Industrial AI Analytics
Seeq Analytics
SF 8.7Self service analytics for process data
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Industrial AI Analytics
TrendMiner
SF 8.5Time series search for process plants
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Quick decision guide
Choose based on your workflow
Seeq Analytics may fit better if...
- Calculation Engine
- Operational Dashboards
- Engineer Collaboration
TrendMiner may fit better if...
- Pattern Search
- Root Cause
- Asset Dashboards
Overview
How each tool is described
Seeq Analytics
What is Seeq Analytics?
Seeq is an industrial analytics, AI, and enterprise monitoring platform built for engineers, operations teams, and data scientists working with time-series process data. Its browser-based applications include Workbench for analysis, Organizer for reporting, Data Lab for Python workflows, and Vantage for monitoring large numbers of assets and operational events.
- Industrial analytics: Seeq Workbench provides no-code and low-code tools for cleansing data, defining operating periods, performing calculations, modeling behavior, making predictions, and investigating equipment or process performance. Its Formula language adds more advanced transformations and calculations when point-and-click tools are not sufficient.
- Reporting and enterprise monitoring: Organizer turns Workbench analyses into interactive dashboards and reports that can update with current data manually or on a schedule. Vantage provides a separate enterprise-monitoring workflow that aggregates conditions and events across large numbers of sensors and assets, helping teams triage and investigate predictive-maintenance issues, downtime, alarms, and other operational exceptions. Vantage requires additional licensing.
- Industrial data connectivity: Seeq connects directly to historians, SQL databases, data lakes, and other industrial data systems through supported connectors and Remote Agents. Connected source systems remain authoritative: Seeq retrieves and caches their data for calculation and visualization rather than replacing the historian or creating a new system of record. Buyers should therefore evaluate connector availability, network access, source-system performance, latency, and data quality.
- AI and Python workflows: Seeq AI Assistant includes agents for tasks such as writing formulas, performing analyses, creating Seeq content, assisting with reports, and automating repeatable multi-step workflows. Data Lab provides a JupyterLab environment with Python, the Seeq/Python library, machine-learning libraries, scheduled jobs, and custom analytics. AI availability is deployment- and license-dependent: the current AI Assistant is offered to eligible SaaS customers, while capabilities such as Agent Q and custom Agent Builder workflows require Seeq Intelligence licensing.
Seeq is most relevant to industrial organizations that already hold substantial process and asset data in historians or other operational systems and want engineers to analyze that data without repeatedly exporting it into spreadsheets or separate analytics silos. Its value depends on data connectivity, engineering adoption, monitoring scale, licensing requirements, and whether faster analysis and exception-based monitoring can materially improve operational decisions across assets and plants.
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.
Side-by-side
Key differences
Criteria
Industrial AI AnalyticsSeeq Analytics
Industrial AI AnalyticsTrendMiner
Best for
Industrial AI Analytics
Industrial AI Analytics
Score
8.7/10
8.5/10
Pricing
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Category / audience
AI Maintenance, CMMS & Asset Reliability Software › Industrial AI Analytics
- seeq
- process analytics
- time series analytics
AI Maintenance, CMMS & Asset Reliability Software › Industrial AI Analytics
- industrial analytics
- process analytics
- trendminer
OVERLAP
Where Seeq Analytics and TrendMiner are similar
Both tools cover similar catalog signals. The deciding factor is usually workflow fit, implementation needs, and ecosystem fit.
1 capabilities1 workflows
Shared capabilities
Capability overlap
Shared workflows
Workflow overlap
Feature check
Side-by-side feature check
Feature
Seeq Analytics
TrendMiner
Time Series SearchFinds patterns across historian data quickly
-
Calculation EngineBuilds engineered signals on top of data
-
Operational DashboardsOperational views and asset performance reports
-
Engineer CollaborationShared analyses across reliability and process teams
-
Cloud DeploymentRuns in major cloud providers globally
-
Pattern SearchFinds similar operating periods across history
-
11 capabilities compared.10 differentiating rows are shown first.
Use cases
Who they're built for
Seeq Analytics
- Investigate process plant events and anomaliesSearch historian data to understand specific abnormal events
- Build asset performance dashboards for engineersCreate live views of critical process equipment behavior
- Support reliability engineering analysis and decisionsHelp reliability teams analyze recurring failures and patterns
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
The trade-offs
Pros & cons of each tool
Trade-offs
Seeq Analytics
Pros
- Powerful time series analytics for process engineers
- Connects to many industrial historians and sources
- Encourages collaboration across reliability and process teams
Cons
- Needs strong historian data and engineering capacity
- Less suited for plants without process data history
- Pricing scales with users and connected sources
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
Final verdict
Best fit depends on your workflow
Current catalog data shows meaningful overlap between Seeq Analytics and TrendMiner. Use the signals below to decide based on workflow, ecosystem, pricing, and implementation fit.
Shared catalog overlap
Seeq Analytics and TrendMiner share 2 catalog signals, so the decision should focus on fit rather than broad capability alone.
Differentiators available
Seeq Analytics has 4 visible decision signals and TrendMiner has 4.
Score signal
Seeq Analytics has the higher SoftFinders Score in the current catalog data.
Choose Seeq Analytics if…
- Investigate process plant events and anomalies
- Build asset performance dashboards for engineers
- Support reliability engineering analysis and decisions
- Calculation Engine
Choose TrendMiner if…
- Search historian patterns quickly across years
- Investigate abnormal process behavior across plants
- Build asset monitoring dashboards for engineers
- Pattern Search
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.