Production AI Systems I’ve Built

All systems listed here are in production at DigitalOcean/Cloudways, processing real workloads and delivering measurable business results.

1. AI Support Deflection System

Problem: Support volume growing 30% YoY. Hiring doesn’t scale. Quality inconsistent across agents.

Solution: Intelligent conversation system that handles customer queries automatically.

Architecture:

  • Multi-agent conversation framework
  • RAG-based knowledge retrieval from 800+ article knowledge base
  • Confidence-scored response generation
  • Smart escalation to human agents
  • Real-time analytics and monitoring

Results:

  • ~30% deflection rate maintained in production
  • Workload equivalent to 28 full-time support engineers
  • $270K-$300K in verified annual cost savings
  • 90%+ customer satisfaction maintained
  • 24/7 availability without additional staffing

2. AI QA-Agent

Problem: QA team could only evaluate ~10% of support conversations. Inconsistent quality across teams and regions.

Solution: Automated quality assurance system that evaluates every conversation for soft skills and communication quality.

Architecture:

  • LLM-based evaluation framework
  • Soft-skills assessment (empathy, clarity, professionalism)
  • Industry-standard metrics implementation (CSAT predictors, tone analysis)
  • Automated scoring and reporting pipelines
  • Anomaly detection for coaching opportunities

Results:

  • 9x efficiency improvement for QA department
  • Coverage: 10% → 100% of all conversations
  • 30,000+ conversations analyzed monthly
  • Identified coaching patterns across teams
  • Reduced subjective bias in evaluations

3. AI Support Agent Co-pilot

Problem: Complex server/application debugging taking 30+ minutes per chat. Agent productivity limited by investigation time.

Solution: Real-time assistant that helps support engineers diagnose and resolve technical issues faster.

Architecture:

  • Context-aware assistance system
  • Server diagnostics integration
  • Log analysis and pattern recognition
  • Solution suggestion engine
  • Integration with existing support tools

Results:

  • Complex issue investigation: 30 min → 7-8 min (75% reduction)
  • Improved average handling time (AHT)
  • Increased agent confidence and satisfaction
  • Better first-contact resolution rate
  • Reduced escalations to senior engineers

4. Cloudways MCP Server

Problem: Need standardized way for AI agents to interact with Cloudways infrastructure. Manual API integration for each use case is inefficient.

Solution: Model Context Protocol server implementing complete Cloudways API surface.

Architecture:

  • MCP-compliant server implementation
  • 43+ tools organized by domain:
    • Basic Operations (18 tools): Auth, server/app discovery, monitoring
    • Server Operations (12 tools): Power management, backup, storage, services
    • Application Management (8 tools): Deployment, performance, configuration
    • Security & Access (5 tools): IP management, SSL, Git deployment
  • Authenticated API wrappers with error handling
  • Structured tool definitions for AI agent consumption

Capabilities:

  • Server lifecycle management (start, stop, restart, backup)
  • Application deployment and rollback
  • Security configuration and monitoring
  • Performance analytics and optimization
  • Git-based deployment workflows

5. Internal Knowledge Base

Problem: Support knowledge scattered across wikis, Slack, email, tribal knowledge. Inconsistent answers to same questions.

Solution: Centralized, searchable repository of customer problems and solutions.

Scope:

  • 800+ articles covering common issues
  • Categorized by product area and problem type
  • Step-by-step solutions with screenshots
  • Continuously updated based on ticket patterns
  • Integration with support tools for easy access

Impact:

  • Single source of truth for all teams
  • Improved answer consistency across agents
  • Reduced new hire onboarding time (6 weeks → 3 weeks)
  • Foundation for AI deflection system
  • Better customer experience through standardization

6. Automated Signup Verification System

Problem: Manual verification of new customer signups required 3-5 minutes per request. Billing team performing repetitive data collection and assessment. Inconsistent verification timelines affecting customer experience.

Solution: Hybrid automation system combining workflow automation for routine tasks with AI-powered analysis for complex decisions.

How it works:

Layer 1: Automated Workflows

  • Automatic data extraction and routing
  • Risk score assessment and categorization
  • Domain validation and legitimacy checks
  • Email reputation and infrastructure analysis
  • Automated documentation and audit trails

Layer 2: AI Intelligence

  • Domain age and registration verification
  • Social media profile validation
  • Business legitimacy analysis
  • Risk contextualization (high-risk indicators vs. mitigating factors)
  • Structured evidence compilation for human review

Decision Framework:

  • Low Risk: Automated approval with systematic verification
  • Medium Risk: AI provides pre-verified evidence, human decides in 60-90 seconds
  • High Risk: Enhanced scrutiny with detailed risk analysis

Results:

  • 70-80% automation rate for routine verifications
  • Verification time reduced:
    • Low-risk cases: 3-5 min → <30 seconds (automated)
    • Complex cases: 3-5 min → 60-90 seconds (pre-verified for human review)
  • 35% workload reduction for billing team
  • Improved fraud detection through systematic checks
  • Better customer experience through faster legitimate onboarding
  • Complete audit trail for compliance and investigation

Business value:

  • Faster onboarding for legitimate customers
  • Scalable verification without linear headcount growth
  • Systematic fraud prevention
  • Consistent, auditable decision-making
  • Team focuses on complex cases requiring business judgment

7. Multi-agent Customer Health Monitoring (In Development)

Problem: Customer insights scattered across multiple systems. Reactive approach - issues identified only after complaints or churn. Manual analysis doesn’t scale. Technical problems not connected to business outcomes.

Solution: Comprehensive AI system that automatically analyzes customer interactions, usage patterns, and technical health to predict issues before they escalate.

System Architecture:

Data Analysis Across All Touchpoints:

  • Support conversations and ticket patterns
  • Platform usage and feature adoption
  • Billing history and payment trends
  • Infrastructure monitoring alerts
  • Technical incident correlation

Human-in-the-Loop Design:

  • Technical teams provide root cause analysis
  • Customer success reviews critical findings
  • Human validation for high-impact recommendations
  • Automated insights, human judgment

Planned Outputs:

  • Customer health scores with trend analysis
  • Churn risk predictions with intervention timing
  • Prioritized upsell opportunities with revenue estimates
  • Action items for customer success teams
  • Pain point reports for product improvements

Want to Discuss?

If you’re building similar systems:

If you want to collaborate:

  • Email me - Open to discussions and collaboration