Building Production AI Systems That Actually Work

Staff Engineer at DigitalOcean. 17 years in technology, from infrastructure to intelligent automation.

Currently leading AI initiatives that process 30,000+ customer conversations monthly, save $270K-$300K annually, and maintain 90%+ satisfaction scores.

Based in Karachi, Pakistan Open to knowledge sharing and mentoring

Production AI Systems

AI Support Deflection System

Production - 18+ months

Intelligent conversation system that handles customer support automatically.

What it does:

  • Answers customer questions without human intervention
  • Escalates to humans when confidence is low
  • Learns from 800+ article knowledge base
  • Monitors satisfaction in real-time

Results:

  • ~30% deflection rate (handles 1 in 3 tickets automatically)
  • Equivalent to 28 full-time support engineers
  • $270K-$300K verified annual cost savings
  • 90%+ customer satisfaction maintained
  • 24/7 availability

Technical: Multi-agent conversation framework, RAG-based knowledge retrieval, confidence scoring, Intercom integration

AI QA Agent

Production - 12+ months

Automated quality assurance for support conversations.

What it does:

  • Evaluates every support conversation for soft skills
  • Scores empathy, clarity, professionalism, resolution quality
  • Uses industry-standard metrics
  • Identifies coaching opportunities

Results:

  • Processes 30,000+ conversations monthly
  • QA coverage: 10% → 100% (9x efficiency gain)
  • Eliminated evaluation backlog
  • Consistent, unbiased scoring
  • Team coaching insights

Technical: LLM-based evaluation, custom scoring algorithms, PII sanitization, Redis queue processing, MariaDB analytics

AI Support Co-pilot

Production - 6+ months

Real-time assistant for support engineers debugging technical issues.

What it does:

  • Helps diagnose server and application problems
  • Analyzes logs and suggests solutions
  • Provides context-aware troubleshooting steps
  • Integrates with existing support tools

Results:

  • Complex investigation time: 30 min → 7-8 min (75% reduction)
  • Improved first-contact resolution
  • Increased engineer confidence
  • Reduced escalations to seniors

Technical: Context-aware assistance, server diagnostics integration, log analysis, natural language interface

Cloudways MCP Server

Testing Phase

Model Context Protocol implementation for infrastructure management.

What it does:

  • 43+ tools covering complete Cloudways API
  • Enables AI agents to manage cloud infrastructure
  • Handles servers, applications, security, deployments
  • Enterprise authentication and security

Categories:

  • Basic Operations: 18 tools (auth, discovery, monitoring)
  • Server Operations: 12 tools (power, backup, storage, services)
  • Application Management: 8 tools (deploy, performance, config)
  • Security & Access: 5 tools (IP management, SSL, Git)

Technical: MCP-compliant server, authenticated API wrappers, async architecture, Fernet encryption, Redis caching

Internal Knowledge Base

Production - 2+ years

Centralized repository of customer problems and solutions.

What it does:

  • 800+ articles covering common issues
  • Step-by-step solutions with screenshots
  • Searchable by problem, product, error message
  • Updated based on ticket patterns

Results:

  • Single source of truth for all teams
  • Improved answer consistency
  • Faster new hire onboarding (6 weeks → 3 weeks)
  • Foundation for AI deflection system
  • Better customer experience

Maintenance: 30-40 new articles monthly, 100+ verified for accuracy

Automated Signup Verification

Production - 8+ months

Intelligent risk assessment system for new customer signups.

What it does:

  • Automated verification workflows for routine cases
  • AI-powered risk analysis for complex decisions
  • Domain legitimacy and identity verification
  • Smart routing based on risk profiles

Results:

  • 70-80% of signups processed automatically
  • Verification time: 3-5 minutes → 30 seconds (low-risk cases)
  • Complex cases: Pre-verified evidence packages for human review
  • 35% reduction in manual review workload
  • Improved fraud detection through systematic checks

Impact: Faster onboarding for legitimate customers, reduced manual effort for billing team, systematic fraud prevention

Multi-agent Customer Health Monitoring

In Development - 45% complete

Proactive system for predicting customer issues before they escalate.

What it will do:

  • Analyze customer interactions across all touchpoints
  • Predict churn risk based on behavior patterns
  • Identify upsell opportunities from usage data
  • Surface recurring pain points across customer base
  • Connect technical issues with business outcomes

Planned capabilities:

  • Early warning system for at-risk customers
  • Data-driven upsell recommendations
  • Automated analysis of support patterns
  • Technical investigation integration
  • Prioritized action items for customer success teams

Target outcomes:

  • Proactive intervention before customers leave
  • Revenue optimization through timely upgrades
  • Reduced manual data gathering (hours → minutes)
  • Better customer experience through early problem detection

Technical approach: Multi-agent architecture coordinating data from support, billing, platform usage, and infrastructure monitoring

Technical Expertise

AI & Machine Learning: Python, LangGraph, Google ADK, Multi-agent systems, RAG pipelines, Model Context Protocol (MCP), FAISS vector search

Infrastructure: Linux, Kubernetes, Docker, AWS, GCP, DigitalOcean, Linode, CI/CD, GitOps, Argo, Service Mesh, Terraform

Databases & Caching: MySQL/MariaDB, ProxySQL, Redis, Vector databases

Web & Performance: Nginx, Apache, Varnish

Other: Bash, Go (learning), Git, API design

Open to Conversations

Free 30-Minute Consultations

Share your AI automation challenges. Get honest technical feedback.

Good for:

  • System architecture questions
  • Production AI implementation lessons
  • Technical feasibility assessments
  • Honest reality checks

Book a free call →

Deep-dive technical mentoring for teams building production AI systems.

Topics:

  • Multi-agent system design
  • RAG implementation patterns
  • Production deployment strategies
  • Support automation architecture

Format: 1-on-1 sessions, code reviews, architecture guidance

Mentoring details →

Background

17 years in technology:

  • 2008-2011: Media/Editorial work while learning networking
  • 2011-2019: Infrastructure and hosting (Windows, Linux, cloud platforms)
  • 2019-2023: Platform engineering at scale (100K+ VMs, Kubernetes)
  • 2023-Present: AI systems architecture and implementation

Education:

  • PGD Computer Science, University of Karachi (2010-2011)
  • BA International Relations, University of Karachi (2006-2008)

Certifications:

  • Certified Kubernetes Administrator (CNCF, 2023)
  • Service Mesh Fundamentals (Buoyant)

Languages: English, Urdu

Connect

LinkedIn: linkedin.com/in/aphraz Email: connect@afraz.dev GitHub: github.com/aphraz

Open to knowledge sharing and mentoring