Building context graphs that preserve decision reasoning across every function in GTM and solves the biggest pain point....Alignment!
For every single client interaction, whether it's a Zoom call, email, Slack message, hallway conversation, or in-person meeting I capture inputs beyond what typical transcripts show, through a specific set of prompts (10 in our case).
The voice note pushes automatically to a GitHub repo via Wispr Flow.
When I'm back at my desk, the system pulls, classifies, and routes each piece of context to the right agents.
Sales signals update the pipeline.
Pricing decisions feed proposal templates.
Client observations adjust health scores.
Competitive mentions sharpen positioning.
One 60 second note.
Multiple GTM agents learning simultaneously.
But the part that changed everything is what happens when deals close.
Every outcome, won or lost, feeds back into the system with specifics.
Which gut reads predicted correctly.
Where I left money on the table.
Which types of prospects close in three weeks versus three months.
What discovery approach unlocked conversations versus what fell flat.
After multiple cycles, the system started surfacing patterns I hadn't consciously recognized such as:
"Your strong fit rating closes at 85%. Prospects who mention failed agency relationships close 2x faster with shorter commitments. You win 6 out of 8 deals when you lead with ICP analysis and price under a certain threshold."
That's what Foundation Capital's article "AI's Trillion-Dollar Opportunity: Context Graphs" (Jaya Gupta, Ashu Garg) and their part 2 mean by decisions as durable artifacts.
The reasoning doesn't vanish after the deal closes. It compounds into institutional memory that makes every future interaction sharper.
Most GTM teams store the final state of a deal.
We store the judgment that produced it. And the gap between those two approaches widens with every interaction.
If you're interested in building an integrated agentic GTM architecture and want the system to learn from itself, happy to walk through.
