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Oracle Manufacturing Global

MineSense Technologies

Enterprise Knowledge Search · Real-time Analytics

MineSense needed a cloud data platform that could ingest large volumes of industrial IoT data from mining operations, support analytics and machine learning in near real time, and give a small IT team a scalable way to deliver secure customer insights without escalating database administration overhead.

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DBA workloads lowered by 65%
Field engineering productivity improved by 5X
Low-code apps delivered in one week instead of six
Saved hundreds of hours per year in development and maintenance time

Reported impact

Business outcome

MineSense scaled its mining data platform past 55 TB while cutting DBA workload by 65%, improved field-engineering productivity by 5X, saved hundreds of hours per year through knowledge-base search automation, and reduced low-code app delivery time from six weeks to one.

  • Cost Reduction
  • Productivity Improvement
  • Scalability

Solution

MineSense built a lakehouse architecture on Oracle Cloud Infrastructure using Oracle Autonomous AI Lakehouse and Oracle Autonomous AI Database as the platform for sensor-driven mining analytics. High-resolution ore-grade data from rugged X-ray fluorescence sensors is centralized with OCI Object Storage and analyzed in real time, while Oracle APEX, Data Studio, and Oracle Select AI help teams load, transform, query, and operationalize the data. OCI generative AI agents and retrieval-augmented generation automate searches across the company's knowledge base, and Oracle Interconnect for Azure lets MineSense keep using Azure-based machine learning tools while storing governed data in Oracle's autonomous environment. Delta Share and Oracle REST Data Services share near-real-time mining data with customers across clouds. The result is a scalable AI and analytics stack that supports growth while reducing DBA effort, speeding support workflows, and accelerating low-code application delivery.

Data flow

Sensor data from mining equipment is ingested into Oracle Autonomous AI Lakehouse and OCI Object Storage, transformed and queried through Data Studio and Oracle Autonomous AI Database, shared with Azure-based ML tools through Oracle Interconnect for Azure, then exposed to engineers and customers via Oracle Select AI, Oracle REST Data Services, and low-code APEX applications.

Solution architecture

10 components · 5 layers
  1. Ingest
    • Oracle Data Studio Helps teams load and transform mining data inside the Oracle environment.
  2. Compute
    • Oracle Cloud Infrastructure (OCI) Provides the cloud foundation for MineSense's lakehouse and near-real-time industrial analytics workloads.
  3. Storage
    • Oracle Autonomous AI Lakehouse Acts as the scalable lakehouse layer that automates data integration across mining data formats and platforms.
    • Oracle Autonomous AI Database Provides the self-managing database foundation for governed storage, support workflows, and autonomous operations.
    • OCI Object Storage Stores lakehouse data used for large-scale mining analytics and AI processing.
  4. Serving
    • Oracle APEX Lets MineSense rapidly build low-code applications on top of the lakehouse and autonomous data platform.
    • Oracle Select AI Supports natural-language interactions with the data platform and simplifies support and monitoring workflows.
    • Oracle REST Data Services Publishes near-real-time operational data to customers through APIs.
  5. Orchestration
    • Oracle Interconnect for Azure Connects Azure-based machine learning tooling to data held in Oracle's autonomous platform.
    • OCI Functions Automates workflow steps around data processing and application delivery.

Architecture clues

  • Delta Share and Oracle REST Data Services are used for cross-cloud customer data sharing
  • Lakehouse uses OCI Object Storage plus Oracle autonomous services
  • Oracle Interconnect for Azure keeps governed data in Oracle while ML tooling runs in Azure

Evidence from the source

MineSense chose Oracle Cloud Infrastructure (OCI) with Oracle Autonomous AI Lakehouse to support its modern lakehouse architecture.
The company’s small IT team lowered DBA workloads by 65% while supporting rapid growth in both customers and data volumes, scaling to more than 55 TB data sets in four years.
improving MineSense field engineering productivity by 5X and saving hundreds of hours per year in development and maintenance time.
Transparency Evidence & methodology 100/100 reference value · 77% 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 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 confidence77%
  • 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 provenance0/10
    The publication date has no documented provenance.
  • 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. Oracle

    MineSense Technologies customer story

    First-party
    https://www.oracle.com/customers/minesense-autonomous-database/
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
  2. LinkedIn

    MineSense Technologies Ltd. | LinkedIn

    Independent secondary
    https://www.linkedin.com/company/minesense-technologies-ltd-/

    The Organization JSON-LD lists addressLocality 'Vancouver', and addressCountry 'CA', and numberOfEmployees value 252.

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

  • First archived Apr 9, 2025 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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