Battle Cards

Process Intelligence Vendor Comparison

A factual overview of leading process mining and process intelligence platforms – and how Noreja’s causal approach differs methodically. The decisive distinction lies not in connectors or dashboards, but in the analysis paradigm: frequency-based Directly-Follows models versus the reconstruction of genuine cause-and-effect relationships.

Causal Process Intelligence

Noreja

Cause-and-effect analysis on an Event Knowledge Graph instead of frequency-based Directly-Follows models.

Noreja situates process behavior within a semantic model of business objects, domain knowledge, and business rules. Instead of inferring relationships from the sheer frequency of activity sequences, the platform distinguishes causally whether a deviation is a legitimate process path or an error pattern – making AI recommendations traceable.

Event Knowledge Graph instead of flat logs

Events, objects, and their relationships are stored as a graph. This removes the need to reduce multidimensional processes to a single case ID – the core source of distortion in case-centric methods.

From frequency to causality

Directly-follows frequencies evidence temporal proximity, not causation. Noreja’s causal model separates genuine triggers from coincidence and avoids the false causal assumptions of frequency-based process graphs.

Semantics & Process Frontier Agents

Business logic and domain knowledge are part of the model. Deviations are classified in business terms as rework, error, or batching effect – the foundation for Process Frontier Agents that continuously surface optimization and automation potential.

Transparent pricing

Noreja is the only solution in this comparison with publicly available pricing – and therefore likely more cost-effective than all the listed alternatives that only quote “price on request”.

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Comparison Matrix

These attributes condense the methodical differences. “Analysis paradigm” denotes the type of model construction, “causal analysis” the maturity of genuine cause-and-effect reconstruction, and “enterprise AI capability” the suitability of the data model as a context and knowledge base for enterprise-specific AI. The rating logic for the last column is disclosed below the table.

Platform	Analysis paradigm	Data model	Ecosystem lock-in	Causal analysis	Enterprise AI capability
Noreja	Causal + Temporal	Event Knowledge Graph	Low	Native	HighA central Event Knowledge Graph connects events, objects, relationships, temporal references, and enterprise knowledge in one model and can be incrementally extended with further processes, documents, and organizational units.
Celonis	Frequency-based	Object-centric event log	Medium	No	MediumObject-centric logs capture multiple object types and thus provide usable context, but remain extracted tables without an end-to-end knowledge graph: relationships, temporal references, and enterprise knowledge are not modeled jointly and cannot be incrementally extended with documents or organizational units.
SAP Signavio	Frequency-based	Case-centric event log	High	No	LowThe case-centric event log reduces multidimensional flows to a single case notion; object relationships and temporal references are lost, so no extensible knowledge base for enterprise-specific AI emerges.
UiPath Process Mining	Frequency-based	Event log + task mining	Medium	No	LowEvent and task logs do add desktop-level signals, but remain case- and session-scoped recordings: they are not linked into a shared graph of objects, relationships, and enterprise knowledge.
IBM Process Mining	Hybrid	Object-centric event log	High	Partial	MediumOCPM overcomes the single-case view and therefore carries more context than case-centric logs, yet remains a log format: events, objects, and relationships are not maintained as a persistent knowledge graph extensible with documents and organizational knowledge.
Microsoft Process Mining	Frequency-based	Case-centric event log	High	No	LowThe case-centric log view depicts sequences within a single case notion; language models therefore access flat sequence data rather than a connected knowledge graph of objects, relationships, and domain knowledge.
ServiceNow Process Mining	Frequency-based	Platform-native logs	High	No	LowPlatform-native workflow logs stop at the system boundary: they cover the service processes of one application and cannot be grown into an enterprise-wide graph spanning further processes, documents, and organizational units.
ABBYY Timeline	Frequency-based	Event log + document context	Medium	Partial	LowDocument context is a genuine plus, but it is attached to case-scoped logs rather than linked in a graph of objects, relationships, and temporal references – leaving the knowledge base document-centric rather than extensible enterprise-wide.
Appian	Frequency-based	Workflow logs	High	No	LowWorkflow logs record the execution of orchestrated flows inside the platform; they contain no semantic model of objects, relationships, and enterprise knowledge on which enterprise-specific AI could build.
ARIS Process Mining	Frequency-based	Case-centric event log	High	Partial	LowThe governed to-be model repository provides valuable process knowledge, yet the analysis itself runs on case-centric logs: model and event data remain separate rather than joined in one shared, extensible knowledge graph.
mpmX (MEHRWERK)	Frequency-based	Data-platform-native model (Qlik/Snowflake/Databricks), OCPM	High	No	MediumWith object-centric process mining in its standard scope, mpmX captures multiple object types and thus provides usable context; however, the model lives on the respective data platform and is not maintained as a dedicated knowledge graph extensible with documents and enterprise knowledge.

