Building 0-to-1 products. Discovery to profitable launch.
Six-plus years shipping AI/ML platforms, IoT products, and GTM-ready launches. I take ideas from customer discovery through production deployment and go-to-market. And I prototype them myself.
Selected work
Products taken from discovery through launch, and the revenue, scale, and systems they produced.
Fleet Safety GenAI
Fleets generate enormous dashcam-event telemetry, but legacy tools dump it as lists for a manager to read, interpret, write up, and never measure. I built a browser-demoable system that replaces that load with a governed agentic pipeline: pattern detection, coaching drafted from the driver's real events, guardrails, human approval, and outcomes measured against a matched control cohort.
SQL · driver_score_v14
AI Dashcam Platform Launch
Led end-to-end GTM of a next-gen AI dashcam across 5 concurrent workstreams and 35+ engineers: positioning, tiered subscription pricing, competitive differentiation, and field enablement.
ML Risk Scoring & Data Platform
Drove the ML roadmap with data science to design compound risk scoring across 7 vision models, edge + cloud. Built the analytics layer linking model outputs to safety outcomes and commercial KPIs.
Developer Platform & Partner Ecosystem
curl -H "Authorization: Bearer ••••••" \ https://api.surfsight.net/v1/devices/AI14/events
Established the API platform powering integrations for 50+ partners. Defined a tiered partnership model, OAuth 2.0 flows, and a revenue-share framework. Designed the partner portal from wireframes to production.
Partner Channel & Commercial Strategy
Designed the revenue-share model and tiered pricing that scaled the partner ecosystem 10→50+ integrators. Built co-sell motions and enablement now adopted company-wide, plus carrier distribution across 5 continents.
The AI Lab
A self-hosted AI lab running on my own hardware. I don't just spec AI products, I operate them.
Local-first, self-hosted. A Mac Mini M4 Pro in my living room runs the lab: this site, a private Git server, a photo library, the knowledge-base hub, dashboards, and the AI gateways, all behind Cloudflare tunnels. 13 local models are served through Ollama; cloud models are budget-gated at ≤$1/day and used only where frontier quality pays.
The 22 builds were all designed and built end-to-end here. Some run as always-on services and scheduled agents (screenshot cataloging every morning, exec reports every two hours, hourly git sync), others ship as desktop and iOS apps or live on as working prototypes. CrabCake, the multi-agent gateway, is one of them. Every operating decision (which model for which task, local vs. cloud, memory, budget) shapes how I reason about AI products in my day job.
The 22 builds
built end-to-end · drag sidewaysProduct concepts
AI-native prototypes, designed and built end-to-end. These are live, not screenshots.
Account Truth Layer
Enterprise account intelligence with receipts: scattered account signals become a queryable, citation-backed dossier. Grounded Q&A where every answer links to its source, drill-in dashboards, SSO and audit-ready by design.
Northwind Logistics
Bounded-Autonomy FinOps
An agent investigates spend anomalies through an Observe → Investigate → Reason → Propose → Verify loop, but every action passes a permission envelope (Auto / Approve / Locked) and a customer-tunable ROI gate. Failed investigations stay on the ledger by design: trust comes from showing the misses.
Side project
Keeping product instincts sharp on a real consumer build.
Tiny Wins, parenting made rewarding
An iOS app that helps parents encourage positive behaviors through a visual, star-based reward system. Built end-to-end to keep my product instincts and shipping muscles sharp.
- Discovery: found a gap in parenting apps for positive-reinforcement tracking via competitive analysis
- UX: flows and hierarchy optimized for quick, one-handed parent interactions
- Analytics: behavioral pattern insights to guide coaching
Background
Product leader based in Israel with 6+ years building and shipping B2B SaaS from 0-to-1 startup through acquisition to scale. Today I lead product execution on a $50M ARR AI/ML platform serving 500K+ subscriptions, delivering technical launches and commercial releases that drive revenue growth.
I bridge technical depth and GTM execution: designing ML products, structuring commercial models, enabling sales teams, and launching to market. The platform & partner-ecosystem strategy I built became a primary growth vector over time.
Early employee #8
0-to-1 startup phase
50K subscriptions
14-month rapid scale
Acquired by Lytx
Joined a $500M+ revenue leader
$50M ARR platform
TAM → PM II → Senior PM
Domains
- AI/ML products
- IoT & cloud platforms
- B2B SaaS
- Fleet telematics
Leadership
- Product growth & GTM
- Commercial strategy
- Partner ecosystems
- Cross-functional teams
Technical
- System architecture
- ML pipelines & API design
- Edge/cloud, AWS
- SQL, Python, LLM agents
Working together
The questions a hiring manager usually asks first.
What roles are you looking for?
Do you actually build, or just write specs?
Where are you based, and are you open to remote or relocation?
What's your domain sweet spot?
Can I see a full resume?
Need a 0-to-1
product mind?
I'm open to product leadership opportunities and happy to talk strategy, platform architecture, or GTM. No pitch required.
Start a conversation