How agents get context

The failure mode of most GTM AI is that it reasons impressively over the wrong picture. A model with a clean prompt and no grounding will describe a deal confidently and get the stage, the owner, and the last touch wrong.

RevTech maintains a GTM data model as a first-class layer: your objects and relationships, the activity that moved them, and the rules that give them meaning. Agents read from that rather than from an ad-hoc query at the moment of asking.

• CRM objects and their relationships, kept current
• Activity history — what happened, when, and by whom
• Business rules and definitions specific to your organization
• Permission scope, so context respects who is allowed to see what

A qualified lead in one company is an unqualified lead in another. Agents that apply generic best practice produce work that has to be corrected, which is worse than no work at all.

RevTech captures your definitions during onboarding and keeps them versioned. When a definition changes, the change propagates to every agent that relies on it rather than being re-implemented in several places.

Because context is assembled rather than improvised, an agent can show its work. An answer about pipeline risk names the opportunities, the signals, and the records behind it.

That is what makes agent output reviewable in practice. A person approving work needs to check the reasoning, not re-derive the facts.