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AWS Commercial and Professional Services India

Fractal Analytics

Customer Service Automation · Enterprise Knowledge Search

Fractal Analytics is a prominent provider of artificial intelligence solutions to Fortune 500 companies. To accelerate development while ensuring security at scale, the firm chose to build its Knowledge Assist solution on AWS.

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10–15 percent
15 million
30 percent

Reported impact

Business outcome

During a pilot program in the first six months after launch, nearly 500 knowledge workers in contact centers adopted Knowledge Assist as a unified knowledge base. The solution fielded hundreds of thousands of queries per month and seamlessly handled complex data from more than 10,000 documents across three file formats, namely .pdf, .docx, and .pptx.

  • Speed to Market

Solution

Fractal Analytics is a prominent provider of artificial intelligence solutions to Fortune 500 companies. To accelerate development while ensuring security at scale, the firm chose to build its Knowledge Assist solution on AWS. Fractal’s client base comprises numerous Fortune 500 companies, so data security and privacy are paramount when developing new products. Most clients deploy Fractal’s solutions within their private networks, so platform flexibility is also important. Having already executed many large-scale software as a service (SaaS) implementations on Amazon Web Services (AWS), including a chat solution that handles 15 million queries per month, Fractal chose to build Knowledge Assist on AWS. The company deployed its LLMs on , a fully managed service with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

Data flow

Data is stored in Amazon OpenSearch Service and processed by Amazon Bedrock, AWS Lambda.

Solution architecture

4 components · 3 layers
  1. Compute
    • Amazon Bedrock Hosts the foundation models and generative AI application logic.
    • AWS Lambda Runs event-driven application logic and serverless processing tasks.
  2. Storage
    • Amazon OpenSearch Service Stores and retrieves indexed or vectorized content for search and RAG workflows.
  3. Orchestration
    • Amazon Elastic Kubernetes Service Runs containerized services and scales application workloads across clusters.

Architecture clues

  • In addition to Amazon Bedrock, Fractal used Amazon Elastic Container Service (Amazon ECS) to build connectors for Knowledge Assist and for vector/semantic search.
  • The SaaS application layer runs on Amazon Elastic Kubernetes Service (Amazon EKS) and AWS Lambda serverless compute.

Evidence from the source

Fractal Analytics is a prominent provider of artificial intelligence solutions to Fortune 500 companies.
Fractal’s client base comprises numerous Fortune 500 companies, so data security and privacy are paramount when developing new products.
Most clients deploy Fractal’s solutions within their private networks, so platform flexibility is also important.
The global firm, one of India’s original AI unicorns, uses technologies including generative AI to empower imagination with data-driven intelligence.
To accelerate development while ensuring security at scale, the firm chose to build its Knowledge Assist solution on AWS.
believes human imagination is at the heart of every decision, whether or not that decision is aided by artificial intelligence (AI).
Transparency Evidence & methodology 66/100 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 value66/100
  • 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 specificity16/20
    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
    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 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

    Fractal Analytics customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/fractal-analytics-case-study/
    Source type
    Vendor-published customer story
    Published
    Jun 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

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

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