How to Use the KPI99 AI-Augmented PPI-F Digital Diagnostic Tool
This guide will walk you through using the AI-enhanced application to assess your organization's engineering maturity with intelligent diagnostics, predictive insights, and workload optimization recommendations.
Table of Contents
The PPI-F Framework
What is PPI-F?
PPI-F (Performance, Production Readiness, Infrastructure Efficiency, Failure Resilience) is KPI99's AI-Augmented Performance Engineering framework for measuring and improving digital system maturity. Enhanced with intelligent diagnostics, predictive analytics, and workload optimization, PPI-F measures what engineers actually control: system capabilities, operational practices, and engineering outcomes—all powered by machine learning insights.
KPI99's Philosophy: Performance failures are business risks — until they are engineered. The AI-Augmented PPI-F framework helps engineering teams identify gaps with intelligent anomaly detection, prioritize improvements with ML-powered recommendations, and track progress toward engineering excellence with predictive analytics.
The Four Dimensions
Performance (P)
What it measures: System speed, throughput, bottleneck identification, monitoring capabilities, performance budgets, and optimization techniques. Enhanced with AI-powered anomaly detection and ML-based regression detection.
Why it matters: Performance directly impacts user experience, conversion rates, and operational costs. Slow systems lose users, waste resources, and damage brand reputation. AI diagnostics help proactively identify issues before they impact users.
PPI-F perspective: Performance isn't just about speed—it's about understanding system behavior, identifying constraints, and engineering solutions that scale. AI-augmented diagnostics provide intelligent insights into workload patterns and optimization opportunities.
Production Readiness (P)
What it measures: SLOs, deployment strategies, rollback capabilities, runbooks, change management, configuration management, and testing coverage.
Why it matters: Production readiness determines deployment safety, incident response speed, and system reliability. Unprepared systems fail in production.
PPI-F perspective: Production readiness is about engineering confidence—knowing that your systems can be deployed safely, monitored effectively, and recovered quickly when things go wrong.
Infrastructure Efficiency (I)
What it measures: Capacity management, cost monitoring, resource utilization, optimization strategies, infrastructure as code, and cost allocation. Enhanced with AI-driven predictive capacity forecasting and cost trajectory modeling.
What it measures: Capacity management, cost monitoring, resource utilization, optimization strategies, infrastructure as code, and cost allocation.
Why it matters: Infrastructure efficiency directly impacts operational costs, scalability, and resource utilization. Inefficient infrastructure wastes money and limits growth.
PPI-F perspective: Infrastructure efficiency is about engineering economics—building systems that scale cost-effectively, optimize resource usage, and provide clear cost visibility. AI-driven predictive capacity and cost modeling help forecast demand and optimize spending.
Failure Resilience (F)
What it measures: High availability strategies, disaster recovery, RTO/RPO, cascading failure prevention, backup and recovery, failure testing, and incident response.
Why it matters: Failure resilience determines system availability, data protection, and business continuity. Systems that can't handle failures cause outages and data loss.
PPI-F perspective: Failure resilience is about engineering reliability—building systems that fail gracefully, recover quickly, and protect data integrity when things go wrong.
How PPI-F Differs
- Engineering-First: Measures engineering practices and system capabilities, not just processes
- Four Focused Dimensions: Concentrates on what matters most for production systems
- Actionable Recommendations: Provides prioritized, engineering-focused improvements with effort and impact estimates
- Continuous Improvement: Designed for iterative progress tracking and velocity measurement
Learn More About PPI-F
Want to understand the PPI-F framework in depth? Visit our comprehensive guide to learn about the framework philosophy, methodology, and how it helps engineering teams build better systems.
