Microsoft Commercial and Professional Services United States

PwC

Document Processing · Enterprise Knowledge Search · Fraud Detection

ChatPwC has achieved widespread adoption with hundreds of thousands of employees benefiting from increased productivity and the capacity to realize additional value for customers.

During its Microsoft GenAI pilot program, 88% of users said it saved time, 86% of users were highly satisfied, and 69% of users said it improved accuracy and...
PwC, operating in 151 countries
PwC, operating in 151 countries and serving a client base that includes 87% of the Fortune Global 500 companies, has consistently prioritized equipping its...
These learning events have helped drive 95% engagement with My AI.

Reported impact

Business outcome

ChatPwC has achieved widespread adoption with hundreds of thousands of employees benefiting from increased productivity and the capacity to realize additional value for customers.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

PwC built its solution on Azure OpenAI Service, Azure AI Foundry, Azure AI Search to automate high-friction work and turn enterprise data into faster decisions. The implementation combines cloud application services, data services, and AI capabilities so business users can search, summarize, analyze, or act on information through a single digital workflow instead of manual handoffs. It also connects with surrounding systems such as Microsoft Word, GitHub Copilot, which lets the new Azure-based workflow fit into existing operations rather than standing apart from them. ChatPwC has achieved widespread adoption with hundreds of thousands of employees benefiting from increased productivity and the capacity to realize additional value for customers.

Data flow

Content and operational context are prepared in Azure AI Search, passed to Azure OpenAI for generation or extraction, stored in Azure storage services, and returned through the user-facing application to employees or customers with grounded responses.

Solution architecture

5 components · 4 layers
  1. Ingest
    • Azure AI Document Intelligence Extracts text, layout, and document fields before downstream AI or analytics processing.
  2. Compute
    • Azure OpenAI Service Runs large language model inference for summarization, extraction, conversational AI, and grounded answers.
    • Azure Kubernetes Service Hosts scalable APIs, microservices, and model-serving workloads in containers.
  3. Serving
    • Azure AI Search Indexes enterprise content and retrieves grounding context for assistants and search experiences.
  4. Orchestration
    • Azure AI Foundry Provides the managed environment for model orchestration, experimentation, and responsible AI operations.

Architecture clues

  • Applies retrieval-based grounding with Azure AI Search before answer generation.
  • Extracts text and document structure prior to downstream AI processing.
  • Runs scalable containerized services on Azure Kubernetes Service.
  • Uses Azure OpenAI for grounded generation or large-scale extraction workloads.

Evidence from the source

ChatPwC and these proprietary plugins utilize Microsoft Azure OpenAI Service Azure AI Document Intelligence , and Azure AI Search for retrieval-augmented generation (RAG).
ChatPwC has achieved widespread adoption with hundreds of thousands of employees benefiting from increased productivity and the capacity to realize additional value for customers.
He has been especially impressed by the text extraction and optical character recognition capabilities of Azure AI Document Intelligence as well as the semantic ranking capabilities of Azure AI Search, both of which have helped ChatPwC and...
PwC, operating in 151 countries and serving a client base that includes 87% of the Fortune Global 500 companies, has consistently prioritized equipping its employees with the most effective solutions available.
To seamlessly integrate with PwC's proprietary plugin architecture, ChatPwC leverages Azure Kubernetes Service to integrate these services as APIs.
Transparency Evidence & methodology 100/100 reference value · 89% 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
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • 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 provenance10/10
    The publication date comes from the source page or publisher.
  • 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. Microsoft

    PwC customer story

    First-party
    https://www.microsoft.com/en/customers/story/1778147923888814642-pwc-azure-ai-document-intelligence-professional-services-en-united-states
    Source type
    Vendor-published customer story
    Published
    Jun 27, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Jun 27, 2024 · Microsoft · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.