Rating logic: enterprise AI capability (graph-centric)

This column assesses only how well the respective data model serves as a context and knowledge base for enterprise-specific AI. The benchmark is a central Event Knowledge Graph that connects events, objects, relationships, temporal references, and enterprise knowledge, and can be incrementally extended with further processes, documents, and organizational units.

High: Central Event Knowledge Graph with the properties above – events, objects, relationships, temporal references, and enterprise knowledge in one extensible model.
Medium: Object-centric event log: multiple object types per event, but no end-to-end, persistent knowledge graph.
Low: Case-centric event log or workflow logs: reduced to a single case notion or to the platform boundary.

Transparency note: the benchmark is deliberately graph-centric and therefore structurally favors architectures with a knowledge graph – including Noreja. It rates only the suitability of the data model as a knowledge base, not the feature scope, market maturity, or the AI capabilities of the products themselves. Data models that do not match any of the three categories exactly (such as relational in-memory models) are classified by structural proximity.

The Battle Cards in Detail

Each card neutrally summarizes orientation, strengths, and methodical reach, and states the distinction from the causal approach.

Market pioneer · ERP event log

Celonis

Celonis substantially shaped the process mining market and industrialized the extraction of event logs from transactional systems early on. The platform is broadly established in the enterprise space and covers discovery, conformance, and reporting comprehensively.

Strengths

Largest selection of pre-built interfaces and connectors on the market
Mature, extensive training and enablement programs
Workflow triggers and Orchestration Engine for automated follow-up actions
Extensive library of standard KPIs and process apps
Process Intelligence Graph and AgentC connectivity to leading agent platforms (incl. Copilot Studio, Bedrock, Agentforce)

Methodical context

Model construction relies on directly-follows frequencies and reflects sequence, not causation
Labor-intensive data extraction and preparation before the first reliable analysis
First reliable discovery results often only after a multi-month rollout

Distinction from Noreja

Noreja complements frequency-based analysis with a genuine causal and temporal model on an Event Knowledge Graph, uncovering cause-and-effect relationships rather than mere frequencies.

Additional Noreja advantages

Focused adoption instead of platform transformation: Noreja starts process by process directly on relational source tables and delivers first causal analyses without requiring an enterprise-wide platform rollout – existing BI, workflow, and automation systems remain in the lead.
AI context with free model choice: Noreja links process data, business rules, and SOPs directly in the Event Knowledge Graph and supports your own or privately hosted LLMs up to on-premises operation – rather than primarily handing AI context to external agent platforms.

SAP ecosystem · BPM suite

SAP Signavio

Signavio combines process mining with process modeling and business process management inside the SAP Business Technology Platform. For organizations with an SAP-centric landscape, the native connectivity noticeably reduces integration effort.

Strengths

Deep, largely pre-configured connectivity to SAP source systems
Large adjacent product family for modeling and process governance
End-to-end suite from modeling through mining to conformance
Industry benchmarks for contextualizing your own metrics

Methodical context

Full value unfolds primarily within SAP-dominated landscapes
Analysis follows a classic, case-centric process perspective
Heterogeneous non-SAP sources require additional integration effort

Distinction from Noreja

Noreja is source-system agnostic and models cross-cutting object relationships in a graph rather than tying analysis to a single ERP ecosystem.

Additional Noreja advantages

Independent of an S/4HANA transformation: Noreja’s value does not hinge on an ERP migration roadmap – analysis starts mining-first on existing operational data, with no upstream process repository or modeling initiative required.
SAP and non-SAP data in one model: existing primary- and foreign-key relationships of the source systems are used directly to connect business objects across SAP boundaries in one Event Knowledge Graph.

RPA-driven discovery

UiPath Process Mining

UiPath integrates process mining, task mining, and communications mining into an automation-centric platform. Insights feed directly into building and operating software robots in a closed loop.

Strengths

Seamless transition from analysis to RPA implementation
Combined view of system, desktop, and communication data
Integrated governance and ROI tracking for automation
Agentic orchestration (Maestro) coordinates agents, robots, and people in BPMN processes

Methodical context

Discovery is primarily geared toward finding automation candidates
Analytical depth of causal root-cause analysis is subordinate to automation
Full value emerges in conjunction with the UiPath automation platform

Distinction from Noreja

Noreja puts causal diagnosis first: only a semantic understanding of causes determines which steps should be automated at all – independent of any platform.

