Microsoft Nonprofit United Kingdom

Royal National Institute of Blind People

Customer Service Automation

Doctors found a tumor in Tony’s brain when he was a teen, which caused degenerative sight loss. When he was registered blind in his thirties, he struggled to adapt and mostly gave up on being active.

Meanwhile, around 2 million people in the UK are living with sight loss, a figure that will increase year after year.

Reported impact

Business outcome

RNIB anticipates that the AI-based Mailings will drastically reduce the time it takes to convert a document. Initial runs have cut conversion time from up to two weeks to as few as three hours now, RNIB estimates.

  • Customer Experience
  • Productivity Improvement

Solution

The Royal National Institute of Blind People (RNIB) works to remove societal barriers for people with blindness and sight loss. It converts clients’ communication into accessible formats, which required writing custom code and hand-fixing mistakes. Doctors found a tumor in Tony’s brain when he was a teen, which caused degenerative sight loss. When he was registered blind in his thirties, he struggled to adapt and mostly gave up on being active. Decades later, though, a friend invited him to train for a 5k and did not give up until Tony agreed. “I never imagined I could run with limited vision. But with the support of people around me, I can do this,” Tony says. He now advocates for other people who are blind or partially sighted to try a sport, including through the Royal National Institute of Blind People (RNIB). He says, "Just because we can’t see or can’t see fully, doesn’t mean we can’t do. We've just got to believe in ourselves, and magic will happen.”

Solution architecture

1 components · 1 layer
  1. Compute
    • Azure AI Services Supplies pretrained AI capabilities such as language, speech, or vision inside the business workflow.

Architecture clues

  • It uses Microsoft Azure AI services to automatically convert documents, from a mortgage application to an explanation of a medication’s potential side effects, into an internal markup tool.
  • It’s been a continual process of experiment, refine, and demonstrate what we can do with Azure AI to help make society more accessible for all.
  • The nonprofit handles sensitive data, from debit card PINs to explanations of medical diagnoses, none of which is retained in the Mailings solution.
  • Using Microsoft Azure AI services, RNIB’s new Mailings solution drastically reduces the time needed to convert communications into the different formats to ensure that documents are accessible to the people receiving them.
  • “It’s just an API away,” Pearson says.
  • “The optical character recognition is fantastic, allowing a person to step in after and refine the output.” Further, nontechnical staff and volunteers will not need complex training to use the solution and can now lead this workstream, since the process no longer requires writing custom code.

Evidence from the source

It converts clients’ communication into accessible formats, which required writing custom code and hand-fixing mistakes.
It uses Microsoft Azure AI services to automatically convert documents, from a mortgage application to an explanation of a medication’s potential side effects, into an internal markup tool.
RNIB developed an AI-based solution to streamline and scale up this accessible document service.
That step will further consolidate the disparate processes and increase organizations’ ability to provide documentation in Welsh as well as English.
The Royal National Institute of Blind People (RNIB) works to remove societal barriers for people with blindness and sight loss.
Using Microsoft Azure AI services, RNIB’s new Mailings solution drastically reduces the time needed to convert communications into the different formats to ensure that documents are accessible to the people receiving them.
Transparency Evidence & methodology 70/100 reference value · 87% 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 value70/100
  • 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 impact15/25
    1 metric describe measurable impact.
  • Deployment scale0/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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
    6 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 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. Microsoft

    Royal National Institute of Blind People customer story

    First-party
    https://www.microsoft.com/en/customers/story/18764-royal-national-institute-of-blind-people-azure-ai-services
    Source type
    Vendor-published customer story
    Published
    Oct 1, 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 Oct 1, 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.