Approach

AI adoption that starts with the right tools, not a bigger budget

Most AI rollouts fail because nobody evaluates whether the tools fit the workflows. I take the opposite approach: evaluate first, prove value fast, then scale what's working.

Why Most AI Initiatives Stall

The pattern is always the same

  • →

    Tools before evaluation

    Companies buy platforms based on demos, not fit. Nobody evaluates whether the tool integrates with the existing stack or solves a real workflow problem. The tool sits unused.

  • →

    Build vs. buy paralysis

    Engineering teams stuck debating whether to build internal tooling or buy a platform, without a framework for evaluating either option against their actual constraints.

  • →

    Throwing tokens at the problem

    Defaulting to the most expensive model for every task instead of matching model selection, prompt design, and token spend to the actual complexity of each workflow.

The Framework

Crawl. Walk. Run.

Every engagement follows this progression. I don't skip steps, and I don't move to the next phase until the current one is working.

01
Weeks 1–2

Assess & Enable

  • Audit current workflows and tooling for highest-value AI opportunities
  • Evaluate existing tools against your stack, team size, and actual constraints
  • First AI-assisted workflow scoped, built, and validated with your engineering team
→ Team aligned on which tools fit and where AI delivers real value
02
Weeks 3–6

Build & Integrate

  • AI workflows built alongside your engineering and ops teams around real processes
  • Token-efficient architectures that match model selection to task complexity
  • Performance tracking to measure time saved, output quality, and cost per workflow
→ AI is integrated into daily work with cost and performance visibility
03
Week 7+

Automate & Scale

  • Automated workflows for validated, high-impact processes
  • Cross-functional AI systems (customer intelligence, compliance, observability)
  • Internal documentation so capability stays in your organization
→ Self-sustaining AI operations that scale with your team

By Function

What your team actually learns

Customer-Facing Teams

  • Surface account risk signals before they become churn
  • Draft personalized client communications in minutes
  • Build internal knowledge bases that make every rep more effective

Operations & Compliance

  • Automate repetitive documentation and reporting workflows
  • Build RAG systems that pull from approved source material
  • Reduce manual review cycles without sacrificing accuracy

Engineering & Platform

  • Evaluate AI tooling against existing stack constraints and integration requirements
  • Build token-efficient architectures that match model selection to task complexity
  • Implement evaluation frameworks for model performance, cost, and reliability

Leadership

  • Build a structured framework for build vs. buy decisions on AI tooling
  • Understand token economics and model pricing before committing to a platform
  • Make adoption decisions tied to real business outcomes, not vendor demos

Engagement Options

Two ways to work together

Every engagement is scoped to your team's size, complexity, and goals.

Workshop

2-week intensive

  • AI workflow audit and tool evaluation
  • Build vs. buy analysis for your use cases
  • Custom prompt library and workflow templates
  • Token-efficient architecture recommendations
  • 30-day follow-up support
Get started →
Most Popular

Advisory + Build

60–90 day engagement

  • Everything in the Workshop
  • Weekly strategy and optimization sessions
  • Custom AI system design and implementation
  • Cross-functional workflow automation
  • Full internal documentation and engineering handoff
Get started →

Common questions

  • "We tried ChatGPT. It didn't stick."
    That's the most common starting point I hear. Giving your team access to a tool without evaluating fit is like handing someone Salesforce and expecting pipeline management. I evaluate the right tools, implement the workflows, and build internal capability so AI gets used day-to-day, not abandoned after a week.
  • "Our team isn't technical."
    Most of the teams I work with aren't. AI literacy isn't about writing code. It's about knowing what to ask for, how to evaluate output, and where AI fits into the work you're already doing.
  • "How is this different from hiring an AI vendor?"
    Vendors sell you a platform and leave. I evaluate the right tools for your stack, implement them alongside your team, and build internal capability. When the engagement ends, your people own the workflows, the systems, and the knowledge.
  • "What size companies do you work with?"
    Primarily growth-stage companies (Series A–C) in SaaS, professional services, and regulated industries. Teams that are big enough to benefit from AI but don't have the headcount to hire a full-time AI lead.

Ready to figure out where AI actually fits?

30 minutes. No pitch deck. A real conversation about what's possible and what isn't.

Book a strategy call →