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IBM Software / SaaS Australia

Knowledge Orchestrator

Enterprise Knowledge Search

Knowledge Orchestrator was created to prevent catastrophic corporate knowledge loss and make tacit technical and commercial know-how usable for pricing, investment and day-to-day decision-making.

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0.9 articles written per employee per day
500,000 words
9.3 articles read per employee per day
more than 2,000 articles

Reported impact

Business outcome

Knowledge Orchestrator created a living enterprise knowledge base that improved employee productivity and engagement while giving the company a commercial platform it could extend into additional analytics use cases.

  • Productivity Improvement
  • Scalability

Solution

Knowledge Orchestrator built its enterprise knowledge platform on IBM Cloud and IBM watsonx.ai using an approach it calls pre-enhanced generation (PEG). The company started by deploying its application in a Kubernetes cluster on IBM Cloud for scalability and availability, then used watsonx.ai to run content-generation algorithms that turn raw unstructured material—such as spreadsheets and sales information—into structured natural language that becomes “ground truth” for later use. Rather than relying on classic retrieval-augmented generation alone, the platform segments knowledge into domains and continuously converts operational artifacts into reusable knowledge objects, lowering hallucination risk and inference cost. The result is a searchable, AI-assisted enterprise knowledge base that employees use daily through a common interface, while the same foundation is being extended into use cases such as sales enablement, forecasting and profitability simulation for customers.

Data flow

Raw and unstructured business content is ingested into a Kubernetes-hosted IBM Cloud platform, transformed by watsonx.ai into domain-specific structured knowledge, and then served back to employees and customers through a searchable knowledge experience.

Solution architecture

2 components · 1 layer
  1. Compute
    • IBM Cloud Hosts the platform in a Kubernetes-based cloud architecture that supports scale and availability.
    • IBM watsonx.ai Runs the PEG content-generation algorithms that transform raw operational data into structured ground truth.

Architecture clues

  • Can call cloud NLP, speech-to-text and foundation-model services through watsonx.ai
  • Runs inside a Kubernetes cluster on IBM Cloud
  • Uses a PEG approach rather than standard RAG alone

Evidence from the source

As a part of the knowledge recovery plan, Dr. Brandt started building a prototype as a passion project on IBM Cloud.
This is done through a unique content generation algorithm, which was built and developed on IBM Cloud using the IBM watsonx.ai AI studio.
Today, with a team of five full time equivalents (FTEs) plus KIRA, an AI assistant, the company’s enterprise knowledge base contains more than 2,000 articles.
Transparency Evidence & methodology 100/100 reference value · 84% 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 value100/100
  • 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 impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence84%
  • 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 evidence15/20
    3 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. IBM

    Knowledge Orchestrator customer story

    First-party
    https://www.ibm.com/case-studies/knowledge-orchestrator
    Source type
    Vendor-published customer story
    Published
    Apr 22, 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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