AtlasGTM + Google Cloud
MCP application · GTM orchestration

The go-to-market application layer for your customer's AI.

AtlasGTM is an MCP application. It plugs into the AI tool your customer's team already uses — Google Antigravity, Claude, Cursor or any MCP-compatible agent — and gives it a governed go-to-market layer. Ask for a customer page in plain language and it goes live on their brand, with a Gemini agent attached. Everything it builds runs in the customer's own Google Cloud tenant, on their data and their Gemini endpoints.

No new assistant to adopt, no Terraform, no engineer. It's transactable on Google Cloud Marketplace, so it draws down committed spend — and every page a seller or marketer publishes puts another agent into production.

Bring us into a deal → How it fits together
Connects into Google Antigravity Claude Cursor + Codex and any MCP client

About four minutes end to end. Forward it as is.

MCP application Plugs into Antigravity, Claude, Cursor and any MCP client
Marketplace Transactable on Google Cloud Marketplace — it retires commit
Gemini agents One ships with every surface, on Agent Platform, in their tenant
50+ connectors Governed access to the systems the GTM team already runs on
What it is

An MCP application, not another AI tool

AtlasGTM doesn't ask anyone to switch assistants. It connects into the one they already use and adds the go-to-market layer that assistant is missing — then runs everything it builds on Google Cloud.

1 · Where they work

Their AI tool of choice

Google AntigravityClaudeCursorCodexany MCP client

A seller or marketer asks for what they need in plain language, in the tool they already have open. Nothing new to install, learn or approve.

2 · The application layer
AtlasGTM

AtlasGTM orchestrates the GTM work

  • One governed MCP connection — 50+ connectors behind it
  • Role-based access, scoped credentials, approvals and logging
  • Brand, product data, forms, analytics and lead capture built in
  • Surfaces edited in place and versioned, not regenerated
3 · Where it runs
Google Cloud

Live on Google Cloud, in their tenant

  • Live surfaces on the customer's own domain
  • A Gemini Enterprise Agent Platform agent with every surface
  • On their data and their Gemini endpoints
  • Bought on Google Cloud Marketplace, against commit
Say this

The thirty-second version

If you only remember one thing on this page, remember this paragraph. Copy it straight into an email or a call note.

Copy-ready

AtlasGTM is an MCP application: a go-to-market orchestration layer that plugs into the AI tool your customer's team already uses — Google Antigravity, Claude, Cursor or any MCP-compatible agent. A seller describes what they want in plain language — a customer page, a partner page, an account plan — and Atlas builds it live, on their brand, on their domain, in minutes. Every one of those pages automatically stands up an agent on Gemini Enterprise Agent Platform, running inside the customer's own Google Cloud tenant on their own data. So instead of one AI project owned by engineering, you get sellers and marketers generating Gemini usage as part of their normal week. It's bought through Google Cloud Marketplace, so it lands against committed spend.

🗣️

Plain language in

A seller asks from the AI tool they already have open — Antigravity, Claude, Cursor or any MCP client. Atlas connects over MCP, so nobody switches assistants and nobody files a ticket.

🌐

A live page out

Branded, on the customer's own domain, with hosting, analytics, lead capture and version control attached. Edited in place afterwards, not thrown away and redone.

✨

An agent, every time

A Gemini Enterprise Agent Platform agent is created alongside the page automatically — governed, scoped, and pointed at the customer's own endpoints.

Why you care

What this does for your number

Four reasons to spend a call on it, in the order most sellers find useful.

Commit

It draws down committed spend

AtlasGTM is transactable on Google Cloud Marketplace. The customer buys it through their existing Google Cloud billing account, so the spend retires against their commitment instead of leaving the platform as a separate SaaS line item.

For an account sitting on an under-consumed commit, that is a clean, fast way to move burn-down without waiting on an infrastructure project.

Consumption

It grows Gemini usage every week

Most agent projects create one agent, owned by one team, used occasionally. AtlasGTM inverts that: every surface a seller or marketer publishes stands up its own agent, and those agents answer real questions from real buyers on live pages.

It doesn't matter which AI tool the request starts in — the agent it creates runs on Gemini. Usage scales with how much go-to-market work the customer does, not with how much engineering time they can free up.

Speed

It closes the gap between "listed" and "used"

The hard part of the agent motion isn't launching an agent, it's getting one into daily production use. Atlas removes the deployment step entirely — no Terraform, no platform engineer, no environment work.

That makes it a realistic first agent workload for accounts that have Gemini entitlement and nothing running on it yet.

Stickiness

It puts Google in front of the revenue org

This lands with the CRO, the VP of Marketing and RevOps — buyers who don't usually show up in a cloud conversation, and who hold budget that isn't currently pointed at Google.

Once their pages and agents run on Gemini in their own tenant, the dependency is on the platform, not on a pilot.

Why they care

What your customer gets

The problem it solves, in their words: AI agents are powerful, and completely cut off from the systems and content that make them useful.

🔌

Their AI tools finally reach their systems

Whichever assistant each team prefers, it gets one governed MCP connection instead of point-to-point integrations — 50+ connectors across CRM, content, collaboration, marketing, sales and support, with role-based access and scoped credentials.

