Stop doing marketing.
Start deploying your Growth OS.

GTM engineering means building go-to-market as technical infrastructure instead of running campaigns: data capture, enrichment, routing, agents and reporting assembled into one system a company owns and operates. Aiporate GTM builds that system: a signal ledger, a GitHub-governed content engine, executive command centers and AI agents, inside the client's own accounts.

An agency runs campaigns inside its own tools and keeps the operational knowledge. Aiporate GTM builds systems inside the client's accounts and Git repository, trains the client's team to operate them, and leaves the infrastructure behind. If the engagement ends, the data, the repo and the systems stay with the client.

B2B companies and agencies where marketing has become a process rather than a person: typically teams of about 20 employees and up. The relevant number is the total marketing budget (team, tools, agencies and ads together), not just ad spend, because that is what determines which system the company can actually run.

Consulting starts at EUR 4,000 and custom systems at EUR 8,000. The full Growth OS buildout is scoped by the number of connected data sources plus add-ons like team enablement and the ad engine. The estimator on gtm.aiporate.com produces an indicative figure in minutes, and a scoping call fixes the price. Engagements are milestone-based, and every milestone ships infrastructure the client keeps.

The full Growth OS is a 60-day, milestone-based engagement covering the signal ledger, the command center, the GitHub engine, the agent loop and team enablement. A single custom system, meaning one ledger, one dashboard or one agent workflow, typically takes two to six weeks.

Nothing switches off. The systems run in the client's own accounts and repository, the team has been trained to operate them, and the documentation ships as part of the build. There is no proprietary platform to keep paying for and no data to migrate out, because it was never anywhere else.

They do the operational grind under human approval: enriching accounts, drafting outbound into the CRM, building and syncing audience segments, catching creative fatigue, and shifting budget on real attribution data. Agents draft into branches and queues, never straight into a live channel, so a human approves before anything ships.

A signal ledger is a first-party system of record for buying intent. Events like pricing-page visits, product usage and email replies are captured server-side on infrastructure the client owns, following a versioned JSON schema with account resolution, consent snapshots and intent scores. Every downstream system, meaning agents, ads and dashboards, reads from that one contract instead of from separate third-party tools.

Because copy, ad variants, sequences and scoring rules are assets that change constantly and get lost in drives. In a repository every change has an author, a diff and a review, expensive mistakes get caught before spend, and merges deploy through CI/CD to the site, ad platforms and CRM. It makes marketing versioned and reversible, the way software already is.

No, and any buildout sold on that promise should be treated with suspicion. Agents remove the repetitive execution layer: list building, drafting, tagging, reconciliation. What is left is the work that actually needs a person, which is judgement about offer, positioning and where to spend next. Teams that run the system well get faster, not smaller.

No. Client data is used to operate the client's own systems and nothing else. It is not pooled across accounts and it is not used to train models.

Yes. The skills library at gtm.aiporate.com/skills publishes 76 of the working files behind this practice, covering copy, paid media, process, strategy and the context and infrastructure files everything else reads from. They are free to use, adapt and share, and they work whether or not there is ever an engagement.

The common go-to-market stack: HubSpot, Salesforce and Pipedrive on the CRM side, Meta, Google, LinkedIn and TikTok on the ad side, plus the analytics, enrichment and warehouse tools already in place. The system is built around what a client already pays for rather than requiring a migration.

The client, in every case. Systems are built inside the client's own cloud accounts, ad accounts, CRM and Git repository. Aiporate GTM operates them during the engagement and hands over the keys at the end, because infrastructure a vendor holds hostage is not infrastructure.

Consent state is captured on the event itself, in the ledger schema, so a withdrawal propagates to every downstream system rather than being honoured in one tool and missed in three others. Processing agreements, retention rules and access scope are agreed before any system goes live, and the buildout documents where each category of data is stored.

Only the named people on the engagement, with individual accounts at the least permission the work requires, never shared logins. Access is granted by the client, listed in the onboarding pack, and revoked at handover.

That is the normal starting point and usually the better one. The first phase is an audit of what is already there: what each tool captures, where that data goes, and what breaks if it is cancelled. Most buildouts add a ledger and a reporting layer on top of existing tools rather than replacing them.

On the client's own cloud infrastructure, in the region the client requires, with backups and access control set up as part of the build. Nothing routes through a shared Aiporate platform, because there is not one.