Explore PPI-F FrameworkGetting Started
1. Start the Application
Make sure both the backend and frontend servers are running:
Backend (Terminal 1):
cd backend
source venv/bin/activate
python run.pyThe backend will run on http://localhost:8001
Frontend (Terminal 2):
cd frontend
npm run devThe frontend will run on http://localhost:3000
2. Open the Application
Open your browser and navigate to: http://localhost:3000
You'll see the home page with:
- A hero section explaining the tool
- PPI-F dimensions overview (Performance, Production Readiness, Infrastructure Efficiency, Failure Resilience)
- Organizations section
Creating Your First Assessment
Step 1: Create an Organization
- On the home page, click "Start Your First Assessment" or "New Assessment" in the navigation
- You'll be taken to the "Create Organization" page
- Fill in:
- Organization Name: Your company or team name (required)
- Domain: Your organization's domain (optional)
- Click "Create Organization"
Step 2: Create an Assessment
- After creating an organization, you'll be redirected to the organization's assessments page
- Click "Create New Assessment"
- Fill in:
- Assessment Name: e.g., "Q1 2024 Engineering Maturity Assessment"
- Version: Optional version identifier (defaults to "1.0")
- Click "Create Assessment"
Completing an Assessment
Understanding the Assessment Interface
Once you create a PPI-F Diagnostic, you'll see the assessment questionnaire page with:
- PPI-F Progress Indicators: Shows your progress for each of the four PPI-F dimensions and overall maturity
- Question Navigation Sidebar: Lists all questions organized by PPI-F dimension
- Current Question: The PPI-F question you're currently answering
- Answer Options: Different input types depending on the question:
- Single Select: Radio buttons
- Multi Select: Checkboxes
- Numeric: Number input
- Free Text: Text area
Answering Questions
- Read the question carefully - Each PPI-F question is designed to assess a specific aspect of your engineering maturity within one of the four dimensions
- Select or enter your answer based on your current practices
- Auto-save: Your answers are automatically saved as you progress
- Navigate between questions:
- Use Previous and Next buttons
- Click on any question in the sidebar to jump to it
- Use the search/filter to find specific questions
- Critical Questions: Some questions are marked as critical - make sure to answer these
- Validation: The system will prevent you from completing the assessment if critical questions are unanswered
Completing the Assessment
- Once you've answered all questions (or at least all critical ones), click "Complete Assessment"
- The system will:
- Calculate maturity scores for each dimension
- Generate findings based on your answers
- Create AI-powered, intelligently prioritized recommendations
- Calculate overall maturity score and risk level
- Run AI diagnostics for anomaly detection and predictive insights
Viewing Results
After completing an assessment, you'll see the AI-Augmented results page with:
1. Executive Summary
- Overall Maturity Score: 0-5 scale
- Risk Level: Low, Medium, High, or Critical
- Cost Leakage Estimate: Estimated financial impact
- Key Insights: High-level takeaways
2. PPI-F Framework Visualization
Visual representation of the PPI-F framework with your scores:
- PPI-F Framework Diagram: Interactive visualization showing all four dimensions
- Dimension Scores: Individual scores for Performance, Production Readiness, Infrastructure Efficiency, and Failure Resilience
3. Dimension Scores & Visualizations
Detailed visual representations of your PPI-F scores:
- PPI-F Maturity Heatmap: Color-coded view of maturity across all four dimensions
- Radar Chart: Visual comparison of all PPI-F dimensions
- Progress Indicators: Detailed breakdown with percentages for each dimension
4. PPI-F Findings
- Severity Levels: Critical, High, Medium, Low
- Organized by Dimension: See findings for each PPI-F dimension
- Descriptions: Detailed explanations of each finding
5. PPI-F Engineering Recommendations
- PPI-F Prioritization Matrix: Visual representation of engineering effort vs. business impact
- Quick Wins: Low engineering effort, high business impact - start here!
- Major Projects: High engineering effort, high business impact - plan strategically
- Fill-ins: Low engineering effort, low business impact - do when convenient
- Thankless Tasks: High engineering effort, low business impact - avoid or defer
- Status Tracking: Track PPI-F recommendation status (pending, in_progress, completed, skipped)
- Engineering Details: Each PPI-F recommendation includes title, description, engineering effort level, business impact level, timeline estimate, priority score, and related KPIs
6. Assessment Tools
- Notes: Add notes and comments to your assessment
- Tags: Organize assessments with custom tags
- Custom Fields: Add additional metadata
Understanding Scoring Scale
Score Range
The KPI99 PPI-F Digital Diagnostic Tool uses a 0.0 to 5.0 maturity scale to assess your engineering practices:
- Each of the four PPI-F dimensions (Performance, Production Readiness, Infrastructure Efficiency, Failure Resilience) receives a score from 0.0 to 5.0
- The overall maturity score is the average of all dimension scores
- Scores are calculated using weighted averages based on question importance
Overall Maturity Ratings
4.0 - 5.0: Excellent
Low risk, mature practices. Your organization demonstrates best-in-class engineering maturity with comprehensive processes and practices in place.
3.0 - 3.9: Good
Medium risk, solid foundation. You have a good base with room for improvement. Focus on addressing gaps to reach excellence.
2.0 - 2.9: Fair
High risk, significant gaps. There are substantial areas requiring attention. Prioritize critical recommendations.
0.0 - 1.9: Needs Improvement
Critical risk, major gaps. Immediate action required. Focus on critical blockers first.
Risk Levels
Risk levels are automatically calculated based on your overall maturity score:
| Risk Level | Score Range | Description |
|---|---|---|
| Low | ≥ 4.0 | Minimal risk, mature engineering practices |
| Medium | 3.0 - 3.9 | Moderate risk, solid foundation with improvement opportunities |
| High | 2.0 - 2.9 | Elevated risk, significant gaps requiring attention |
| Critical | < 2.0 | Critical risk, major gaps requiring immediate action |
What is a Good Score?
