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KPMG Public Sector India

Government of India

Customer Service Automation

India's digital public services needed to reach citizens across many languages and dialects, but traditional language tools lacked the scalability, real-time performance, and platform integration needed to reduce language barriers and support nationwide digital inclusion.

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hundreds of regional dialects
more than 12 reference applications
more than 15 services
more than 300 AI models

Reported impact

Business outcome

The initiative reduced language barriers in access to government services and increased digital inclusion at scale by operationalizing multilingual translation, speech recognition, and chatbot capabilities across more than 15 implemented services.

  • Customer Experience
  • Scalability

Solution

The Government of India worked with KPMG in India to build a broad multilingual AI platform for citizen-facing digital services. At the core of the implementation is a language AI platform powered by more than 300 AI models that support more than 220 languages. The platform combines translation, speech recognition, and chatbot integration so speech and text from citizens can be processed in native Indian languages and then delivered back through government service channels. KPMG's contribution extended beyond technical delivery into policy frameworks, NLP enablement, and expansion strategy, helping the initiative scale responsibly across services. The result was not a concept deck but an operational stack with more than 15 implemented services and more than 12 reference applications used to test, refine, and extend multilingual service delivery across the public sector.

Data flow

Citizen speech and text requests enter the language AI platform through government digital services, speech recognition converts audio input where needed, AI models perform multilingual processing and translation, and the outputs are returned through translated interfaces and chatbot-enabled service channels.

Solution architecture

6 components · 4 layers
  1. Ingest
    • speech recognition service Converts spoken citizen input into machine-readable text for multilingual service delivery.
  2. Compute
    • AI models Provide the multilingual AI processing that powers translation and other language tasks across more than 220 languages.
  3. Serving
    • language AI platform Acts as the central multilingual platform used to deliver AI-powered speech and text services across government channels.
    • translation service Translates digital-service content and citizen interactions into multiple native Indian languages.
    • chatbot integration Embeds multilingual AI capabilities into conversational government-service workflows.
  4. Governance
    • policy frameworks Guides how the language AI services expand and operate across public-sector programs.

Architecture clues

  • KPMG coupled AI technology delivery with policy frameworks, NLP capability building, and expansion strategy.
  • The implementation supported over 220 languages.
  • The multilingual platform was powered by more than 300 AI models.

Evidence from the source

KPMG in India's client leads a government initiative focused on using AI to translate speech and text into multiple native Indian languages to help reduce language barriers in accessing the government's services and increasing digital inclusion at scale.
Through KPMG in India's strategic guidance on AI-driven technology solutions, policy frameworks and expansion strategies, more than 15 services were implemented such as translation, speech recognition and chatbot integration, supported by more than 12 reference applications for testing and refinement.
Traditional language solutions lacked scalability, real-time performance and platform-integration, further limiting reach and impact.
With support from KPMG in India, a broad-ranging language AI platform was developed, powered by more than 300 AI models and supporting over 220 languages.
Transparency Evidence & methodology 75/100 reference value · 83% 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 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 confidence83%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability25/25
    12 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency10/10
    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. KPMG

    Reducing language barriers and increasing inclusion

    First-party
    https://kpmg.com/xx/en/client-stories/reducing-language-barriers-and-increasing-inclusion.html
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Industry
    • Region
    • Company size
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • Business outcome
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Feb 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 Mar 31, 2026 · KPMG · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source

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