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AWS Software / SaaS Taiwan

KDAN

Document Processing · Enterprise Knowledge Search

KDAN wanted to add generative AI to its document management and eSignature products so it could automate analysis, extraction, and internal assistance while getting new offerings to market faster.

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50%
75%
KDAN Accelerates Development of Generative AI Applications by 50% with Amazon Bedrock
faster development of generative AI applications on AWS 50%

Reported impact

Business outcome

KDAN reported KDAN Accelerates Development of Generative AI Applications by 50% with Amazon Bedrock, faster development of generative AI applications on AWS 50%, and increase in reporting efficiency 75%, improving AI-assisted document intelligence and workflow automation with measurable gains in productivity improvement, speed to market, and customer experience.

  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

KDAN uses Amazon Bedrock, Amazon SageMaker, Llama 2, and Claude 3 to support AI-assisted document intelligence and workflow automation. KDAN wanted to add generative AI to its document management and eSignature products so it could automate analysis, extraction, and internal assistance while getting new offerings to market faster. In practice, the implementation runs transformation or AI logic on Amazon Bedrock. Documents, notes, and structured records are consolidated into a governed cloud data layer; Amazon Bedrock applies analytics or model logic. The case study reports KDAN Accelerates Development of Generative AI Applications by 50% with Amazon Bedrock, faster development of generative AI applications on AWS 50%, and increase in reporting efficiency 75%, showing measurable improvement after the rollout.

Data flow

Documents, notes, and structured records are consolidated into a governed cloud data layer; Amazon Bedrock applies analytics or model logic.

Solution architecture

4 components · 1 layer
  1. Compute
    • Amazon Bedrock Provides managed foundation models and prompt workflows for the application
    • Amazon SageMaker Supports model development, tuning, evaluation, or managed generative ai inference
    • Llama 2 Provides the language or embedding model used for reasoning, retrieval, or response generation
    • Claude 3 Provides the language or embedding model used for reasoning, retrieval, or response generation

Evidence from the source

50%
KDAN Accelerates Development of Generative AI Applications by 50% with Amazon Bedrock
faster development of generative AI applications on AWS 50%
improvement in search results for students 70%
increase in reporting efficiency 75%
Transparency Evidence & methodology 85/100 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 value85/100
  • 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 impact25/25
    6 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
    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 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

    KDAN customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/case-study-kdan/
    Source type
    Vendor-published customer story
    Published
    Jul 4, 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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