Target: 3.5+ Overall
A score of 3.5 or higher indicates a solid, production-ready system with mature engineering practices.
- All dimensions should be ≥ 3.0
- No critical blockers present
- Some dimensions may be 4.0+
Excellent: 4.0+ Overall
A score of 4.0 or higher represents best-in-class engineering maturity.
- All dimensions ≥ 3.5
- Most dimensions ≥ 4.0
- Low risk across all areas
Minimum Acceptable: 3.0 Overall
A score of 3.0 or higher is the minimum for a production-ready system.
- No dimension should be below 2.5
- Address critical blockers immediately
- Focus on high-priority recommendations
Critical Blockers
Some questions are marked as critical. If any critical question receives a score below 2.0, the entire dimension score is capped at 2.0, regardless of other answers. This ensures that critical gaps are addressed before claiming higher maturity levels.
Important: Always address critical blockers first, as they prevent you from achieving higher overall scores even if other areas are strong.
Dimension Balance
While a high overall score is good, it's important to have balanced scores across all four dimensions. A score of 4.5 in one dimension but 2.0 in another indicates an unbalanced maturity profile.
Aim for:
- All dimensions within 0.5 points of each other for balanced maturity
- No single dimension below 2.5 (even if overall is good)
- Consistent improvement across all areas over time
Understanding Reports
Export Options
You can export your assessment in multiple formats:
PDF Reports
- • Full Report: Complete assessment with all details
- • Executive Report: High-level summary for leadership
- • Engineering Report: Technical details for engineering teams
Other Formats
- • JSON Export: Machine-readable format
- • CSV Export: Recommendation backlog
- • Excel Export: Spreadsheet format
Report Contents
PDF Reports include:
- Executive summary
- Dimension scores and visualizations
- Detailed findings
- Prioritized recommendations
- Action roadmap (30/60/90 days)
AI-Augmented Capabilities
🤖 AI-Augmented Performance Engineering
The KPI99 PPI-F Digital Diagnostic Tool is enhanced with AI-powered diagnostics that provide intelligent insights beyond traditional assessments. Our AI-Augmented Performance Engineering approach helps identify patterns, predict trends, and optimize recommendations using machine learning.
1. AI-Assisted Anomaly Detection
The system automatically detects anomalies in your assessment results:
- Regression Detection: Identifies significant drops in maturity scores compared to historical assessments
- Dimension Imbalance Analysis: Detects when one dimension is significantly weaker than others
- Unusual Patterns: Flags potential data quality issues or assessment inconsistencies
- Confidence Scoring: Each anomaly includes a confidence level based on statistical analysis
Anomalies are displayed in the AI-Augmented Diagnostics section of your results page, helping you identify areas that need immediate attention or verification.
2. Predictive Insights & Forecasting
Based on your assessment history, the AI system provides predictive analytics:
- Maturity Projections: Forecasts your maturity score 6 months ahead based on current trends
- Trend Analysis: Identifies whether your maturity is improving, declining, or stable
- Velocity Tracking: Measures the rate of improvement over time
- Capacity Forecasting: Predicts infrastructure capacity needs and cost trajectories
- Cost Optimization Insights: Estimates potential savings opportunities (15-30% typical)
These insights help you plan ahead and make data-driven decisions about where to invest engineering resources.
3. Workload Behavior Modeling
For organizations using distributed systems (Spark, EMR, EKS, Kubernetes), the AI system provides specialized insights:
- Distributed Workload Optimization: Identifies opportunities for Spark executor skew detection and cluster efficiency improvements
- Workload Clustering: Analyzes behavioral patterns to recommend optimization strategies
- Resource Waste Detection: Highlights areas where infrastructure resources are underutilized
These insights are automatically generated when the system detects distributed systems usage in your assessment responses.
4. AI-Powered Recommendation Prioritization
Recommendations are intelligently prioritized using machine learning:
- Impact/Effort Optimization: AI calculates optimal prioritization based on dimension criticality and historical patterns
- Workload Pattern Analysis: Adjusts recommendations based on detected system patterns
- Contextual Scoring: Considers your organization's maturity level when prioritizing
This ensures you focus on recommendations that will have the greatest impact for your specific situation.
KPI99's AI Integration Philosophy
KPI99 positions itself as an AI-Augmented Performance Engineering company, not a generic AI consultancy. Our AI capabilities are specifically designed to enhance performance engineering for enterprise distributed systems.
Core Focus: Performance. Scale. Reliability—Engineered. AI augments our engineering-first approach by providing intelligent diagnostics, predictive insights, and workload optimization—all in service of building better systems.
