Google Cloud Real Estate and Construction United States

CBRE

Document Processing · IT Operations Automation

As the world's largest commercial real estate services and investment firm, CBRE delivers a fully integrated suite of financial, property accounting, and fund administration services. Managing asset-level transactional processing and generating complex financial statements across a massive global portfolio requires immense operational precision.

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CBRE is leveraging the Gemini platform with the help of McKinsey to rapidly build, deploy, and scale enterprise-grade AI agents.

To manage latency, the ADK supports both asynchronous and synchronous generation patterns.

We believe AI agents will alleviate these pain points, but a robust architecture is essential to enable successful deployment.

Reported impact

Business outcome

"Building an agentic AI capability allows us to reimagine processes, build intelligent workflows, and optimize execution at enterprise scale. The real value of AI automation comes from consistency, scale, and disciplined execution, not isolated use cases," says Georgeta Ionescu, Managing Director, Global Business Process Transformation, CBRE.

Solution

CBRE is leveraging the Gemini platform with the help of McKinsey to rapidly build, deploy, and scale enterprise-grade AI agents. To continue improving service delivery for commercial property owners and managers, CBRE has been modernizing its facilities management technology stack. The company launched a multi-year digital initiative focused on improving operational efficiency, scaling its internal Ellis AI platform, and developing predictive maintenance capabilities. Moving beyond simple point-automation required a flexible framework designed to support AI-enabled workflows with appropriate oversight and controls. Traditional CRE operations involve a high level of manual interventions and rote document and information processing. We believe AI agents will alleviate these pain points, but a robust architecture is essential to enable successful deployment.

Solution architecture

2 components · 1 layer

Orchestration
- Gemini platform Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
- Agent Development Kit (ADK) Coordinates multi-agent workflows, tool calling, and governed automation across the application.

Architecture clues

"Building our agentic AI platform in-house gives us more flexibility to adapt to evolving business needs and develop new capabilities for our workflows and clients.
A key technology supporting this effort is the Gemini platform, which helps CBRE centrally create, run, and govern agentic AI workflows.
The ADK provided capabilities that support CBRE's multi-agent workflows.
Working together, the teams followed a disciplined, four-step framework.

Evidence from the source

"Google Cloud and Gemini platform are helping us build and deploy AI-enabled services with more flexibility and consistency as our agentic AI capabilities mature," says Surya Rebbapragada, Group Vice President, Head of D

A key technology supporting this effort is the Gemini platform, which helps CBRE centrally create, run, and govern agentic AI workflows. CBRE developers used the Google Cloud Agent Development Kit (ADK) to build and depl

Building a robust architecture with Gemini platform

CBRE is leveraging the Gemini platform with the help of McKinsey to rapidly build, deploy, and scale enterprise-grade AI agents.

Google Cloud Agent Development Kit (ADK) has helped streamline development and deployment for selected AI use cases

Transparency Evidence & methodology 56/100 reference value · 75% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value56/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth15/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity16/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact0/25
0 metrics describe measurable impact.
Deployment scale5/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence75%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability21/25
10 of 12 key fields link to a named source.
Direct evidence20/20
5 direct source quotes retained.
Date provenance0/10
The publication date has no documented provenance.
Source transparency8/10
1 source reference document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity6/10
Rewards source-backed components, data flow, quantified impact, and a specific outcome.

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

Google Cloud
CBRE customer story

First-party
https://cloud.google.com/customers/cbre

Source type
Vendor-published customer story
Supports
- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes

Date evidence

First archived Oct 9, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
Open date source
Sitemap last modified Oct 8, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
Open date source

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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