What challenges did Emccamp Residencial face before AI implementation?

Marketing content production depended on external suppliers and slow back-and-forth processes. Internal teams spent significant time searching for information scattered across systems. Land acquisition research was slow and inefficient.

What was the impact of the AI solution?

Landing pages were built in four hours for R$50 instead of three weeks and R$8,000. Estimated marketing production savings from the GenMedia implementation reach R$170,000 to R$200,000. Land acquisition research previously took three to five weeks and now runs in minutes with a 33% efficiency gain. A 140-day land acquisition and regulatory approval process is being redesigned to run in 22 days.

Source

GCP

Emccamp Residencial automates marketing production and land research with Gemini Enterprise

Use case typeContent generationUpdated May 27, 2026

Emccamp Residencial is a Brazilian real estate and construction company that uses Google Cloud AI to automate marketing content production and internal knowledge access. With Cloud Ace handling the technical implementation, the company connects Gemini Enterprise Agent Platform, Imagen, Veo, Text-to-Speech API, Cloud Run and Cloud Storage to its existing environment. The solution supports faster creation of landing pages, images, video and voiceovers, and also automates land acquisition research through an agentic workflow connected to internal knowledge systems.

Organization: Emccamp Residencial
Industry: Real Estate
Location: Brazil
Published: May 2026

Reported outcomes

−96%

landing page cost reductionCost savings

50 BRLlanding page cost170,000 BRLestimated marketing production savings200,000 BRLestimated marketing production savings−33%land acquisition research cycle time reduction

Strategic outcomes

Speed & agilityMoved marketing production in-house for faster turnaroundEmployee experienceEnabled non-technical staff to build and deploy toolsScale & capacitySupported broader AI adoption across the companyCost efficiencyRedesigned the land acquisition process to compress approvals

Catalog median for cost savings deployments: −44.5% across 344 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 4

1Content generation
2Knowledge management
3Research collaboration

Marketing content production depended on external suppliers and slow back-and-forth processes.
Internal teams spent significant time searching for information scattered across systems.
Land acquisition research was slow and inefficient.

Cloud Ace connected Google Cloud tools to Emccamp's existing environment.
Gemini Enterprise Agent Platform is used with Imagen, Veo and Text-to-Speech API to generate marketing assets.
Gemini Enterprise connects directly to Emccamp's internal knowledge system through a native connector.
An agentic workflow built on Gemini Enterprise automates land acquisition research.

Technologies

Gemini Enterprise Gemini Enterprise Agent Platform Imagen Veo Text-to-Speech AI Cloud Run Cloud Storage

Landing pages were built in four hours for R$50 instead of three weeks and R$8,000.
Estimated marketing production savings from the GenMedia implementation reach R$170,000 to R$200,000.
Land acquisition research previously took three to five weeks and now runs in minutes with a 33% efficiency gain.
A 140-day land acquisition and regulatory approval process is being redesigned to run in 22 days.

Architecture

Cloud Ace connected Google Cloud tools to Emccamp's existing environment. The outward-facing workflow combines Gemini Enterprise Agent Platform with Imagen for image generation, Veo for video creation, and Text-to-Speech API for localized voiceover narration. The inward-facing workflow uses Gemini Enterprise with a native connector to Emccamp's internal knowledge system, and an agentic workflow automates land acquisition research. The article also references Cloud Run and Cloud Storage as part of the product stack.

Implementation partners1

Cloud Ace

Sources & evidence1

Groundedness: 5/5Type: Customer StoryPublished: May 27, 2026Publisher: Google CloudEvidence: PrimaryConfidence: High

Primary source

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