Training & Enablement

Most AI training falls into two categories: academic courses taught by people who’ve never shipped production systems, or vendor training focused on selling you their platform. Neither prepares your team for the reality of building AI applications that serve real users at scale.

I’ve been a Microsoft Certified Trainer since 1998 and an AWS Authorized Instructor since 2017. I’ve trained over 1,000 developers across dozens of organizations. But more importantly, I’m actively building production AI systems right now—the Kusog AI Agent platform processes 100K+ monthly operations. When I teach production patterns, I’m teaching what I use.

The Problem We Solve

Your team is smart. They can follow tutorials, complete online courses, and build demos. But there’s a gap between “I completed the LangChain quickstart” and “I can build an AI system that handles 10,000 concurrent users with cost controls and graceful degradation.”

That gap includes:

• Architecture decisions — When to use streaming vs. batch? How to structure conversation state? Where does RAG fit?
• Production concerns — Cost management, error handling, observability, multi-tenant isolation
• Operational reality — What breaks at scale? How do you debug AI systems? What does on-call look like?
• Integration patterns — How does AI fit into existing systems without requiring a complete rewrite?

Online courses don’t cover this. Vendor training glosses over it. Your team needs to learn from someone who’s solved these problems in production.

Training Programs

AI Platform Architecture for Development Teams

A comprehensive program for teams building AI-powered applications. This isn’t an introduction to machine learning—it’s a practical guide to architecting systems that use LLMs and other AI capabilities in production.

Core Modules:

Conversational AI Architecture

• The four interaction patterns: topic starters, guided workflows, builders, tool-driven interviews
• When to use each pattern and how to combine them
• Conversation state management and context windows
• Streaming responses and real-time UX

LLM Integration Patterns

• Multi-provider architecture (OpenAI, Anthropic, Google, open-source)
• Prompt engineering for production (not just “write better prompts”)
• Cost management: caching, routing, token optimization
• Error handling and graceful degradation

RAG Implementation

• Document ingestion and chunking strategies
• Embedding models and vector storage
• Retrieval strategies and re-ranking
• Grounding responses in your data

Production Operations

• Observability for AI systems (what to log, what to measure)
• Cost tracking and budget controls
• Testing strategies for non-deterministic systems
• Incident response for AI failures

Format: Typically 3-5 days depending on depth. Combines presentation, discussion, and hands-on exercises using your actual technology stack.

Production AI Patterns Workshop

A focused workshop on the architectural patterns that differentiate production AI systems from prototypes. Ideal for teams that have basic AI familiarity and need to level up on production concerns.

Workshop Modules:

Pipeline Architecture

• Sequential and parallel processing patterns
• Configuration-driven orchestration (YAML-based pipeline definition)
• Error handling and retry strategies
• Performance optimization through parallelization

Queue-Based AI Workloads

• Why queues matter for AI (GPU constraints, rate limits, cost control)
• SLA-based prioritization
• Model loading optimization
• Handling backpressure and overload

Multi-Tenant AI Systems

• Tenant isolation strategies
• Per-tenant configuration and customization
• Usage tracking and billing
• Data separation and security

Cost Control Strategies

• The K-token economy approach
• Intelligent caching for AI responses
• Multi-provider routing for cost optimization
• Budget controls and alerting

Format: 1-2 days, intensive and hands-on. Participants leave with patterns they can apply immediately.

Custom Corporate Training

Your team, your stack, your problems. We design and deliver training programs tailored to your specific situation.

Examples of custom programs we’ve delivered:

• Cloud AI architecture — For teams adopting AWS, Azure, or GCP AI services
• Kubernetes for AI workloads — GPU integration, scheduling, and resource management
• LLM application development — Building applications on OpenAI, Anthropic, or open-source models
• AI infrastructure operations — Running and maintaining AI systems in production
• Modernization for AI — Preparing legacy systems for AI integration

Process:

1. Discovery call — Understand your team’s current skills, your technology stack, and your learning objectives
2. Curriculum design — Build a program that addresses your specific gaps and goals
3. Material development — Create exercises and examples relevant to your domain
4. Delivery — On-site or remote training with hands-on components
5. Follow-up — Q&A sessions as your team applies what they learned

Custom programs range from single-day workshops to multi-week comprehensive training depending on scope.

Hands-On Implementation Labs

Not a lecture—a working session where your team builds something real with expert guidance.

Lab Formats:

Guided Implementation Your team works on an actual project (yours or a representative scenario) with me providing real-time guidance, code review, and architectural direction. You leave with working code, not just notes.

Architecture Review Lab Bring your existing AI implementation. We walk through it together, identify issues and opportunities, and work through improvements as a team. Part training, part consulting, entirely practical.

Proof-of-Concept Sprint For teams evaluating AI approaches, we spend 2-3 days building a focused POC together. Your team learns by doing, and you end with something you can demonstrate to stakeholders.

Why Train With Us

Current practitioner. I’m not teaching from a textbook or slides I wrote five years ago. The Kusog AI Agent platform is in active development. When I describe production patterns, I’m describing what I built last month.

Certified credentials. Microsoft Certified Trainer since 1998. AWS Authorized Instructor since 2017. This matters for organizations that need documented training from certified professionals.

Enterprise context. 34 years building enterprise systems for organizations like American Express, HBO, NBC Universal, and American Family Insurance. I understand the constraints, politics, and realities of enterprise development—not just startup greenfield projects.

Full-stack perspective. Training covers the complete picture: infrastructure, backend, frontend, operations. Not just one layer in isolation.

Practical focus. Every concept connects to implementation. No theoretical tangents that don’t translate to real work.

Training Formats

On-Site We come to you. Best for team cohesion, intensive learning, and when you want everyone focused without distractions. Typically for groups of 6-20 developers.

Remote/Virtual Live instruction via video conference with screen sharing, collaborative exercises, and breakout sessions. Works well for distributed teams or when travel isn’t practical.

Hybrid Combination of on-site intensive sessions and remote follow-up. Often the best balance for comprehensive programs—start together in person, continue remotely as the team applies learning.

Office Hours Add-On For any format, we can add ongoing office hours—scheduled time for your team to ask questions, get code reviewed, or work through problems as they apply new skills. Available for 1-3 months post-training.

Typical Engagements

Single-Day Workshop: $4,000 - $6,000

• Focused topic (e.g., Production AI Patterns, RAG Implementation)
• Up to 15 participants
• Includes materials and exercises
• Remote or on-site (plus travel for on-site)

Multi-Day Program: $3,000 - $5,000 per day

• Comprehensive curriculum (e.g., AI Platform Architecture)
• Custom exercises using your technology stack
• Typically 3-5 days
• Volume discounts for longer programs

Custom Corporate Training: Scoped based on requirements

• Full curriculum design
• Custom materials and exercises
• Flexible delivery schedule
• Follow-up support included

Implementation Labs: $2,500 - $4,000 per day

• Hands-on working sessions
• Small group (4-8 developers ideal)
• Real code output, not just learning

All pricing is negotiable for larger engagements, ongoing relationships, or non-profit/educational organizations.

What Participants Say

After training sessions, participants consistently highlight:

• “Finally, someone who’s actually built this stuff” — The difference between theoretical knowledge and production experience is immediately apparent
• “The examples were relevant to our actual work” — Custom exercises using familiar technology stacks accelerate learning
• “I can apply this Monday morning” — Practical patterns that translate directly to current projects

Ready to Level Up Your Team?

If your team is building AI capabilities and needs to move beyond tutorials to production-ready skills, let’s talk about what training would look like for your situation. We’ll discuss your current skill levels, technology stack, and objectives, then recommend an approach that makes sense.