Services

How I Help

I work with SMB and mid-market companies, roughly $10M to $50M in revenue, that have run an AI pilot and cannot get it into production, or that know they need a mid-market AI strategy and do not want to buy one from a vendor selling the platform underneath it.

I do not resell models. I do not deliver a slide deck and leave. I embed with your team, take ownership of the implementation, and build the capability so you are not dependent on me a year from now.

Engagement Models

I flex to fit your challenge. Some organizations need AI judgment at the leadership table. Others need someone to take a stalled pilot and ship it. Here is how we can work together:

Fractional AI Leadership

Senior technology leadership focused on your AI portfolio. I set strategy, evaluate build versus buy, own vendor selection, establish governance, and keep executive expectations calibrated to what the technology can actually do this quarter. For companies that need fractional AI leadership without a full-time hire.

AI Implementation Engagements

Scoped delivery for a specific outcome: moving a stalled pilot into production, standing up an AI governance framework, building an agentic workflow that replaces a manual process, or running a 2 to 3 day assessment that tells you honestly which of your six proposed use cases are viable. Clear scope, defined deliverables, measurable outcomes.

Embedded Leadership

Full integration as a hands-on technology leader owning the AI function alongside the rest of the stack. I build and mentor the team, drive implementation from the inside, and leave behind people who can run it. My longest engagements have run 10 and 15 years for a reason.

The AI Implementation Framework

Every implementation engagement follows the MLLytics AI Framework, a four-phase path from pilot to production:

Foundation

Establish the preconditions for everything that follows: AI governance guardrails, data readiness, and organizational alignment. This is the phase companies skip and then pay for later. The output is an AI policy charter, a data landscape assessment, and a readiness report your team can act on.

Execution Readiness

Translate the foundation into a structured plan for what you will build and how. We score and prioritize use cases, map process integration points, and build a 12 to 18 month program roadmap. Most engagements kill two or three proposed use cases in this phase and find one nobody had considered.

Build & Prove

Ship a production system, not a proof of concept. Real integrations, real error handling, real monitoring, and defined fallback behavior for when the model is wrong. Value gets measured against the ROI hypotheses set in the roadmap, not against a demo.

Scale & Lead

AI stops being a project and becomes organizational capability. Handoff, upskilling, scaling playbooks, and the operating rhythm that keeps systems improving after the engagement ends. Twenty years of embedded leadership says this is the phase that determines whether any of it lasted.

The Operating Discipline Underneath

The framework works because of the discipline underneath it. People, Process, Technology, Innovation is the method I have applied for over twenty years: align the people, optimize the process with Lean Six Sigma rigor, modernize the technology, and build innovation as a habit rather than a project. AI implementation is the newest application of a discipline that has always applied.

Four operating habits shape every AI system I ship:

• Workflow before model. The workflow the system lives inside gets designed before the model gets chosen.

• Memory as product surface. What the system remembers, and how people correct it, is a first-class feature.

• Review where risk lives. Human review sits exactly where the cost of a wrong output is highest.

• Outputs people can act on. Every system ships outputs a specific person can act on the same day.

Areas of Expertise

• AI implementation, pilot to production

• AI governance and risk frameworks for mid-market

• Agentic systems and workflow automation

• AI readiness and use case assessment

• Data platform architecture and AI-grade data readiness

• Build versus buy evaluation and vendor selection

• Change management and AI upskilling for non-technical teams

• Security and compliance for AI systems (50+ audits guided)

• ERP, CRM, and core systems modernization

Industries

Manufacturing, automotive, financial services, banking, healthcare, government, education, logistics, warehousing, insurance, travel and leisure, marketing, and print. The industries are different. The fundamentals of transformation are the same.