Google Cloud Public Sector India

DMRC

Application Refactoring · IT Operations Automation · Real-time Analytics

The Delhi Metro Rail Corporation (DMRC) is one of Asia’s largest transit networks, carrying more than seven million commuters every day across the Delhi-NCR region. With 395 kilometers of track and 289 stations, DMRC has long been a leader in transportation efficiency and safety in India’s capital.

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Aligns teams with 300+ shared performance dashboards using BigQuery and KPI dashboards

Cuts service disruptions, targeting 10-20% less downtime for passengers, with predictive analytics in Vertex AI and Dataflow

The Delhi Metro Rail Corporation plans, builds, and operates one of Asia’s largest metro networks, serving 7M+ daily riders across the Delhi-NCR region with 395km, known for safety, efficiency, and innovation.

Reported impact

Business outcome

Predicts failures early, keeping passenger journeys smooth, with Gemini in Vertex AI; Gives operations teams instant access to unified data with BigQuery; Detects faults in real time to improve service reliability with Dataflow; Aligns teams with 300+ shared performance dashboards using BigQuery and KPI dashboards.

Solution

Beyond the engineering teams, the platform empowers leadership through an interactive app interface built on Google Cloud, which provides real-time visibility into operational performance and enables timely strategic interventions from anywhere. In the background, Cloud SQL is used for maintaining logs and configuration tables, while Cloud Run is used for all small transactional processing functions. Underpinning all of this is a modular cloud architecture that standardizes data integration, ensuring that as DMRC expands its network, this digital backbone is ready to scale with it. This data-driven intelligence extends into the depot, where DMRC is transforming manual workflows with Generative AI. in Vertex AI helps the system decipher complex depot maintenance logs to automatically assign the correct sub-systems for repair.

Data flow

stored or unified in BigQuery, Cloud Storage, Cloud SQL, processed with Vertex AI, orchestrated via Google Kubernetes Engine (GKE), and surfaced through Gemini, Cloud Run

Solution architecture

7 components · 4 layers

Compute
- Vertex AI Hosts and orchestrates machine learning and generative AI capabilities inside the solution.
Storage
- BigQuery Stores and queries the analytics and AI-ready datasets that power the workload.
- Cloud Storage Stores large files, lake data, or static assets that feed downstream analytics and applications.
- Cloud SQL Provides the managed relational data layer for operational application data.
Serving
- Gemini Powers natural-language reasoning, summarization, generation, or assistance inside the workflow.
- Cloud Run Runs application services and proprietary code in a managed container environment.
Orchestration
- Google Kubernetes Engine (GKE) Orchestrates the platform's containerized services and high-concurrency workloads.

Architecture clues

Aligns teams with 300+ shared performance dashboards using BigQuery and KPI dashboards
Cuts service disruptions, targeting 10-20% less downtime for passengers, with predictive analytics in Vertex AI and Dataflow
Gemini LLM CloudSQL Cloud Storage
Gives operations teams instant access to unified data with BigQuery
Predicts failures early, keeping passenger journeys smooth, with Gemini in Vertex AI
The Delhi Metro Rail Corporation uses Google Cloud to move to predictive maintenance and reduce downtime across operations.

Evidence from the source

Aligns teams with 300+ shared performance dashboards using BigQuery and KPI dashboards

Cuts service disruptions, targeting 10-20% less downtime for passengers, with predictive analytics in Vertex AI and Dataflow

Detects faults in real time to improve service reliability with Dataflow

Gives operations teams instant access to unified data with BigQuery

Predicts failures early, keeping passenger journeys smooth, with Gemini in Vertex AI

Transparency Evidence & methodology 95/100 reference value · 82% confidence · 2 sources

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 value95/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth20/25
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 confidence82%

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 evidence20/20
5 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 specificity10/10
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
DMRC customer story

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

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
Delhi Metro Rail Corporation
Welcome to Delhi Metro Rail Corporation(DMRC) | Official Website

First-party
https://delhimetrorail.com/pages/en/contact-us
Official site footer strings list: 'Metro Bhawan Fire Brigade Lane, New Delhi - 110001, India.'

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 Feb 6, 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.

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