AI Process Mining

AI Process Mining for Business Optimization

We build AI-powered process mining solutions that analyze your business operations, discover hidden inefficiencies, and recommend data-driven improvements.

Understanding Your Processes Through Data

Every business process generates digital footprints in the form of event logs, timestamps, status changes, and transaction records. Process mining analyzes these footprints to reveal how processes actually execute, as opposed to how they are documented or assumed to work. The gap between intended and actual process execution is often substantial, and understanding this gap is the first step toward meaningful improvement.

Traditional process analysis relies on interviews, workshops, and manual observation, which are time-consuming and subject to bias. AI-powered process mining analyzes millions of process instances automatically, discovering patterns, variations, and bottlenecks that manual analysis would miss. The result is an objective, data-driven view of your operations.

Arthiq builds process mining solutions that combine classical process mining algorithms with AI capabilities for pattern recognition, anomaly detection, and predictive analysis. We deliver insights that go beyond descriptive analytics to prescriptive recommendations for process improvement.

Process Discovery and Visualization

Our process mining solutions start with automated process discovery. By analyzing event logs from your systems, we reconstruct process flows, identify the most common execution paths, and highlight deviations and exceptions. The discovered processes are visualized as interactive process maps that show flow frequencies, timing distributions, and exception points.

Process variants are identified and categorized. You can see how often each process variant occurs, how long each variant takes, and what factors determine which variant is followed. This analysis often reveals that the standard process is not the most common path, and that significant effort is spent on exception handling and workarounds.

We integrate data from multiple systems to create cross-system process views. A procurement process might span your email, ERP, approval system, and payment platform. Our mining solution connects events across these systems to visualize the complete end-to-end process, revealing handoff delays and system integration issues that are invisible when analyzing each system independently.

Bottleneck Identification and Root Cause Analysis

Process mining reveals where time and resources are consumed. Bottleneck analysis identifies the specific process steps, handoffs, and conditions that cause delays. AI-powered root cause analysis determines why bottlenecks occur, examining factors like time of day, team workload, document complexity, and upstream process quality.

Our systems quantify the business impact of each bottleneck. You can see how much delay each bottleneck contributes, how many process instances are affected, and what the cost impact is in terms of delayed revenue, wasted resources, or customer satisfaction degradation. This quantification enables data-driven prioritization of improvement initiatives.

Conformance checking compares actual process execution against your defined standard procedures. Deviations are flagged and categorized as policy violations, inefficient workarounds, or legitimate exceptions. This analysis helps enforce process discipline while identifying cases where the standard procedure needs updating.

Predictive Process Analytics

Beyond understanding past performance, AI process mining predicts future outcomes. Predictive models estimate the completion time of in-progress cases, identify cases at risk of breaching SLAs, and forecast resource demand based on current pipeline volume. These predictions enable proactive management rather than reactive firefighting.

Our predictive models learn from historical process data to identify the factors that influence outcomes. For approval processes, the model might learn that certain document types, request amounts, or submitter departments predict longer cycle times. For customer onboarding, the model identifies which steps are most likely to cause abandonment. These insights drive targeted interventions.

We also implement simulation capabilities that let you test process changes before implementing them. What happens if you add a parallel approval path? How does changing the assignment logic affect cycle time? Simulation provides evidence-based answers to these questions without the risk and cost of live experimentation.

Optimize Your Processes with Arthiq

Process mining delivers the highest value when insights are connected to action. Arthiq helps you translate process mining findings into concrete improvement initiatives, whether that means automating manual steps, redesigning process flows, or implementing real-time monitoring for ongoing optimization.

Our team combines technical process mining expertise with practical business process experience. We understand not just how to analyze processes but how to improve them in ways that are practical and sustainable for your organization.

Contact us at founders@arthiq.co to discuss how AI process mining can reveal optimization opportunities in your business operations.

What We Deliver

  • Automated process discovery from event log data
  • Interactive process visualization with variant analysis
  • Bottleneck identification with quantified business impact
  • Root cause analysis for process delays and exceptions
  • Conformance checking against standard procedures
  • Predictive analytics for in-progress cases
  • Process simulation for change impact assessment

Technologies We Use

PythonPyTorchPostgreSQLApache KafkaFastAPIOpenAILangChainPandasD3.jsDocker

Frequently Asked Questions

Process mining requires event logs with at minimum a case identifier, activity name, and timestamp for each event. Most business systems including ERPs, CRMs, ticketing systems, and workflow tools generate this data. We help you identify and extract the relevant data from your systems.
Initial process discovery and visualization can be delivered in 3 to 4 weeks. Comprehensive analysis with bottleneck identification, root cause analysis, and recommendations typically takes 6 to 10 weeks. Ongoing monitoring and predictive analytics are implemented in subsequent phases.
Yes. We integrate event data from multiple systems to create cross-system process views. This is often where the most valuable insights emerge, in the handoffs and delays between systems that are invisible when analyzing each system in isolation.
Process mining typically identifies efficiency improvements of 20 to 40 percent in analyzed processes through elimination of unnecessary steps, reduction of bottlenecks, and standardization of process variants. The specific ROI depends on the processes analyzed and the current level of optimization.

Ready to Discover Process Improvements?

Our team will analyze your business processes using AI-powered mining techniques, revealing hidden inefficiencies and providing data-driven recommendations for optimization.