Use Case Intelligence RSS Search use cases Search
IBM Software / SaaS Netherlands

Pyramid Analytics

Enterprise Knowledge Search · Real-time Analytics

Pyramid Analytics wanted non-technical business users to get answers and visualizations from distributed enterprise data without waiting on overburdened data teams or learning specialist tools.

Read the original story Explore all use cases
under 30 seconds

Reported impact

Business outcome

Pyramid reduced the time from business question to actionable insight, widened access to analytics and created a foundation for secure generative BI inside customer environments.

  • Productivity Improvement
  • Scalability

Solution

Pyramid Analytics extended its GenBI product with IBM watsonx.ai to let business users request sophisticated analytics in natural language. The company’s customers often had data scattered across multiple repositories and represented differently in each system, which meant report requests queued up with technical teams and insight arrived too late for many decisions. By adding watsonx.ai to the stack, Pyramid built a natural-language layer that can interpret ambiguous user intent, map it to analytic needs and generate relevant insights and visualizations in seconds. IBM workshops and Build Lab collaboration accelerated the early design and implementation. The resulting architecture brings generative AI close to enterprise data while remaining deployable in private environments, so organizations can democratize analytics access without sending sensitive data into uncontrolled public workflows.

Data flow

Business users submit natural-language questions, watsonx.ai interprets the request against distributed enterprise data and the GenBI application returns analytics outputs and visualizations without waiting for manual report creation.

Solution architecture

1 components · 1 layer
  1. Compute
    • IBM watsonx.ai Understands user intent expressed in natural language and generates analytics outputs and visualizations.

Architecture clues

  • Can run on-premises or in a private cloud
  • Designed to be LLM-agnostic but implemented with watsonx.ai
  • Generates both insights and data visualizations from natural-language requests

Evidence from the source

That’s why we created Generative Business Intelligence (GenBI): a revolutionary AI-powered solution that allows non-technical users to describe their analytics needs in their own words and get insights and data visualizations for sophisticated requests within seconds.
The capabilities of IBM watsonx™ to understand natural language, whether it’s written or spoken, enable Pyramid to interpret user intent.
The result is that users benefit from an expert data analyst who’s available 24 hours a day and can respond to even the most complicated questions in under 30 seconds.
Transparency Evidence & methodology 90/100 reference value · 87% confidence · 2 sources

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 depth25/25
    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 impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    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 traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence15/20
    3 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency9/10
    2 source references 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. IBM

    Pyramid Analytics customer story

    First-party
    https://www.ibm.com/case-studies/pyramid-analytics
    Source type
    Vendor-published customer story
    Published
    Dec 18, 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
  2. GetLatka

    Pyramid Analytics Revenue 2024: $8.3M Est., $66.5M Raised

    Independent secondary
    https://getlatka.com/companies/pyramid-analytics

    The profile states Pyramid Analytics is based in Amsterdam, and its Organization schema lists addressCountry 'Netherlands'.

    Source type
    Structured company profile
    Supports
    • Region

Date evidence

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

Related

Similar use cases

See all like this
IBM Mar 24, 2026 IBM Human Resources Software / SaaS Global IBM HR needed to make people analytics easier for business leaders and non-technical users because dashboard growth alone still left managers dependent on analysts to interpret metrics, filters, and workforce context. 20% each year initial self-serve Dashboards Adoption Growth Enterprise Knowledge SearchReal-time Analytics IBM Cognos · IBM watsonx BI IBM Apr 15, 2024 Polpo Software / SaaS Netherlands Polpo needed to automate political and parliamentary monitoring because analysts were spending too much time manually searching many disparate data sources and could not efficiently surface the context relevant to… 75% less time searching for relevant political information Enterprise Knowledge SearchReal-time Analytics IBM Content Analytics Studio · IBM Watson Discovery · IBM Watson Explorer Google Cloud Archived · Jul 31, 2026 Aible Software / SaaS United States Aible needed to deploy secure enterprise AI agents quickly while keeping customer data inside each enterprise cloud boundary, avoiding idle GPU cost, and proving that large-scale analytics and model orchestration could… 1000x analytics cost efficiency gain Enterprise Knowledge SearchReal-time Analytics BigQuery · Cloud Run · G4 GPUs