OPERATIONAL INSIGHTS
Process Mining
Process Mining
Process Mining
Otonomii’s process mining methodology follows a five-step cycle: Connect, Understand, Analyze, Act, Automate. Each step builds on the previous one and the cycle repeats continuously as processes evolve.
This is not a consulting engagement with a final report. It is a perpetual optimization loop that adapts as your operations change.
Built-in connectors for the systems where your processes live, such as ERP platforms, CRM systems, databases, cloud warehouses and custom APIs. Connectors handle authentication, schema discovery, incremental data extraction and change data capture. No manual ETL pipelines, no data engineering prerequisite.
Mine processes from event data to discover what actually happens, not what the documentation says should happen. Visualize process flows as directed graphs with frequency and timing on every edge. Identify process variants and quantify how often each occurs, how long it takes and where it deviates from the ideal.
Query related objects to understand why processes behave the way they do. Diagnose bottlenecks by tracing delays to root causes: resource constraints, handoff problems, policy issues or system limitations. Analysis uses prediction error analysis and causal reasoning to distinguish correlation from causation.
Write back to external systems to fix issues at their source. Insights translate into concrete changes: updating workflow configurations, adjusting resource allocations, modifying routing rules and escalating stuck cases. Every action is logged with a full audit trail.
Custom triggers, threshold monitoring and milestone actions turn reactive process management into proactive optimization. Escalate cases that exceed SLA thresholds, reroute work when bottlenecks are detected and notify stakeholders when milestones are reached or missed.
Automatic data transformations eliminate the weeks of preparation that traditionally precede process mining initiatives. The platform introspects connected data sources, identifies event sequences and constructs process models without manual mapping. Interactive visualizations render within hours of connecting a data source.
The Brain-Mind architecture is applied to process data, discovering patterns traditional process mining tools miss. The Brain stores observed variants with full context. The Mind builds predictive models of process behavior and flags deviations as prediction errors.
API integration and write-back capabilities ensure insights translate into changes. Every finding includes a recommended action such as re-route cases, add resources, change business rules, update CRM fields, create tickets or send notifications.
Business rules and ML prediction combine to identify issues before they impact outcomes. Multiple monitoring spokes watch throughput, cycle time, variant distribution, resource utilization and error rates while the hub detects agreement, conflict or silence.
Webhooks, alerts and cross-system triggers break down the silos that fragment process visibility. A process spanning ERP, CRM and custom applications appears as a single unified flow, with events in one system triggering actions in another.
Automatic data transformations eliminate the weeks of data preparation that traditionally precede process mining initiatives. The platform introspects connected data sources, identifies event sequences and constructs process models without manual mapping.
Data transformation uses the Brain layer’s pattern recognition to automatically identify case IDs, activity names, timestamps and resources from raw data. Even when column names are non-standard or data is spread across multiple tables, the system infers the process structure from the data itself.
The Brain-Mind architecture is applied to process data, discovering patterns that traditional process mining tools miss. The Brain stores all observed process variants with their full context.
The Mind builds predictive models of process behavior, generating expectations about how cases should flow and flagging deviations as prediction errors. This indicator identifies not just slow processes, but structurally broken ones.
ML agnostic forecasting means the system is not tied to any specific model architecture. It selects the appropriate technique based on the data characteristics and continuously evaluates whether the current model is outperformed by alternatives.
API integration and write-back capabilities ensure that insights translate into changes. Every process mining finding includes a recommended action: reroute cases through a faster path, add resources at a bottleneck or change a business rule that causes unnecessary loops.
Write-back uses the same connector framework as data extraction. The platform can modify records in ERP systems, update CRM fields, trigger workflow actions, create tickets and send notifications.
Business rules and ML prediction combine to identify issues before they impact outcomes. Business rules define known thresholds, while ML prediction identifies emerging issues that no business rule covers.
The monitoring system uses the Hub and Spoke architecture. Multiple monitoring spokes watch throughput, cycle time, variant distribution, resource utilization and error rates. The convergence hub detects agreement, conflict or silence.
Webhooks, alerts and cross-system triggers break down the silos that fragment process visibility. A process that spans ERP, CRM and custom applications appears as a single, unified flow.
Orchestration uses event-driven architecture with guaranteed delivery. Events are published to a central bus and subscribers in different systems react independently. This decoupling means adding or removing systems does not require changes to existing integrations.
Process mining was applied to a financial institution’s transaction execution pipeline / execution workflow spanning order management, risk checks, market connectivity and settlement. The platform discovered 47 distinct process variants where the documented process described 3. The primary bottleneck was not execution latency, but a risk check handoff that introduced variable delays depending on instrument class.
A risk management division deployed Otonomii process mining on their assessment pipeline within two weeks, from initial data connection to actionable findings. The platform identified 12 manual steps that added no value to the assessment but consumed 35% of total cycle time.
Standard HTTP endpoints for all platform operations. Create process models, query process instances, trigger actions, and retrieve analytics. OpenAPI 3.1 specification with versioned endpoints, rate limiting, authentication and pagination.
Event-driven notifications for process state changes. Configure subscriptions for case completion, SLA breach, bottleneck detection and anomaly alerts. Guaranteed delivery with exponential backoff retry.
Scheduled data exchange for high-volume, latency-tolerant workloads. Import process data in bulk, export analytics results, and synchronize reference data. Supports CSV, JSON, Parquet and Avro.
Real-time, continuous data flows for low-latency process monitoring. Native integration with Kafka, Kinesis and Pulsar. Server-sent events for web clients and backpressure handling under load.
