Skip to content
Ali Zarreh

Advisory & consulting

Hands-on help with the AI decisions that matter.

Whether you need a system built, an existing one reviewed, or an independent technical voice at the table, I work directly with your team — from framing the problem to production code and the governance around it.

Discuss it with me Ways to work together

Projects & consulting

How I can help

Fixed-scope reviews, build engagements and workshops, in any industry.

01 Fixed scope

AI strategy & readiness assessment

Where should AI actually go in your business — and where should it not?

A fixed-scope review of your use cases, data and platform. You get a ranked opportunity portfolio, the risks and governance gaps for each, a build/buy/partner view, and a roadmap your team can execute.

You get

  • Ranked use-case portfolio with value and feasibility
  • Data, platform and team readiness
  • Risk, governance and evaluation gaps
  • Build / buy / partner recommendation and roadmap
02 Build engagement

Agentic AI: from prototype to production

Turn a promising demo into a system you can operate, audit and afford.

Design and build agentic workflows with the parts most pilots skip: evaluation from day one, guardrails in the graph rather than in the prompt, human review where the stakes are high, cost and latency budgets, and observability.

You get

  • Architecture and decision log
  • Working workflow with evals, guardrails and human review
  • Cost, latency and quality dashboards
  • Runbook and hand-over to your team
03 Build engagement

Forecasting & optimization

Predictions are only useful when they turn into decisions people adopt.

Demand, labor, capacity, pricing, markdown and space problems — machine-learning forecasts paired with mathematical optimization, built on the same patterns that run planning across hundreds of stores.

You get

  • Backtested, leakage-audited forecasts
  • Optimization model with business guardrails
  • Explainability for planners and merchants
  • MLOps pipeline on your cloud (GCP, AWS, Databricks)
04 Fixed scope

AI evaluation & guardrails review

An independent look at an LLM or agent system before it — or your customers — find the failure modes.

A structured review of an existing system: evaluation design, grounding and citation checks, prompt-injection and data-leak exposure, human-oversight points, monitoring and regulatory exposure. You get prioritized findings and fixes.

You get

  • Failure-mode and risk assessment
  • Evaluation set and metrics you can keep running
  • Red-team findings with severity
  • Prioritized remediation plan
05 Workshop

Team enablement & workshops

Hands-on generative and agentic AI for engineers, and clear-eyed briefings for leaders.

Workshops shaped by teaching hundreds of practitioners in Johns Hopkins University and UT Austin programs: retrieval, multi-agent systems, MCP, evaluation and guardrails — on your use cases, not toy examples.

You get

  • Workshop tailored to your stack and use cases
  • Exercises and reference implementations
  • Executive briefing option

Advisory

Board and fractional advisory

Ongoing counsel for founders, executive teams and boards on AI strategy, architecture and governance.

Advisory board seat

Ongoing strategic counsel to founders and boards on AI and technology.

Fractional AI advisor

Regular working sessions with the leadership team, plus on-call review of key decisions.

Executive & board briefing

A focused session that gives directors a clear, hype-free picture of AI opportunity and risk.

Technical due diligence

A scoped assessment of an AI product, platform or team ahead of an investment decision.

Current role

Aug 2026 – Present · Remote · United Kingdom

Syne Life

Health Tech Board Advisor — AI & Technology

syne.life

Serve on the advisory board of a recovery-science wellness startup, advising the founding team on technology strategy, platform architecture and AI/ML strategy across the product.

Syne pairs a nightly recovery practice with an app that turns a member's own wearable data (Oura, Whoop or Apple Health) into one clear action each evening.

What I bring

Where I add the most value

AI strategy & roadmap

Separate defensible value from demo-ware, and sequence AI investments against the business plan.

Architecture & build-vs-buy

Pressure-test platform choices, vendor claims and the true cost of operating models in production.

AI risk & governance

Evaluation, human oversight, auditability and model-risk practices that hold up to customers, regulators and frameworks such as the NIST AI RMF.

Technical due diligence

An independent read on an AI product or team for investors and acquirers: what is real, what is fragile, what it will take.

Data & platform strategy

The data foundations, MLOps and cloud choices that decide whether AI scales past the first use case.

Team building

Hiring senior AI talent, structuring data-science teams and mentoring technical leaders.

Operator, not commentator

I run AI programs inside a large enterprise today — architecture, evaluation, teams and executive stakeholders — so my advice comes from what actually ships.

Risk is my home turf

A Ph.D. in game-theoretic security and a portfolio of AI systems for credit, clinical and compliance decisions: I think about failure modes before features.

Strategy that survives contact

I translate between board-level questions and engineering reality — cost, timelines, data readiness and what the model can and cannot do.

Process

How we'd work together

  1. 1

    Intro call

    Thirty minutes to understand the decision you are trying to improve and whether I am the right fit.

  2. 2

    Scoping note

    A short written scope: the problem, how success is measured, the risks, and what I need from your team.

  3. 3

    Build in the open

    Regular working demos and evaluation results from the first iteration — no big reveal at the end.

  4. 4

    Hand-over

    Documentation, runbooks and a trained team, so the system keeps working after the engagement ends.

How I work and advise

  • Start with the decision, not the model

    Every system I build begins with the decision it improves and the person who owns it.

  • Evidence before scale

    Evaluation, backtests and baselines come first, so we know it works before we bet on it.

  • Humans own high-stakes calls

    AI assembles evidence and drafts; accountable people decide where the stakes demand it.

  • Auditable by default

    Every number, claim and citation can be traced back to its source and checked by code.

  • Honest limits

    I publish where a system falls short. Knowing the failure modes is what makes it safe to use.

FAQ

Common questions

How do you take on engagements?

Selectively. I take a small number of advisory and project engagements that do not conflict with my current role, and I am upfront about capacity from the first conversation.

Which industries do you work with?

Mostly regulated or high-stakes settings: retail and supply chain, financial services, insurance and healthcare. The common thread is decisions that need to be explainable and auditable.

Do you work remotely?

Yes. I am based in San Antonio, Texas, and work remotely across US time zones. Workshops and kick-offs can be on-site.

Will you sign an NDA?

Yes. Anything you share during scoping stays confidential, and I never use client material in public work.

What do you need from us?

A problem owner, access to the relevant data and people, and an agreed measure of success. Everything else we can work out together.

Open to consulting, advisory roles and select projects.

Have a project, a decision or a board seat in mind?

Tell me what you're working on. If AI is the right tool, I'll help you get it right; if it isn't, I'll tell you that too.

Discuss it with me

Search zarreh.ai