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AWS Software / SaaS Brazil

BDM

Document Processing

Document processing can be a momentous task where there are millions of documents to analyze. The process is even more complicated when grappling with unstructured data from a variety of formats, such as images, videos, and audio files.

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LOUIS cut operational effort by 40 percent—saving $4.2 million over five years.
LOUIS has accelerated decision-making down to just a few minutes by capturing the data required in just 60 seconds.
Since then, LOUIS has been applied to 15 different industries—now over 20 proofs of concepts are creating powerful results.
The organization has saved approximately 50 percent on development costs by using Amazon Bedrock versus writing the application from scratch.

Reported impact

Business outcome

Since then, LOUIS has been applied to 15 different industries—now over 20 proofs of concepts are creating powerful results. In the aviation space, LOUIS can assess various geographic regulations, taxes, and calculations to recommend the most cost-effective locations to refuel aircrafts. Key reported metrics include faster analyses 85%; accuracy 98%; reduction in operational effort 40%.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Risk Reduction
  • Scalability
  • Speed to Market

Solution

Learn how Amazon Bedrock helped BDM transform document processing. Manually analyzing complex documents is a painstaking but familiar process for many industries, from financial services to the legal sector and beyond. It takes a huge amount of time, resources, and effort. Every day employees spend hours sifting through large volumes of unstructured data. And by the time they have extracted and interpreted that data, human error can prevent accurate analyses. Big Data Mining (BDM) set out to solve this problem and make people’s day-to-day work more rewarding with the help of artificial intelligence (AI). Founded in Brazil back in 2018, BDM began by tackling a scenario that frustrates many consumers—searching for the best car to suit their needs within a crowded marketplace. Source data enters the BDM workflow through its application services, processed with Amazon Bedrock.

Data flow

Source data enters the BDM workflow through its application services, processed with Amazon Bedrock.

Solution architecture

1 components · 1 layer
  1. Compute
    • Amazon Bedrock Hosts the foundation models and inference APIs that power the generative AI features.

Architecture clues

  • Another model is reshaping end customers’ experiences within insurance through rapid response times.
  • LOUIS dramatically sped up the workflow by creating a process with standardized criteria for interpreting powers.

Evidence from the source

A large financial institution in Brazil is just one customer benefiting from greater efficiency and scale by deploying LOUIS.
Document processing can be a momentous task where there are millions of documents to analyze. The process is even more complicated when grappling with unstructured data from a variety of formats, such as images, videos, and audio files.
LOUIS cut operational effort by 40 percent—saving $4.2 million over five years.
LOUIS has accelerated decision-making down to just a few minutes by capturing the data required in just 60 seconds.
Since then, LOUIS has been applied to 15 different industries—now over 20 proofs of concepts are creating powerful results.
The organization has saved approximately 50 percent on development costs by using Amazon Bedrock versus writing the application from scratch.
Transparency Evidence & methodology 90/100 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 value90/100
  • 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
    8 metrics describe measurable impact.
  • Deployment scale5/10
    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 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 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. AWS

    BDM customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/bdm-case-study/
    Source type
    Vendor-published customer story
    Published
    Nov 14, 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

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

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