Cohere Software / SaaS Global

Oracle

Customer Service Automation · Sales Automation · Supply Chain Optimization

Oracle wanted to embed generative AI into Oracle Fusion Applications for a global base of more than 14,000 customers without breaking existing workflows, while still preserving enterprise-grade privacy, security, and a flexible path for customers and partners to add their own AI features.

over 100 GenAI use cases
over 14,000 customers

Reported impact

Business outcome

Oracle delivered over 100 embedded generative AI use cases across Fusion Applications, helping customers improve the speed and accuracy of finance, HR, supply chain, and customer-experience processes while keeping tight control over data governance and security through OCI.

  • Compliance
  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

Oracle integrated Cohere's Command R and Command R+ models into Oracle Fusion Applications and ran the resulting features on Oracle Cloud Infrastructure so generative AI could show up directly inside finance, supply chain, HR, sales, marketing, and service workflows. The architecture pairs those language models with retrieval-augmented generation patterns built on Cohere Embed and Cohere Rerank, which help ground outputs in enterprise data and reduce hallucinations before answers reach end users. Oracle used this stack to deliver more than 100 generative AI use cases such as insight narratives, supplier recommendations, candidate assistants, service-webchat summaries, and assisted authoring. Because the capabilities ship inside Fusion's regular update cycle and governance model, customers get faster adoption, tighter data control, and a reusable framework for extending AI across core business processes.

Data flow

A Fusion user triggers an in-app workflow such as reporting, sourcing, recruiting, or service support; Fusion retrieves relevant enterprise context with Cohere Embed and Cohere Rerank, Command R or Command R+ generates the narrative or recommendation, and OCI delivers the result inside Oracle's governed cloud application environment.

Solution architecture

6 components · 4 layers
  1. Ingest
    • Cohere Embed Supports retrieval-augmented generation by representing enterprise content for search and grounding.
  2. Compute
    • Command R Provides the language-model layer for embedded generative AI tasks inside Fusion workflows.
    • Command R+ Handles more demanding enterprise reasoning and generation tasks across Fusion use cases.
    • Cohere Rerank Improves response quality by pushing the most relevant retrieved context to the top before generation.
  3. Serving
    • Oracle Fusion Applications Presents the embedded generative AI features inside Oracle's finance, HR, supply chain, sales, marketing, and service workflows.
  4. Orchestration
    • Oracle Cloud Infrastructure (OCI) Hosts the governed Oracle environment where the embedded AI services run and receive regular upgrades.

Architecture clues

  • Customers can adopt new embedded AI features through Oracle Fusion Applications' quarterly updates.
  • Oracle designed the framework so customers and partners can add their own generative AI features.
  • The applications use retrieval-augmented generation (RAG) systems to reduce hallucinations.

Evidence from the source

Applications were built with retrieval-augmented generation (RAG) systems to help reduce hallucinations and optimized for search and retrieval with Cohere Embed and Cohere Rerank
Built on Oracle Cloud Infrastructure (OCI)
Oracle integrated Cohere’s Command R and Command R+ models into its Fusion Cloud Applications
The Cohere team worked with the Oracle Fusion Applications team on over 100 GenAI use cases embedded across finance, supply chain, HR, sales, marketing, and service workflows.
With Oracle Fusion Applications’ quarterly updates, customers can easily adopt new embedded AI features
Transparency Evidence & methodology 75/100 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 value75/100
  • 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 impact0/25
    0 metrics 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 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. Cohere

    Oracle customer story

    First-party
    https://cohere.com/customer-stories/oracle
    Source type
    Vendor-published customer story
    Published
    Sep 4, 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 Sep 4, 2024 · Cohere · 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.