Otonomii’s process mining methodology follows a five-step cycle: Connect, Understand, Analyze, Act, Automate. Each step builds on the previous one and the cycle repeats continuously as processes evolve.
This is not a consulting engagement with a final report. It is a perpetual optimization loop that adapts as your operations change.
Built-in connectors for the systems where your processes live, such as ERP platforms, CRM systems, databases, cloud warehouses and custom APIs. Connectors handle authentication, schema discovery, incremental data extraction and change data capture. No manual ETL pipelines, no data engineering prerequisite.
Mine processes from event data to discover what actually happens, not what the documentation says should happen. Visualize process flows as directed graphs with frequency and timing on every edge. Identify process variants and quantify how often each occurs, how long it takes and where it deviates from the ideal.
Query related objects to understand why processes behave the way they do. Diagnose bottlenecks by tracing delays to root causes: resource constraints, handoff problems, policy issues or system limitations. Analysis uses prediction error analysis and causal reasoning to distinguish correlation from causation.
Write back to external systems to fix issues at their source. Insights translate into concrete changes: updating workflow configurations, adjusting resource allocations, modifying routing rules and escalating stuck cases. Every action is logged with a full audit trail.
Custom triggers, threshold monitoring and milestone actions turn reactive process management into proactive optimization. Escalate cases that exceed SLA thresholds, reroute work when bottlenecks are detected and notify stakeholders when milestones are reached or missed.
Automatic data transformations eliminate the weeks of preparation that traditionally precede process mining initiatives. The platform introspects connected data sources, identifies event sequences and constructs process models without manual mapping. Interactive visualizations render within hours of connecting a data source.
The Brain-Mind architecture is applied to process data, discovering patterns traditional process mining tools miss. The Brain stores observed variants with full context. The Mind builds predictive models of process behavior and flags deviations as prediction errors.
API integration and write-back capabilities ensure insights translate into changes. Every finding includes a recommended action such as re-route cases, add resources, change business rules, update CRM fields, create tickets or send notifications.
Business rules and ML prediction combine to identify issues before they impact outcomes. Multiple monitoring spokes watch throughput, cycle time, variant distribution, resource utilization and error rates while the hub detects agreement, conflict or silence.
Webhooks, alerts and cross-system triggers break down the silos that fragment process visibility. A process spanning ERP, CRM and custom applications appears as a single unified flow, with events in one system triggering actions in another.
Automatic data transformations eliminate the weeks of data preparation that traditionally precede process mining initiatives. The platform introspects connected data sources, identifies event sequences and constructs process models without manual mapping.
Data transformation uses the Brain layer’s pattern recognition to automatically identify case IDs, activity names, timestamps and resources from raw data. Even when column names are non-standard or data is spread across multiple tables, the system infers the process structure from the data itself.
The Brain-Mind architecture is applied to process data, discovering patterns that traditional process mining tools miss. The Brain stores all observed process variants with their full context.
The Mind builds predictive models of process behavior, generating expectations about how cases should flow and flagging deviations as prediction errors. This indicator identifies not just slow processes, but structurally broken ones.
ML agnostic forecasting means the system is not tied to any specific model architecture. It selects the appropriate technique based on the data characteristics and continuously evaluates whether the current model is outperformed by alternatives.
API integration and write-back capabilities ensure that insights translate into changes. Every process mining finding includes a recommended action: reroute cases through a faster path, add resources at a bottleneck or change a business rule that causes unnecessary loops.
Write-back uses the same connector framework as data extraction. The platform can modify records in ERP systems, update CRM fields, trigger workflow actions, create tickets and send notifications.
Business rules and ML prediction combine to identify issues before they impact outcomes. Business rules define known thresholds, while ML prediction identifies emerging issues that no business rule covers.
The monitoring system uses the Hub and Spoke architecture. Multiple monitoring spokes watch throughput, cycle time, variant distribution, resource utilization and error rates. The convergence hub detects agreement, conflict or silence.
Webhooks, alerts and cross-system triggers break down the silos that fragment process visibility. A process that spans ERP, CRM and custom applications appears as a single, unified flow.
Orchestration uses event-driven architecture with guaranteed delivery. Events are published to a central bus and subscribers in different systems react independently. This decoupling means adding or removing systems does not require changes to existing integrations.
Process mining was applied to a financial institution’s transaction execution pipeline / execution workflow spanning order management, risk checks, market connectivity and settlement. The platform discovered 47 distinct process variants where the documented process described 3. The primary bottleneck was not execution latency, but a risk check handoff that introduced variable delays depending on instrument class.
A risk management division deployed Otonomii process mining on their assessment pipeline within two weeks, from initial data connection to actionable findings. The platform identified 12 manual steps that added no value to the assessment but consumed 35% of total cycle time.
Standard HTTP endpoints for all platform operations. Create process models, query process instances, trigger actions, and retrieve analytics. OpenAPI 3.1 specification with versioned endpoints, rate limiting, authentication and pagination.
Event-driven notifications for process state changes. Configure subscriptions for case completion, SLA breach, bottleneck detection and anomaly alerts. Guaranteed delivery with exponential backoff retry.
Scheduled data exchange for high-volume, latency-tolerant workloads. Import process data in bulk, export analytics results, and synchronize reference data. Supports CSV, JSON, Parquet and Avro.
Real-time, continuous data flows for low-latency process monitoring. Native integration with Kafka, Kinesis and Pulsar. Server-sent events for web clients and backpressure handling under load.
2026 © Otonomii LTD. All rights reserved.
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2026 © Otonomii LTD. All rights reserved.
TOP
2026 © Otonomii LTD. All rights reserved.
TOP