Shlomo Kashani · Deep-tech AI, quantum & strategic advisory
I work across a line most people sit on one side of — strategists who speak the language of the boardroom but have never shipped a system, and engineers who can build anything but cannot tell you whether you should. I build instruments to disprove my own hypotheses, and trust a theory only once it survives a genuine attempt to kill it.
I take on a small number of chief executives and deep-tech teams at a time — on the AI, quantum, and machine-learning decisions where getting it wrong is expensive. The fastest route to a serious conversation is a short, confidential note describing the decision in front of you and the timeline you are working to. I reply personally.
Where to start:
Selective advisory and technical-leadership engagements covering strategy, architecture, and delivery of machine-learning systems. Conducted under NDA.
Define your AI roadmap, evaluate build-vs-buy decisions, and architect systems that scale with your business.
Production-grade retrieval-augmented generation pipelines with proper evaluation, guardrails, and observability.
Tool-integrated reasoning agents that perform real tasks. Multi-step workflows with proper error handling.
Applied quantum computing and quantum machine learning for research, prototyping, and advanced computational workflows.
Applied analysis for complex geopolitical environments, dual-use technology strategy, and AI-enabled policy questions.
What I do
Production large language models, retrieval, and agentic AI — designed, built, and shipped into real use.
Applied quantum computing and QML — research, prototyping, and simulation, including frameworks tuned for Apple Silicon.
Judgement on high-stakes AI decisions made under uncertainty and time pressure, for boards and chief executives.
Background
I am a doctoral researcher in Defence and Strategic Studies and the practitioner who builds the systems — production LLM, retrieval, and agentic AI through to quantum computing — so I can hold the strategic frame and, in the same conversation, look under the hood of the specific claim in front of you.
The pace of AI capability is changing what institutions can build — and what they should refuse to build. The decisions are large, often irreversible, and made under time pressure, where moving too slowly is now as costly as moving recklessly.
Choose the model that fits your stage, timeline, and technical ambition.
Perfect for: Urgent decisions around your first AI hire, reviewing technical approaches, or pressure-testing a specific problem.
What you get: A focused one-hour session tackling your current AI challenge, with preparation based on the materials you share in advance.
Perfect for: Companies with existing AI initiatives that need a sharp assessment of the current state and a realistic plan for next steps.
What you get: A structured review, gap analysis against best practices, and a roadmap aligned with your team capacity and business goals.
Deliverables: Written assessment, prioritized roadmap, technology recommendations, and team-structure guidance.
Perfect for: Well-scoped projects where you need expert execution while building your team’s capacity to maintain the result.
What you get: Direct collaboration to deliver working systems such as RAG pipelines, computer vision workflows, multimodal products, or LLM fine-tuning.
Approach: Fixed-fee project or milestone engagements with knowledge transfer built in throughout.
Suited to: Organisations that need a senior technical voice on AI strategy and architecture without committing to a full-time hire.
Scope: Strategy, architecture review, hiring guidance, and selective hands-on delivery across LLM, RAG, and agentic systems.
Form: Monthly retainer, typically one to two days a week.
Perfect for: Teams that want to ship native macOS AI applications with full local performance.
What you get: End-to-end Apple Silicon app development with local and cloud model support, offline-first workflows, and release-ready packaging.
Scope: Desktop UX, model integration, performance optimization, packaging, and launch support.
Researcher, author, and advisor.
Selective engagements with institutional clients. Conducted under NDA.
Doctoral researcher in Defence and Strategic Studies at Missouri State University, working on strategic decision-making under conditions of extreme uncertainty and compressed time, with particular focus on international terrorism and AI wargame simulations. Author of two books spanning history and deep learning. Based in Jerusalem.
Frameworks, decision criteria, and hands-on guidance for teams planning their next AI move.
Reach out to discuss your AI roadmap, evaluate vendors, or get hands-on help shipping production systems.
From strategy to production. AI systems that work in the real world.