How a GTM brain runs your company’s GTM

Start with a job your team repeats

A GTM brain is a modular operating system for a company’s go-to-market. It connects company context to skills, lane plugins, commands, scripts, references, connectors and memory. An agent can use that system to prepare an account brief, check a lead’s owner or reconcile a pipeline report without reconstructing the business from a new prompt every time.

The first useful question is specific: which recurring decision is slow, inconsistent or dependent on one person’s memory? Choose one. A missed inbound handoff is a better starting point than “automate our GTM”. You need a visible output, an accountable owner and a way to check whether the work improved.

GTMhub runs on its own GTM brain. That supports our context, research and production. It does not make every workflow in this library a delivered client system; those designs carry their own evidence labels.

What belongs in the brain

Keep facts, hypotheses and rules distinct. “This signal might predict intent” is a hypothesis until campaign results support it. “Never contact suppressed accounts” is a rule. “This account has an open opportunity” is a CRM fact. Treating all three as interchangeable creates confident mistakes.

One company, one brain

Build the company brain once. Each service adds a plugin to it. Paid media needs conversion definitions, deal values and exclusions. Outbound needs account fit, buying groups, signals and suppression. AI search needs positioning, proof and buyer questions. They share the company’s context and measurement.

GTM engineering adds company-wide depth: scoring, routing, TAM and scheduled jobs written into the CRM. A company can start with a single lane. It does not need to buy every service to make the brain useful, and work in different lanes can start in parallel.

A scoped job, with an inspectable output

Take inbound routing. The brain supplies the ownership rules, territories, exclusions and response commitment. The connector reads the existing account and deal. A rule checks for an owner before enrichment. The output is an assigned lead and an account brief, or a named exception for someone to resolve.

> /lead-routing demo-requests
read ownership rules and active territories
check customers, open deals and suppression
match the account and fill required gaps
prepare assignment and buyer context
review exceptions with the revenue owner

The command starts a configured job. The system still needs field mappings, access, exception handling and people responsible for changes. A good demonstration shows the output and a failure case, not just a prompt returning fluent prose.

Build the smallest useful version

1. Agree the decision and its owner. Name the output that would make the next action easier.
2. Confirm company context: fit, exclusions, proof, vocabulary and the pipeline definition.
3. Define one skill and its lane plugin. Include the stop conditions and review point.
4. Connect only the data needed for that job. Start with read access where it is enough.
5. Replay known examples, including a duplicate, a missing field and a conflicting owner.
6. Run it with the team. Record what they corrected and change the skill or context accordingly.

Do not add a connector to compensate for an unresolved decision. If sales and marketing disagree on “qualified”, an automation will make the disagreement move faster.

What makes it compound

After each run, keep the result, the exception and the reason a person changed the output. Review patterns weekly. Repeated ownership exceptions mean the routing rules need attention. Rewritten first touches may reveal that the positioning or proof is weak. A rejected account may improve the ICP exclusion list.

Measure the job’s outcome: leads with a valid owner, briefs accepted without substantive rework, reconciled reports, qualified replies or a shorter handoff. Track failures too. A high volume of outputs is useful only when the team can act on them.

Ownership and access

The brain belongs to the company. Accounts and data stay in the client’s name. Permissions should match each job, and access should be removable. Sensitive client material is kept apart from public examples. A publishing workflow should read only content explicitly prepared for publication.

Our own Hub follows that boundary: the website build reads the public resource folder, not the private company brain. The pipeline definitions template gives you a practical starting point for the shared measurement layer.

Use the open starter

The GTMhub brain starter on GitHub gives you original company-context templates, three skills and a runnable local account-decision example. It is free to read and adapt under the MIT license. Its illustrative scoring weights need your company’s approved model; no client material or credentials are included.