Strategic AI Consulting

Most AI consulting starts with the technology. We start with a different question: What does this need to look like in production, serving real users, two years from now?

After 34 years building enterprise systems for organizations like American Express, HBO, NBC Universal, and American Family Insurance, I’ve seen the pattern repeatedly. The proof-of-concept works beautifully. The demo impresses stakeholders. Then reality hits: scale, cost, reliability, security, and the hundred small decisions that determine whether a system actually serves users or becomes shelfware.

The Problem We Solve

The AI industry has a dirty secret: most projects fail at the transition from prototype to production. Not because the AI doesn’t work—but because nobody planned for:

• Cost at scale — That $0.03 API call becomes $30,000/month when real users show up
• Reliability requirements — Demos can fail gracefully; production systems serving customers cannot
• Integration complexity — AI doesn’t exist in isolation; it needs to work with your existing systems
• Organizational readiness — Who maintains this? Who’s on call? What happens when the model provider changes their API?

We help you answer these questions before you’ve spent six months and significant budget learning them the hard way.

Our Approach

AI Production Readiness Assessment

We evaluate your existing AI initiatives—or planned ones—against the realities of production deployment. This isn’t a checkbox exercise. It’s an experienced architect examining your approach and telling you where the gaps are.

The assessment covers:

• Architecture viability — Will this design handle 10x load? 100x? What breaks first?
• Cost modeling — Realistic projections based on actual usage patterns, not optimistic estimates
• Operational readiness — Monitoring, alerting, failover, and the unsexy stuff that keeps systems running
• Team capability gaps — What skills do you have? What do you need?
• Vendor risk — What happens if your AI provider changes pricing, deprecates a model, or goes down?

You get a clear-eyed assessment and a prioritized list of what to fix, in what order.

Architecture Review & Remediation

Already built something that’s struggling? We diagnose the issues and design the path forward.

Common patterns we address:

• Synchronous architectures that can’t handle AI response latency
• Missing queue infrastructure for managing GPU workloads
• Single-provider dependencies creating cost and availability risk
• Monolithic designs that can’t scale components independently
• Observability gaps that make debugging impossible

This isn’t theoretical. We’ve built production AI infrastructure handling 100K+ monthly operations with queue-based orchestration, multi-provider failover, and SLA-based prioritization.

Build vs. Buy Analysis

The AI vendor landscape is overwhelming. OpenAI, Anthropic, Google, Azure AI, AWS Bedrock, open-source models, specialized providers—each with different pricing, capabilities, and lock-in implications.

We help you navigate:

• Managed services vs. self-hosted — When does running your own GPU infrastructure make financial sense?
• Multi-provider strategies — How to avoid lock-in while managing complexity
• Open-source viability — When do open models meet your requirements? When don’t they?
• Total cost of ownership — Beyond API pricing to infrastructure, operations, and team costs

Our own platform runs multi-provider with intelligent routing and caching that achieves 44-59% cost reductions. We’ll help you determine what approach fits your situation.

Use Case Prioritization & ROI Modeling

Not all AI use cases are created equal. Some deliver clear ROI in months. Others are science projects that consume resources without business impact.

We help you:

• Identify use cases with realistic production paths
• Sequence initiatives based on value, feasibility, and learning potential
• Build business cases that finance and operations will actually approve
• Define success metrics that matter beyond “we shipped an AI feature”

Why Work With Us

Production experience, not just AI enthusiasm. We’ve built systems that process millions of credit card transactions, serve 300 call center agents 24/7, and run continuously for over a decade. AI is the latest technology; production discipline is timeless.

Full-stack understanding. From GPU virtualization and Kubernetes orchestration to conversational UX patterns—we can evaluate decisions at every layer of your stack.

No vendor allegiances. We’re not reselling anyone’s platform. Our recommendations are based on what actually fits your situation.

Trainer perspective. As a Microsoft Certified Trainer and AWS Authorized Instructor who has trained 1,000+ developers, we know how to transfer knowledge, not just deliver reports.

Typical Engagement

A strategic consulting engagement usually runs 2-4 weeks depending on scope:

Week 1: Discovery and current state assessment. We review existing systems, interview key stakeholders, and understand your goals and constraints.

Week 2-3: Analysis and architecture. We dig into the technical details, model costs, evaluate options, and develop recommendations.

Week 4: Delivery and roadmap. You receive a clear assessment, prioritized recommendations, and a practical path forward—plus a working session to ensure your team can execute.

From there, we can support implementation directly, help you build and hire a team, or step back and let you run with it.

Ready to Talk?

If you’re navigating AI adoption and want experienced guidance before committing significant resources, let’s have a conversation. No pitch deck, no pressure—just a discussion about where you are and where you’re trying to go.