You own every line from the moment it is written.
• PhD oversight. Our data science advisors review model quality and edge cases bi-weekly.

The Ownership Question

Every piece of IP we create belongs to you. No licensing fees. No proprietary platforms. No renegotiation in year two.

When the engagement ends, you have a production system, full documentation, and the knowledge transfer to maintain it. We build ourselves out of a job. That is the point.

The Results

Our clients see working AI in production every single week. Lease abstraction that used to take 4 hours happens in 12 minutes. IC memo drafting that took 2 days takes 2 hours. Client intake that consumed 45 minutes completes in 5.

These are not projections. These are measured outcomes from systems running in production today.