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Google Cloud Real Estate and Construction India

Square Yards

Sales Automation

Buying a home in India is a deeply emotional, sentiment-driven milestone that often requires a cross-family deliberation journey lasting up to three months.

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Cuts voice conversation latency to sub-700ms with Gemini APIs
Square Yards struggled to ingest and evaluate roughly 100 million legacy property records scattered across fragmented regional registrar offices.
Square Yards uses Google Cloud and Gemini to scale in-house agentic AI, running 200,000 daily calls with sub-700ms latency.
This ensures our sales executives focus entirely on meaningful, face-to-face closing conversations, driving an expected 1.5x leap in overall business efficiency.

Reported impact

Business outcome

Cuts voice conversation latency to sub-700ms with Gemini APIs Square Yards uses Google Cloud and Gemini to scale in-house agentic AI, running 200,000 daily calls with sub-700ms latency.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

Square Yards uses Google Cloud and Gemini to scale in-house agentic AI, running 200,000 daily calls with sub-700ms latency. Buying a home in India is a deeply emotional, sentiment-driven milestone that often requires a cross-family deliberation journey lasting up to three months. As an integrated real estate and mortgage platform, Square Yards coordinates the full property life cycle-from search and transaction to interior design and home financing-for eight million monthly visitors across nine countries. Square Yards completely modernized its platform blueprint, transitioning legacy workloads into a highly responsive microservices architecture running on Google Kubernetes Engine (GKE) and Cloud Run . To resolve persistent data ingestion limits, the team routed its pipelines into . This shift unlocked lightning-fast, automated analytics over their 100 million records, entirely eliminating historical database maintenance loops.

Solution architecture

6 components · 3 layers
  1. Compute
    • Gemini API Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
    • Document AI Extracts structured information from unstructured documents to reduce manual review.
  2. Storage
    • BigQuery Serves as the governed analytics and storage layer for high-volume operational data, features, and reporting.
  3. Orchestration
    • Gemini Enterprise Agent Platform Coordinates multi-agent workflows, tool calling, and governed automation across the application.
    • Gemini Enterprise Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
    • Agent Platform Coordinates multi-agent workflows, tool calling, and governed automation across the application.

Architecture clues

  • Buying a home can be a deeply emotional journey.
  • Deploying conversational and agentic layers to maximize human sales efficiency
  • Square Yards completely modernized its platform blueprint, transitioning legacy workloads into a highly responsive microservices architecture running on Google Kubernetes Engine (GKE) and Cloud Run .
  • Square Yards uses Google Cloud and Gemini to scale in-house agentic AI, running 200,000 daily calls with sub-700ms latency.

Evidence from the source

Buying a home can be a deeply emotional journey. When inquiries spiked, we used agentic AI not to replace humans, but to intelligently filter for true intent. This ensures our sales executives focus entirely on meaningfu
Compounding this front-end pressure was a severe data bottleneck. Square Yards struggled to ingest and evaluate roughly 100 million legacy property records scattered across fragmented regional registrar offices.
Cuts voice conversation latency to sub-700ms with Gemini APIs
Deploys multi-language voice calls through Gemini Enterprise Agent Platform
Square Yards uses Google Cloud and Gemini to scale in-house agentic AI, running 200,000 daily calls with sub-700ms latency.
Transparency Evidence & methodology 90/100 reference value · 77% 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 value90/100
  • 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 specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence77%
  • 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 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

    Square Yards customer story

    First-party
    https://cloud.google.com/customers/squareyards-ai
    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

  • Sitemap last modified Jul 24, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Jul 24, 2026 · Google Cloud · Unknown Legacy extracted date without source-page confirmation.
    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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