Google Cloud Real Estate and Construction New Zealand

FletcherTech

Enterprise Knowledge Search · IT Operations Automation

FletcherTech needed to roll out an enterprise AI assistant across a mixed-skill workforce, connect it to core systems, and keep governance under control while driving broad day-to-day adoption.

Reported impact

Business outcome

FletcherTech created a widely adopted enterprise AI assistant, saved thousands of staff hours, and opened a path for custom workflow agents under governed project controls.

  • Productivity Improvement
  • Risk Reduction
  • Speed to Market

Solution

FletcherTech uses Google Cloud to address this challenge: FletcherTech needed to roll out an enterprise AI assistant across a mixed-skill workforce, connect it to core systems, and keep governance under control while driving broad day-to-day adoption. Gemini Enterprise serves as the company-wide assistant for contextual answers, content generation, and data analysis across core data sets, serving as the serving layer. Vertex AI Agent Builder helps teams rapidly deploy custom agents such as QA and developer-coaching assistants, serving as the serving layer. Vertex AI provides the managed project environment for agent deployment, governance, and scale, serving as the orchestration layer. Core enterprise data is exposed through Gemini Enterprise connectors, employees query the platform in natural language, and Vertex AI Agent Builder delivers specialized agents that automate workflow tasks and surface contextual answers from systems such as Jira, ServiceNow, and SharePoint.

Data flow

Core enterprise data is exposed through Gemini Enterprise connectors, employees query the platform in natural language, and Vertex AI Agent Builder delivers specialized agents that automate workflow tasks and surface contextual answers from systems such as Jira, ServiceNow, and SharePoint.

Solution architecture

3 components · 2 layers
  1. Serving
    • Gemini Enterprise serves as the company-wide assistant for contextual answers, content generation, and data analysis across core data sets
    • Vertex AI Agent Builder helps teams rapidly deploy custom agents such as QA and developer-coaching assistants
  2. Orchestration
    • Vertex AI provides the managed project environment for agent deployment, governance, and scale

Architecture clues

  • Gemini Enterprise went live against most core data sets in three weeks
  • The team uses Gemini Enterprise to audit data sources for keys, salary data, and other sensitive content
  • Winning custom agents automate QA testing and coach developers

Evidence from the source

Gemini Enterprise went live in three weeks due to ease of setup, configuration, and management.
Over a three-month trial, Gemini Enterprise delivered 31,778 answers to 222 employees, saving more than 2,500 work hours.
Using Vertex AI Agent Builder to quickly deploy and scale these agents ensures rapid time-to-production.
Transparency Evidence & methodology 75/100 reference value · 75% confidence · 2 sources

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

01

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 value75/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    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 traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence15/20
    3 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency9/10
    2 source references document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

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

  1. Google Cloud

    FletcherTech customer story

    First-party
    https://cloud.google.com/customers/fletchertech
    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
  2. Fletcher Building

    Contact Us

    First-party
    https://fletcherbuilding.com/about-us/contact-us

    Street address: Fletcher Building Limited, 810 Great South Road, Penrose, Auckland 1061, New Zealand.

    Source type
    Official company website
    Supports
    • Region

Date evidence

  • First archived Feb 11, 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 Jan 21, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.