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Databricks Commercial and Professional Services Germany

zeb

Real-time Analytics · Security and Compliance

The team at zeb is passionate about driving digital transformation for their customers through a deep understanding of their business challenges.

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40% Faster time to solution
72% Increase in demand for
Rather than manually processing data requests, the system transformed user requests into actionable insights, significantly reducing the workload on data analysts by up to 80–90%.
Stated Sid Vivek, “We can confidently say we reduced the time to develop our solution by 40% using the Databricks Data + AI Platform.

Reported impact

Business outcome

zeb achieved measurable business impact on Databricks, including 40% Faster time to solution; 72% Increase in demand for; With over 15 years of experience and a team of more than 1,500 experts, zeb is a leader in digital transformation strategy..

  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

zeb recently launched their SuperInsight self-service reporting engine, built with Databricks tools. Part of their SuperDesk suite of AI-powered service desk products that integrate with existing platforms like ServiceNow and Jira, the idea arose from growing demand from customers who were also using the Databricks Data + AI Platform . These customers sought a way to interact with their data more efficiently and reduce the workload on data analysts who manually handle numerous requests. zeb initially developed a generative AI–based system for a large enterprise logistics company, where data plays a critical role. Prior to the implementation, a team of several data analysts managed a substantial backlog of data requests.

Data flow

Operational, customer, and transactional data lands in Databricks and is transformed into curated datasets

Solution architecture

2 components · 2 layers
  1. Compute
    • Agent Bricks Supports agentic or GenAI applications that zeb uses to automate or accelerate work.
  2. Serving
    • Databricks Data Warehouses Provides warehouse compute for cross-functional reporting and governed analytics.

Architecture clues

  • Cloud: AWS
  • Key product: Agent Bricks
  • Key product: Databricks Data Warehouses

Evidence from the source

40% Faster time to solution
72% Increase in demand for
Rather than manually processing data requests, the system transformed user requests into actionable insights, significantly reducing the workload on data analysts by up to 80–90%.
Stated Sid Vivek, “We can confidently say we reduced the time to develop our solution by 40% using the Databricks Data + AI Platform.
With over 15 years of experience and a team of more than 1,500 experts, zeb is a leader in digital transformation strategy.
Transparency Evidence & methodology 90/100 reference value · 89% 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
    5 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • 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 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. Databricks

    zeb customer story

    First-party
    https://www.databricks.com/customers/zeb
    Source type
    Vendor-published customer story
    Published
    Aug 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

  • Published on source page Aug 1, 2024 · Databricks · 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.

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