Additional Noreja advantages

Recognize business-critical exceptions instead of automating them: rules, SOPs, and domain knowledge in the graph distinguish legitimate process variants from error patterns – before automation candidates are prioritized.
Free choice of execution layer: insights and prioritizations feed into your existing RPA, workflow, and agent systems rather than coupling analysis and automation to one platform ecosystem.

Regulated · hybrid cloud · OCPM

IBM Process Mining

IBM Process Mining is part of Cloud Pak for Business Automation and supports object-centric process mining (OCPM) as well as predictive and prescriptive analytics. Hybrid-cloud capability addresses strict data-residency and compliance requirements.

Strengths

Object-centric analysis overcomes the rigid single-case view
Data-driven what-if simulation and prescriptive recommendations
On-premise and hybrid operation for regulated industries
LLM-powered Process Mining Assistant (watsonx) for root-cause hypotheses

Methodical context

Object-centric, yet methodically still frequency-based rather than causal
Full value usually bundled with the IBM automation suite
Rollout and operation require substantial implementation effort

Distinction from Noreja

Noreja goes beyond the object-centric frequency view and models the causal relationships between events in a graph – not merely their assignment to multiple objects.

Additional Noreja advantages

Traceable causal assumptions: the relationships underlying analyses and AI recommendations are explicitly visible and verifiable in the Event Knowledge Graph – rather than remaining hidden in predictive models or assistant answers.
Focused mid-market scope: entry via a scoped proof-of-value with transparent, publicly available packages – without adopting a comprehensive enterprise automation suite.

Power Platform · low-code

Microsoft Process Mining

As part of Power Automate, Microsoft Process Mining brings process analysis into the familiar Power Platform. The low-code approach lowers the entry barrier and connects discovery, RPA, and document processing in one continuous environment.

Strengths

Tight integration with Azure, Power BI, and Microsoft 365
Low entry barrier through low-code and familiar interfaces
Conversational queries via integrated language models

Methodical context

Oriented toward accessibility rather than analytical depth
Full value presupposes a Microsoft-centric landscape
Process models remain tied to frequency-based logic
Capacity- and quota-based licensing (storage per user license, tenant cap, add-ons)

Distinction from Noreja

Noreja addresses analytical depth: causal modeling and semantic context instead of broad but frequency-based accessibility within a closed ecosystem.

Additional Noreja advantages

Cross-system relational analysis: Noreja connects relational sources beyond Dataverse directly and links them in one Event Knowledge Graph – without the storage quotas and capacity add-ons of Power Platform licensing.
Free LLM choice instead of Copilot dependence: AI features run with your own, privately hosted, or on-premises models – while the semantic process context stays in your own graph.

ITSM-native · service processes

ServiceNow Process Mining

ServiceNow integrates process mining directly into its own platform and targets IT service management workflows. For existing ServiceNow customers, separate data extraction is unnecessary, and insights flow straight into operational dashboards.

Strengths

No external data extraction needed for ServiceNow processes
Real-time KPI monitoring for SLA compliance
Guided setup with fast time-to-insight in the ITSM context

Methodical context

Focus lies on service-management workflows within the platform
External data is surfaced via Workflow Data Fabric primarily for workflows and agents – mining analysis stays focused on platform processes
Analysis remains confined to the frequency-based process view

Distinction from Noreja

Noreja analyzes end-to-end processes across system boundaries in a graph, rather than being confined to the service processes of a single platform.

Additional Noreja advantages

ServiceNow in business context: Noreja links ServiceNow data with ERP, finance, and production data into continuous business objects – tickets become causally analyzable in the context of upstream and downstream backend processes.
One graph, many process perspectives: object and event relationships modeled once are reused beyond ITSM for further processes and analyses instead of being rebuilt per use case.

Document-centric · task mining

ABBYY Timeline

ABBYY Timeline combines task and process mining with ABBYY’s document-processing heritage. Through pattern recognition, the platform surfaces context from documents and emails and suits document-intensive workflows.

Strengths

Strong extraction of unstructured document and email context
Combination of task mining and process analysis
Root-cause and compliance evaluations for regulated workflows
AI-supported process prediction and simulation based on historical execution patterns

Methodical context

Strengths primarily in document-centric use cases
No graph-based, causal process model at its core
Cross-cutting object relationships are not modeled semantically

Distinction from Noreja

Noreja models processes as a semantic graph with causal relationships between events and is therefore not tailored to document-driven workflows.

Additional Noreja advantages

Structured process data at the core, documents as context: Noreja builds the analysis on the relational transaction data of the source systems and links SOPs, policies, and documents as an additional knowledge layer in the graph – rather than the other way around.
Multiple relationship types instead of a primarily temporal timeline: the Event Knowledge Graph distinguishes temporal, causal, and contextual relationships, enabling business interpretation where a timeline analysis reaches its limits.

