AWS Healthcare global

Pfizer

Enterprise Knowledge Search

Pfizer needed to centralize scientific and operational data, accelerate research and manufacturing decisions with AI, and complete a large-scale cloud transformation quickly enough to support medicine and vaccine delivery at global scale.

from 10 percent of its infrastructure in the cloud into one that’s 80 percent cloud based
launch 19 medicines and vaccines in just 18 months
migrate its 10 thousand servers to AWS in less than 1 year
more than 1.3 billion people with medicines and vaccines in 2022

Reported impact

Business outcome

Pfizer used AWS and AI to support treatment of more than 1.3 billion people, save tens of millions of dollars annually, move from 10 percent cloud to 80 percent cloud-based infrastructure, and pursue a target of launching 19 medicines and vaccines in 18 months.

  • Cost Reduction
  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Pfizer built a data-and-AI foundation on AWS to make research data easier to find and turn it into faster scientific decisions. The company centralized its data, created standard platforms, and built a secure global cloud foundation, then worked with AWS to create the Scientific Data Cloud, which aggregates data from hundreds of laboratory instruments so scientists can search it more easily. On top of that data layer, Pfizer built VOX, a generative-AI solution that uses large language models on Amazon SageMaker and Amazon Bedrock to accelerate research, predict product yield, and help teams deliver more medicines to patients. The same AWS foundation also supplied on-demand compute during COVID-19 and supported a rapid migration of applications, databases, and servers, connecting cloud modernization with data accessibility and AI-driven R&D workflows.

Data flow

Data from hundreds of laboratory instruments is aggregated into Pfizer's Scientific Data Cloud, where scientists can search it more easily; that centralized data foundation then feeds VOX, which uses Amazon SageMaker and Amazon Bedrock to generate research insights and product-yield predictions.

Solution architecture

2 components · 2 layers
  1. Compute
    • Amazon SageMaker Supports the large language model workflows behind Pfizer's VOX generative-AI solution.
  2. Serving
    • Amazon Bedrock Provides foundation-model capabilities used by VOX to accelerate research and predict product yield.

Architecture clues

  • AWS provided additional compute capacity on demand during the COVID-19 response.
  • Scientific Data Cloud aggregates data from hundreds of laboratory instruments.
  • VOX is a generative-AI solution built on top of Pfizer's centralized data foundation.

Evidence from the source

On AWS, Pfizer built VOX, a generative AI solution that uses large language models from Amazon SageMaker and Amazon Bedrock, to accelerate research, predict product yield, and help it deliver more medicines to patients.
Pfizer and AWS created the Scientific Data Cloud, which aggregates data from hundreds of laboratory instruments to make it simpler and faster for scientists to search the data.
launch 19 medicines and vaccines in just 18 months
saving tens of millions of dollars annually
transform from a company with just 10 percent of its infrastructure in the cloud into one that’s 80 percent cloud based
Transparency Evidence & methodology 75/100 reference value · 85% 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 confidence85%
  • 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 provenance8/10
    The publication date comes from publisher-controlled metadata.
  • 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. AWS

    Pfizer customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/pfizer-keynote-aws-reinvent-2023/
    Source type
    Vendor-published customer story
    Published
    Nov 29, 2023
    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

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.