Advanced Features
1. Assessment Comparison
Compare two assessments to track progress:
- Go to an assessment's results page
- Click "Compare with Another Assessment"
- Select the assessment to compare
- View side-by-side comparison of scores and changes
2. Assessment Cloning
Create a copy of an existing assessment:
- Go to the assessments list
- Click the "Clone" button on an assessment
- Provide a new name for the cloned assessment
- All answers will be copied to the new assessment
3. Analytics Dashboard
View organization-level analytics:
- Navigate to an organization
- Click "Analytics" in the navigation
- View trends, metrics, and benchmark comparisons
4. Bulk Operations
Perform actions on multiple assessments:
- Bulk Status Update: Update recommendation statuses in bulk
- Bulk Delete: Delete multiple assessments at once
- Bulk Summary: Get summary for multiple assessments
5. Advanced Filtering
Filter assessments by:
- Status: draft, in_progress, completed
- Search: Search by name or tags
- Date Range: Filter by creation or completion date
6. Webhooks and Integrations
Set up webhooks to integrate with other systems:
- Go to "Integrations" in an organization
- Create a webhook subscription
- Configure events to subscribe to (assessment.created, assessment.completed, etc.)
- Set up HMAC verification for security
7. Notifications
Stay informed about:
- Assessment completions
- New recommendations
- Important updates
- View unread count in the notification bell
Tips and Best Practices
1. Answer Honestly
Be honest about your current state. The assessment is only as good as your answers. It's okay to have low scores - that's why you're doing the assessment!
2. Involve the Right People
Include team members who understand different aspects of your engineering. Consider multiple perspectives for a more accurate assessment.
3. Review Recommendations Carefully
Focus on "Quick Wins" first for immediate impact. Plan "Major Projects" for longer-term improvements. Use the prioritization matrix to guide your roadmap.
4. Track Progress Over Time
Run assessments quarterly or bi-annually. Compare results to see improvements. Use the analytics dashboard to visualize trends.
5. Export and Share
Export executive reports for leadership. Share engineering reports with your team. Use CSV exports to create tickets in project management tools.
6. Use Tags and Notes
Tag assessments by quarter, team, or project. Add notes about context or decisions. This helps when reviewing historical assessments.
7. Leverage Custom Fields
Add metadata relevant to your organization. Track additional context (team size, tech stack, etc.). Use for filtering and organization.
PPI-F Success Stories
Case Study: E-Commerce Platform
Initial PPI-F Score: 2.1/5.0
After 6 Months: 3.8/5.0
Improvement: +1.7 points
Key Improvements:
- Reduced P95 latency by 60%
- Implemented automated rollbacks
- Reduced infrastructure costs by 40%
- Achieved 99.9% uptime
"The PPI-F framework helped us identify critical gaps we didn't know existed. Following the recommendations, we saw immediate improvements in system reliability and user experience." — Engineering Lead
Case Study: SaaS Platform
Initial PPI-F Score: 2.8/5.0
After 12 Months: 4.2/5.0
Improvement: +1.4 points
Key Improvements:
- Implemented comprehensive observability
- Reduced deployment time by 75%
- Achieved multi-region active-active setup
- Zero critical incidents in 6 months
"PPI-F gave us a clear roadmap. We focused on Quick Wins first, then tackled Major Projects. The framework's prioritization matrix was invaluable." — CTO
Case Study: FinTech Startup
Initial PPI-F Score: 1.9/5.0
After 9 Months: 3.5/5.0
Improvement: +1.6 points
Key Improvements:
- Implemented SLOs and error budgets
- Built comprehensive disaster recovery
- Reduced infrastructure costs by 50%
- Passed SOC 2 Type II audit
"Starting with a low PPI-F score was actually helpful—it showed us exactly where to focus. The framework's critical blocker identification saved us months of trial and error." — VP Engineering
KPI99 Resources
PPI-F Framework Resources
Framework Documentation
Learn about the PPI-F framework methodology, philosophy, and best practices.
Explore PPI-F FrameworkAPI Documentation
Integrate PPI-F assessments into your workflows using our comprehensive API.
View API DocsKPI99 Philosophy
"Performance failures are business risks — until they are engineered."
At KPI99, we believe that engineering maturity isn't about following processes—it's about building systems that perform reliably, scale efficiently, and recover gracefully from failures. The PPI-F framework is our contribution to the engineering community, providing a practical, actionable approach to measuring and improving engineering maturity that actually works in production.
Get Support
Need help with your PPI-F assessment or have questions about the framework?
- • Review the comprehensive documentation above
- • Explore the PPI-F framework methodology page
- • Check the API documentation for integration help
- • Visit kpi99.io for more resources