Low-code · workflow orchestration

Appian

With Process HQ, Appian positions process mining as part of a low-code platform for workflow automation and case management. Process HQ bundles data fabric, mining, machine learning, and generative AI (AI Copilot); insights can be translated directly into orchestrated workflows within the same environment.

Strengths

Short path from insight to orchestrated implementation
Low-code development for rapid process changes
Orchestration of complex workflows across multiple systems
Process HQ combines data fabric, mining, and AI Copilot in one guided analysis environment

Methodical context

Mining is an add-on capability, not the platform’s methodical core
Analytical depth of process diagnosis is subordinate to orchestration
No causal, graph-based process model

Distinction from Noreja

Noreja specializes in causal process diagnosis – the reliable foundation before workflows are orchestrated or automated.

Additional Noreja advantages

Analysis without migrating process execution: Noreja examines the flows inside existing systems, with no need to move workflows or cases to a new execution platform.
A focused analysis and context layer: existing workflow and case-management solutions are complemented with causal diagnosis and semantic process context – not replaced.

BPM heritage · model conformance

ARIS Process Mining

ARIS combines its long-standing BPM and modeling heritage with process mining and AI in one environment. Its particular value lies in comparing observed as-is flows against governed to-be models in the ARIS repository – a continuous loop of analysis, documentation, and conformance.

Strengths

Tight coupling of process mining with the governed ARIS model repository
Large adjacent product family for modeling and process administration
Automated conformance against to-be models (BPMN, EPC)
AI-supported root-cause miner correlating delay factors
Process Core as a governed digital twin of processes, roles, rules, and controls – also serving as guardrails for AI agents

Methodical context

Full value emerges in conjunction with the ARIS BPM suite
Root-cause analysis remains correlative and frequency-based rather than causal
Analysis follows a case-centric, model-centric process view

Distinction from Noreja

Noreja uncovers causal cause-and-effect relationships in a graph rather than weighting correlating factors against a stored to-be model – and requires no upstream model repository.

Additional Noreja advantages

Data-first instead of repository-first: Noreja starts directly on the as-is data of the source systems – a maintained to-be model repository is not a prerequisite for getting started and can be added later as context.
Open AI and data-science integration: via the integrated Workbench (Jupyter/Python), your own models and analyses work directly on the Event Knowledge Graph – with free choice of LLMs up to on-premises operation.

Data-platform-native · self-service

mpmX (MEHRWERK)

mpmX from German vendor MEHRWERK brings process mining directly onto existing data platforms: data preparation, storage, and mining run natively on Qlik, Snowflake, or Databricks. Object-centric process mining (OCPM) is part of the standard scope, and the self-service approach enables data-savvy teams to start quickly.

Strengths

Reliably resolves complex n:m relationships between process objects
Data-platform-native: preparation and mining directly on Qlik, Snowflake, or Databricks
Object-centric process mining (OCPM) as part of the standard scope
Fast onboarding and self-service for data-savvy departments

Methodical context

Analytical strength is tied to the foundation of the respective data platform
Despite OCPM, process analysis remains frequency-based without causal modeling
No graph-based, semantic causal model at its core

Distinction from Noreja

Noreja relies on a dedicated, graph-based causal and temporal model instead of a frequency analysis built on top of BI and data platforms – thereby separating genuine causes from statistical coincidence.

Additional Noreja advantages

Explicit semantics of business relationship types: the Event Knowledge Graph distinguishes temporal, causal, and contextual paths and links rules, SOPs, and documents with the process data – beyond resolving object relationships.
A knowledge base independent of the data platform: analysis and context model live in a dedicated graph rather than in apps of a BI or data-platform engine – so the usage layer is not tied to Qlik, Snowflake, or Databricks.

How to Recognize a Future-Proof Solution

Analysis paradigm

Does the tool only depict sequences (directly-follows) or reconstruct actual cause-and-effect relationships? Only causal models avoid false conclusions from mere temporal proximity.

Data representation

Are multidimensional processes reduced to a single case ID, or represented as a graph of events, objects, and relationships? The representation determines how realistic the analysis can be.

Semantic context

Do domain knowledge and business rules enter the model? Only business context separates legitimate process paths from error patterns and makes AI recommendations traceable.

Ecosystem independence

Is the analysis bound to a specific ERP, RPA, or platform stack, or is it source-system agnostic? Independence secures a continuous end-to-end view.

Experience the Difference Between Frequency and Causality

See how Noreja analyzes processes causally on an Event Knowledge Graph – beyond frequency-based Directly-Follows models.

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