Phlow AI
Quality & Safety

Operational Insight Dashboard

Pattern recognition across incident reports enabling proactive quality improvement.

70%
Fewer repeat incidents
<1day
Pattern Detection
$500K+
Annual Savings
10K+
Reports Analyzed
01

The Challenge

The operations team collected thousands of incident reports monthly, but extracting patterns required manual review. By the time trends were identified, problems had persisted for months.

Existing analytics could count keywords but could not understand the underlying causes. Teams were reactive, not proactive.

Critical patterns were hiding in plain sight, buried in unstructured text that traditional tools could not parse.

We had all the data we needed, but no way to see what it was telling us.

10K+
Monthly reports
3mo
Avg time to identify patterns
02

The Solution

We developed an LLM-powered analytics dashboard that automatically analyzes incident reports, identifies patterns, and suggests root causes in real-time.

The system understands context and connects related issues across time and departments.

Interactive visualizations make complex patterns immediately understandable.

Key Features

  • Automatic pattern detection across reports
  • Root cause analysis with confidence scoring
  • Real-time trend monitoring and alerting
  • Interactive drill-down visualizations
  • Automated insight summaries for leadership
03

Implementation

Technical Approach

  • Claude API for semantic analysis of incident reports
  • Plotly/Dash for interactive visualizations
  • PostgreSQL for historical analysis
  • Docker deployment for enterprise integration
  • REST API for data pipeline integration

Change Management

  • Executive sponsorship and alignment
  • Pilot with highest-volume incident category
  • Training for operations and analytics teams
  • Integration with existing reporting workflows
04

Results & Impact

70%
Fewer Repeats
<1day
Pattern Detection
$500K+
Annual Savings
10K+
Reports Analyzed
  • First month identified 3 systemic issues that had persisted for years
  • Repeat incident rate dropped significantly within two quarters
  • Operations team shifted from reactive to proactive mode
  • Leadership now has real-time visibility into operational health

Technology Stack

Claude APIPythonPlotlyPostgreSQLDocker