🔒

Their data stays theirs

Deployed inside their own Google Cloud tenant, on their data, against their own Gemini endpoints. Centralized permissions, approval controls and logging — not unrestricted access to a production platform.

📉

Work that took weeks takes an afternoon

Campaign pages, partner pages, customer-specific pages — created, updated and measured without a queue. Pricing tracks creators, not audience, so the people viewing them are unlimited.

The motion

How to sell it in four moves

You don't need to demo it and you don't need to learn the platform. You need to make one introduction at the right altitude.

Spot the account

Look for two signals together: an under-consumed commit, and a marketing or revenue org that has been asking about AI with nothing in production. Gemini entitlement with no agent workload behind it is the clearest tell.

5 minutes, from your own pipeline

Open it with the CRO or CMO, not IT

This is a revenue-team purchase with a cloud consumption consequence. Leading with infrastructure buries it. Lead with "your sellers can publish on-brand customer pages from the AI tool they already use, and each one runs a Gemini agent in your tenant."

One line in a meeting you already have

Bring us in — we'll build their page first

Before the joint call, we'll stand up a page on their brand for their actual use case. It is faster to show than to explain, and it is the same thing they'd be buying. Use the form below.

48 hours' notice is plenty

Transact it on Marketplace

Route the purchase through Google Cloud Marketplace — private offer where the deal size warrants it — so the spend retires against their commit and the consumption shows up where you need it.

Standard Marketplace paperwork
Discovery

Five questions that open the door

Any one of these gets you to the conversation. None of them require you to say the word "agent" first.

"How much of your Gemini entitlement is actually being used week to week?"

Gets you straight to the consumption gap, which is the whole reason this exists.

"When marketing needs a page for a campaign or a big account, how long does that take?"

If the answer is measured in weeks, you have found the pain and the budget in one question.

"Which AI tools are your teams actually using day to day?"

Whatever the answer — Antigravity, Claude, Cursor, a mix — Atlas plugs into it. Nobody has to give anything up.

"Who owns AI over there — and has anything shipped past the pilot?"

Lots of entitlement, nothing in production is the single best-qualified situation for this.

"Does your data have to stay in your own tenant for this kind of thing?"

A yes here is a qualifier, not an objection — in-tenant deployment is the differentiator.
Handling

What they'll push back on

Short answers. Bring us in for anything deeper — you shouldn't have to defend the architecture.

"We're already standardized on Claude / Cursor / something else."

Good — keep it. AtlasGTM is an MCP application, so it plugs into Claude, Cursor, Codex, Google Antigravity or any MCP-compatible agent through one governed interface. There's no assistant to replace and no model lock-in to argue about.

Whichever tool they type into, the surfaces and the agents it creates run on Google Cloud — in their tenant, on Gemini.

"We'd just build this ourselves."

They can — plenty of teams have built an internal MCP server. What they then own is connector maintenance, credential scoping, permissioning, logging and an agent lifecycle, forever, for a capability that isn't their product.

The question to ask back: "who on your team is maintaining fifty connectors next quarter?"

"We can't have AI agents touching our customer data."

Agreed — which is why it deploys inside their own Google Cloud tenant, on their data, against their own Gemini endpoints. Access is role-based with scoped credentials, centralized connector administration, approval controls and full logging.

Agents get useful context and controlled actions. They do not get unrestricted access to the platform.

"How much implementation is this really?"

No Terraform, no environment build, no platform engineer. The first surface and its agent are stood up from a plain-language request in the AI tool they already use. That is the demo, and it is also the product.

Where it's running

Not a slideware agent

Named customers, agents in production, live on customer domains — and listed where your customer already buys.

In production

Live on customer domains

Customer surfaces running on their own domains on the current release, with hosting, analytics, lead capture and version control attached.

Agents

50+ auto-created agents

Created alongside surfaces on Gemini Enterprise Agent Platform — no Terraform and no engineer involved in any of them.

Listed

Marketplace and Agent Gallery

Transactable on Google Cloud Marketplace as a SaaS listing, and present in Agent Marketplace and Agent Gallery.

RiverMeadow DDN Saviynt
Positioning

Say this, not that

Two things keep this crisp: keep it at revenue-team altitude, and keep the claims exact.

Say

  • "It's an MCP application — it plugs into the AI tool you already use."
  • "Every page your team publishes runs a Gemini agent in your own tenant."
  • "It's bought through Marketplace, so it draws down your commit."
  • "Your sellers do this themselves — there's no engineering queue."
  • "Listed on Google Cloud Marketplace, and in Agent Marketplace and Agent Gallery."

Don't say

  • "It's an AI assistant" or "a chatbot." — it's the application layer the assistant calls.
  • "It's available inside Gemini Enterprise." — it isn't; the agents run on Agent Platform.
  • "100+ connectors." — it's 50+.
  • Anything about customer or user counts. Lead with named customers instead.
  • "It generates pages for you." — surfaces are edited in place and versioned, not regenerated.
Next step

Bring us into a deal

Tell us the account and what you need. If there's a meeting on the calendar, we'll build a page on that customer's brand before it — so you walk in with the thing, not the pitch.

We reply the same day. Nothing here gets forwarded to your customer.