Move from AI experiments in engineering to AI as operating model. I help business leaders identify high-value use cases, build governed solutions, and drive adoption across business, operations, and management workflows. Built for regulated environments where AI must be practical, safe, and measurable.
Intelligent Systems degree from Afeka School of Engineering. The math behind the magic, not the marketing.
Building AI-powered apps, websites, and automation for myself and clients. Heavy daily use of Claude Code, RAG pipelines, agentic workflows. I don't just advise, I operate.
10+ years inside fintech, digital banking, and adtech. Built credit and risk infrastructure that processed 500K+ loan requests. I know what "safe AI" actually has to satisfy.
Led R&D as it tripled in size. I understand why most transformation efforts fail at the adoption layer - and how to design around it.
Worked across product, engineering, risk, legal, security, and compliance. I translate between business owners and the people who have to make AI actually work.
I don't sell vision decks. I deliver use cases your team can pilot in 6 weeks, with metrics that prove value or kill the project early.
2-4 weeks. From $5,000.
Identify and prioritize AI use cases across your organization. Output: a written portfolio of 8-15 use cases ranked by business impact, feasibility, data readiness, and control requirements. Decision-ready for your leadership team.
4-8 weeks. From $15,000.
Take one validated use case from idea to working pilot. Includes business case, success metrics, vendor evaluation, pilot launch, change management, and go/no-go review. You get a working tool plus a playbook to repeat.
3-6 months. From $8,000/month.
Embedded as your part-time AI Transformation Lead. Owns use case pipeline, partners with business owners, leads change management, builds governance playbooks, and reports outcomes. The seniority of a full-time hire at the cost of fractional.
Identified, validated, and prioritized AI opportunities across business, operations, and management workflows. Scored by impact, feasibility, data readiness, and control requirements.
Each pilot has a written business case with success metrics: productivity, cycle time, quality, decision support, customer impact. Outcomes tracked from launch through value measurement.
Discovery → pilot → production rollout → adoption → measurement. Clear gates between stages. No PoC graveyard.
Communication plans, enablement programs, and adoption playbooks. The work that makes the difference between "tool deployed" and "tool used."
Practical guides for privacy, security, model risk, and escalation. Built for regulated environments. Reviewed with risk, legal, and compliance.
Continuous evaluation of generative AI, copilots, RAG, agentic workflows, and emerging vendors. The right tools for your operating environment, not the trending ones.
50-2,000+ employees. Past the founder-running-everything stage. Has business, operations, product, data, engineering, risk, legal, and compliance functions that need to coordinate.
Banking, fintech, insurance, healthcare, payments, telecom. Where AI deployment has to satisfy legal, compliance, and model-risk requirements - not just product requirements.
Your devs are using Claude Code, Copilot, or similar. Productivity has shifted. Now the question is how to extend that capability to business, operations, and management workflows without losing control.
You have AI pilots that work in demos but never make it to production. Or production systems with low adoption. The gap is usually change management, governance, and use-case selection - not the technology.
I'm a single operator who has shipped AI products. They are teams of analysts who haven't.
Big 4 strategy engagements typically deliver a 200-page deck and an organizational chart. My deliverables are working pilots, written playbooks, and trained business owners. Faster, cheaper, more honest. The trade-off: I am one person, so I'm right for organizations that want focused expertise on their actual constraints, not a 12-person team running parallel workstreams.
I worked inside regulated tech for a decade - I know what risk, legal, and compliance teams actually need before they sign off. Every use case I prioritize is scored on control requirements, data sensitivity, and escalation paths. My playbooks are structured to give risk and legal teams what they need to approve work, not to argue around them.
Concretely: privacy impact assessments, model-risk classification, auditable logging requirements, fallback and escalation procedures, vendor due diligence templates. All adapted to your regulatory context.
Typically 2-4 weeks from start to delivery. Week 1: leadership interviews + business workflow mapping. Week 2: data readiness review + workshop with business owners. Week 3-4: synthesis, prioritization, written report and debrief.
Cross-functional coverage by design. Typically 8-15 people: business owners (the people whose work might change), operations leaders, data and engineering leadership, plus risk, legal, security, and compliance representatives. The point is that I leave with an aligned view across functions, not just from one perspective.
Vendor-neutral by principle, hands-on with most major platforms. Daily user of Claude (Anthropic), heavy practical familiarity with OpenAI/GPT, Microsoft Copilot, Google Vertex AI, RAG pipelines (vector databases, embeddings, retrieval orchestration), agentic workflows, workflow automation (Make, Zapier, n8n), and bespoke integrations. I recommend the right tool for your environment, not the one I want to sell.
Yes. 3-6 month engagements at 1-3 days per week. I embed with your leadership team, own the use case pipeline, partner with business owners through pilot and production, lead enablement, and report outcomes to executives. Right for organizations that need senior AI transformation leadership but aren't ready to make a full-time hire - or want to operate while they search for one.
Modern consulting economics, not a discount on quality.
One operator with deep pattern recognition. AI accelerates research and synthesis. No partner approval cascades. No 12-person team pricing model.
If you've been quoted $200K-500K for a 6-month AI strategy engagement, ask what's actually being delivered that justifies the price. My deliverables are working pilots, written playbooks, and trained business owners - not slideware.
Yes - free 30-minute call. We discuss your context, where you are on the AI maturity curve, and what engagement model would fit. If it's not the right time or right fit, I'll tell you that.
Start with a free 30-minute call. No pitch, no pressure - just a conversation about where your organization is and where AI can move the needle.