Industrial AI Analytics
TrendMiner
SF 8.5Time series search for process plants
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Industrial AI Analytics
Arundo Analytics
SF 7.9Industrial AI for asset analytics
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Quick decision guide
Choose based on your workflow
TrendMiner may fit better if...
- Pattern Search
- Root Cause
- Asset Dashboards
Arundo Analytics may fit better if...
- Model Deployment
- Edge Runtime
- Data Connectors
Overview
How each tool is described
TrendMiner
What is TrendMiner?
TrendMiner, a Proemion company, provides self-service industrial analytics software for process engineers, operations teams, and reliability specialists. It combines time-series and contextual event analysis, pattern search, monitoring, machine learning, visualization, reporting, and newer agentic-AI workflows so plant experts can investigate operational problems without relying on data scientists for routine analysis.
- Industrial analysis: Engineers can explore historical process data, search for similar operating periods, compare batches and events, calculate new signals, investigate correlations, and develop hypotheses about root causes. Findings can then be turned into dashboards, calculated tags, monitors, and alerts that continue watching for the same conditions in live operations.
- AI and predictive workflows: TrendMiner supports machine-learning and predictive use cases such as anomaly detection, forecasting, classifications, and asset or process-performance monitoring. Its 2026 Analytics Agent adds natural-language investigation across historian, asset, and event data, can perform multistep root-cause analysis, and can create reports, dashboards, and monitoring workflows. TrendMiner also supports MCP connectivity and model-agnostic integration with external AI environments.
- Industrial connectivity: The platform connects to systems including AVEVA PI, AspenTech IP.21, GE Proficy, Honeywell PHD, Yokogawa Exaquantum, AWS IoT SiteWise, Microsoft Azure Data Explorer, Microsoft Fabric, SQL databases, Databricks, Snowflake, and other industrial data environments. Buyers should assess historian and contextual-data compatibility, network architecture, data quality, security boundaries, and the connector requirements for each source.
- Deployment: TrendMiner is available as a managed SaaS service, an on-premises installation, or a self-hosted deployment in private-cloud infrastructure such as Microsoft Azure or AWS. SaaS is currently described by TrendMiner as its preferred deployment model, while self-hosted options provide greater infrastructure and data-sovereignty control but place more configuration and maintenance responsibility on the customer.
TrendMiner is most relevant to process-manufacturing, energy, and other industrial organizations that already collect substantial historian and operational data and want plant experts to analyze it directly. Its value depends on data accessibility, user adoption, deployment architecture, and whether faster investigations and continuous monitoring translate into measurable improvements in process performance, reliability, quality, or energy use.
Arundo Analytics
What is Arundo Analytics?
Arundo Analytics provides industrial AI software for asset-heavy and process-intensive sectors, combining operational data, time-series information, knowledge graphs, machine learning, and AI agents to support equipment monitoring, optimization, and operational decision-making. Its current portfolio combines the Arundo AI Foundation, AI Companion, cloud applications, and tailored industrial AI solutions.
- Industrial analytics: Arundo AI Foundation connects existing industrial data sources, represents assets, sensors, models, and documents through a knowledge graph, stores time-series data, and orchestrates analytical and machine-learning models. Foundation Studio also lets teams inspect data provenance and lineage and configure the industrial domain.
- AI assistance: AI Companion lets operations teams ask natural-language questions across sensor telemetry, maintenance records, equipment manuals, knowledge-graph relationships, and model outputs. Responses can include graphical and tabular results, while source review and trace-back show which underlying data contributed to an answer.
- Operational fit: ArundoEdge streams IoT sensor data from industrial control systems to the cloud, while EdgeManager handles deployment and monitoring of machine-learning models. Current specialized applications include Marathon for equipment monitoring and prescriptive maintenance, CarbonPath for maritime Scope 3 emissions management, and Energy Optimizer for optimizing industrial electricity consumption and cost.
- Commercial considerations: Arundo offers both cloud-delivered SaaS applications and tailored AI solutions built on its Foundation technology. Public list pricing is not provided, so buyers should evaluate the required applications, data integrations, deployment architecture, industrial modeling scope, and implementation services when assessing total cost and project complexity.
Arundo is particularly relevant to industrial organizations that need AI grounded in interconnected assets, sensor histories, engineering documents, and operational models rather than general-purpose business analytics or standalone generative AI.
Side-by-side
Key differences
Criteria
Industrial AI AnalyticsTrendMiner
Industrial AI AnalyticsArundo Analytics
Best for
Industrial AI Analytics
Industrial AI Analytics
Score
8.5/10
7.9/10
Pricing
Contact sales
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Category / audience
AI Maintenance, CMMS & Asset Reliability Software › Industrial AI Analytics
- industrial analytics
- process analytics
- trendminer
AI Maintenance, CMMS & Asset Reliability Software › Industrial AI Analytics
- industrial ai
- oil and gas
- machine learning operations
Feature check
Side-by-side feature check
Feature
TrendMiner
Arundo Analytics
Pattern SearchFinds similar operating periods across history
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Root CauseCompares signals during normal and abnormal operations
-
Asset DashboardsLive views of critical process asset behavior
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Historian ConnectorsReads PI Aspen and other historian sources
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Self ServiceBuilt for process engineers not data scientists
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Process ContextAdds asset context to historian time series
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12 capabilities compared.12 differentiating rows are shown first.
Use cases
Who they're built for
TrendMiner
- Search historian patterns quickly across yearsFind similar operating periods across long historian records
- Investigate abnormal process behavior across plantsCompare normal and abnormal periods to find root causes
- Build asset monitoring dashboards for engineersCreate live views of critical process equipment behavior
Arundo Analytics
- Deploy machine learning models into productionMove models from notebooks into reliable production environments
- Run edge analytics close to assetsProcess sensor data near assets for low latency
- Operate offshore asset analytics across fleetsApply analytics to oil rigs and shipping fleets
The trade-offs
Pros & cons of each tool
Trade-offs
TrendMiner
Pros
- Strong pattern search across long historian records
- Built specifically for process engineers not data scientists
- Strong root cause analysis on historian data
Cons
- Best value for plants with rich historians
- Adoption depends on engineer training and champions
- Ownership has changed across recent years already
Trade-offs
Arundo Analytics
Pros
- Helps move models into reliable production environments
- Edge runtime suits distributed industrial asset deployments
- Strong focus on real industrial use cases
Cons
- Targeted at organizations with internal data teams
- Less suitable as a standalone packaged product
- Smaller market footprint than large industrial platforms
Final verdict
Best fit depends on your workflow
Current catalog data shows meaningful overlap between TrendMiner and Arundo Analytics. Use the signals below to decide based on workflow, ecosystem, pricing, and implementation fit.
Differentiators available
TrendMiner has 4 visible decision signals and Arundo Analytics has 4.
Score signal
TrendMiner has the higher SoftFinders Score in the current catalog data.
Choose TrendMiner if…
- Search historian patterns quickly across years
- Investigate abnormal process behavior across plants
- Build asset monitoring dashboards for engineers
- Pattern Search
Choose Arundo Analytics if…
- Deploy machine learning models into production
- Run edge analytics close to assets
- Operate offshore asset analytics across fleets
- Model Deployment
TRY THEM YOURSELF
See which one fits your workflow
Both tools have their strengths, the best way to decide is to spend a few minutes inside each.