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Snowflake Real Estate and Construction Saudi Arabia

Red Sea Global

Real-time Analytics

consolidate dozens of disconnected data environments so teams could understand visitor journeys, operations, and sustainability performance and use agentic AI without growing the data team.

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100s Of hours saved through Agentic AI
50+ Data environments consolidated into one

Reported impact

Business outcome

Red Sea Global achieved 100s Of hours saved through Agentic AI and 50+ Data environments consolidated into one.

  • Productivity Improvement

Solution

Red Sea Global uses Snowflake, Cortex AI, Streamlit, and Apache Iceberg on Snowflake to consolidate dozens of disconnected data environments so teams could understand visitor journeys, operations, and sustainability performance and use agentic AI without growing the data team. Guest, iot, sustainability, and operations data are centralized in Snowflake so resort, sustainability, and analytics teams can work from a faster, more reliable operating view. Cortex AI adds in-platform LLM and agentic capabilities for classification, summarization, retrieval, or natural-language analytics on top of the curated data foundation. Keeping ingestion, storage, transformation, and governed access on one platform reduces handoffs, simplifies operations, and makes it easier to reuse trusted data across analytics, reporting, and AI. Red Sea Global achieved 100s Of hours saved through Agentic AI and 50+ Data environments consolidated into one.

Data flow

guest, IoT, sustainability, and operations data flow into Snowflake, Cortex AI, Streamlit, and Apache Iceberg; curated data, reports, and model outputs are then served to resort, sustainability, and analytics teams.

Solution architecture

4 components · 3 layers
  1. Compute
    • Cortex AI Provides in-platform AI functions used for summarization, classification, generation, or model-assisted analysis.
  2. Storage
    • Snowflake Provides the centralized data platform that stores governed datasets and serves analytics or AI workloads.
    • Apache Iceberg Provides open table interoperability for shared data stored across the broader data ecosystem.
  3. Serving
    • Streamlit Provides the user interface for analytics or AI applications backed by Snowflake data.

Architecture clues

  • Apache Iceberg keeps the broader data estate interoperable while Snowflake handles the high-value analytics workloads.
  • Cortex AI adds in-platform LLM and agentic capabilities for classification, summarization, retrieval, or natural-language analytics on top of the curated data foundation.
  • Integrates with AWS as part of the broader delivery workflow.
  • Streamlit helps expose curated outputs through self-service applications and dashboards so non-technical users can act on the data quickly.

Evidence from the source

100s Of hours saved through Agentic AI
50+ Data environments consolidated into one
For the last year, RSG has used Snowflake’s AI Data Cloud, running on Amazon Web Services (AWS), to provide the insights needed to power its sustainable, luxury travel experiences
Red Sea Global achieved 100s Of hours saved through Agentic AI and 50+ Data environments consolidated into one
With Snowflake, Red Sea Global enables self-service data exploration to power its regenerative development and tourism
Transparency Evidence & methodology 100/100 reference value · 79% confidence · 1 source

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 value100/100
  • 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 impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • 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 specificity10/10
    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. Snowflake

    Red Sea Global customer story

    First-party
    https://www.snowflake.com/en/customers/all-customers/case-study/red-sea-global/
    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 Dec 6, 2025 · 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 Jul 9, 2026 · Snowflake · 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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