/ Advisory

AI consulting for decisions that actually matter.

I work directly with executive and technical leadership on AI strategy, architecture, engineering, and high-stakes technology decisions.

Simsar is intentionally principal-led. The person you meet is the person doing the thinking, with specialized engineering resources brought in only when the work requires them.

01

AI Strategy & Opportunity

Decide where AI actually belongs.

  • Executive AI strategy
  • Workflow / operating-model analysis
  • Use-case discovery and prioritization
  • Business-value and feasibility scoring
  • Build / buy / partner decisions
  • AI roadmap
  • Data readiness
  • Risk and governance architecture

Outcome A prioritized AI roadmap tied to business outcomes — not a catalog of experiments.

02

AI Architecture & Production

Move AI beyond the prototype.

Production AI is a systems problem. Models are one component; reliable systems require data, context, evaluations, security, observability, orchestration, integrations, deterministic controls, and clear human decision boundaries.

Outcome A production-ready architecture and delivery path — evaluations, integrations, controls — that your team can execute.

03

Fractional CTO & AI Leadership

Senior technical leadership without adding another full-time executive.

  • AI roadmap ownership
  • Architecture oversight
  • Engineering strategy
  • Vendor selection
  • Team / hiring design
  • Technical governance
  • Board communication
  • Modernization programs
  • AI adoption across engineering

Outcome Senior ownership of technology decisions, a roadmap the board understands, and a stronger team when the engagement ends.

04

AI & Technical Due Diligence

Understand what is actually behind the technology.

  • Architecture and scalability
  • Engineering organization
  • Codebase / technical debt
  • Security posture
  • Data assets and defensibility
  • Model / vendor dependencies
  • AI architecture and evaluation
  • Unit economics / inference cost
  • IP and build-vs-buy assumptions
  • Technology roadmap

Outcome Concise findings a board can act on: risk map, defensibility analysis, and recommended actions.

Does the company actually have an AI advantage — or an API call wrapped in marketing?

05

AI-Native Engineering

Software engineering is being rewritten.

Developer agents, architecture, testing and evaluation, SDLC, documentation, productivity, team structure, and technical governance. The cost of producing code is falling; the value of deciding what should be built, how it fits together, and whether it is correct is rising.

Outcome An engineering operating model — tooling, SDLC, evaluation, governance — built for the era when code is cheap and judgment is not.

AI Opportunity & Architecture Sprint

Use when Leadership needs prioritization and architecture.

Typical output Opportunity map, decision framework, target architecture, roadmap.

AI Production Sprint

Use when One workflow is selected; team needs a real implementation path.

Typical output Working pilot / production design, evals, integration plan, delivery backlog.

Embedded CTO / AI Advisor

Use when Senior ownership is missing or transitional.

Typical output Ongoing decisions, architecture, team/vendor guidance, executive/board communication.

Technical / AI Diligence

Use when Investment, acquisition, board review, major vendor decision.

Typical output Concise findings, risk map, defensibility analysis, recommended actions.

What are you trying to make real?

If you’re making an important decision about AI, technology, architecture, or engineering — or trying to move an ambitious idea into production — I’d be interested in hearing about it.

Discuss an AI initiative