Microsoft Software / SaaS India

Fendahl International

Document Processing · Enterprise Knowledge Search

Recognizing the game-changing potential of AI within its industry, Fendahl was determined to pioneer its adoption and harness its advantages early. With user experience being a critical aspect of its offerings, Fendahl aimed to revolutionize the industry by making its products more intuitive. Additionally, processes such as data reconciliation involved tedious manual methods.

Founded in 2012 in London, UK, Fendahl is a rapidly growing software firm with a dedicated team of over 350 employees and offices in the United States, United Kingdom, UAE, Singapore, and India.

Reported impact

Business outcome

The document intelligence feature facilitates automated data entry, processing, and reconciliation, resulting in a 50% improvement in productivity for users engaged in these tasks.

  • Customer Experience
  • Productivity Improvement

Solution

Recognizing the game-changing potential of AI within its industry, Fendahl was determined to pioneer its adoption and harness its advantages early. With user experience being a critical aspect of its offerings, Fendahl aimed to revolutionize the industry by making its products more intuitive. Fendahl International is a specialist provider of CTRM (commodity trading & risk management) software solutions. Founded in 2012 in London, UK, Fendahl is a rapidly growing software firm with a dedicated team of over 350 employees and offices in the United States, United Kingdom, UAE, Singapore, and India. The company’s CTRM software, Fusion, encompasses a range of functions such as trade capture and management, physical operations (logistics), financial settlement, risk management, decision support, and reporting tools.

Data flow

Content from enterprise documents and knowledge sources is ingested into Azure OpenAI Service, where it is indexed, enriched, or reasoned over before grounded answers are returned to employees through business applications.

Solution architecture

1 components · 1 layer
  1. Compute
    • Azure OpenAI Service Provides large language model inference for natural language search, drafting, summarization, and recommendations.

Architecture clues

  • Azure OpenAI Service: Provides large language model inference for natural language search, drafting, summarization, and recommendations.
  • Founded in 2012 in London, UK, Fendahl is a rapidly growing software firm with a dedicated team of over 350 employees and offices in the United States, United Kingdom, UAE, Singapore, and India.

Evidence from the source

Azure OpenAI Service was compatible with Fendahl’s technical ecosystem and therefore, the company readily leveraged it to implement AI applications and enrich its offerings.
Fendahl International is a specialist provider of CTRM (commodity trading & risk management) software solutions. Founded in 2012 in London, UK, Fendahl is a rapidly growing software firm with a dedicated team of over 350 employees and offices in the United States, United Kingdom, UAE, Singapore, and India.
Recognizing the game-changing potential of AI within its industry, Fendahl was determined to pioneer its adoption and harness its advantages early. With user experience being a critical aspect of its offerings, Fendahl aimed to revolutionize the industry by making its products more intuitive. Additionally, processes such as data reconciliation involved tedious manual methods.
The company chose to leverage Azure OpenAI Service to fulfil its requirements. Shrirang Pendharkar, Technical Director, Fendahl, explains, “We, at Fendahl, extensively use Microsoft products.
The document intelligence feature facilitates automated data entry, processing, and reconciliation, resulting in a 50% improvement in productivity for users engaged in these tasks.
Transparency Evidence & methodology 65/100 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 value65/100
  • 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 impact0/25
    0 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 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

    Fendahl International customer story

    First-party
    https://www.microsoft.com/en/customers/story/1762013135187474174-fendahl-azure-openai-service-professional-services-en-india
    Source type
    Vendor-published customer story
    Published
    May 6, 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 May 6, 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.