Context Architecture: How never solve the same GTM problem twice.

There is a right way and wrong way to use AI for GTM.

Wrong way: Starting over and over again every single time you create a new chat. Constantly back to ground zero.

How much time do you think this costs you and your team?

The other side of this approach is reckless abandon. Automate everything without deeply interrogating the why. Pump out generic content. Volume over quality.

This is why you’re seeing shamelessly AI-generated comments on everyone’s posts nowadays. This is slop.

And I am firmly in the anti-slop camp.

Right way: Operationalize your internal tribal knowledge + customer intelligence - call transcripts, research, positioning docs - into a context architecture in the form of a knowledge graph.

You create a living source of truth you and your agents/workflows can work against.

You never have to solve the same problem twice. Continuously benefiting from previous iterations.

This what I’m calling your CIA - a compounding intelligence asset (thanks Jordan for this one)

When you do it the right way, you can create high quality end deliverables 25x to 30x faster by eliminating context silos and working from your CIA.

You amplify your taste by solving for content augmentation instead of end to end slop generation.

You can actually pull out, deeply understand and synthesize insight across all of your hundreds of call transcripts that are painful to review - at scale.

In the video I show you an example implementation, walkthrough it works and the outcomes it creates.