Pini Pur

• Set and drive AI product, platform, and infrastructure strategy across 10+ portfolio companies in data, security, and enterprise automation, owning system architecture, product direction, and enterprise go-to-market readiness.
• Lead foundational and scaling decisions across model selection, inference and data architecture, cost, latency and quality tradeoffs, reliability, and governance, supporting enterprise and regulated deployments.
• Own and shape multi-quarter product roadmaps, early monetization strategy, and go-to-market sequencing to accelerate enterprise adoption, revenue traction, and repeatable growth.

• Owned the end-to-end product vision and execution for AI-powered voice and conversational CX experiences, translating LLM, NLU, and real-time ML into intuitive, reliable workflows for agents and customers.
• Defined and delivered a multi-year AI and ML roadmap across cloud-native platforms, balancing experience quality, latency, scalability, and regulatory constraints.
• Led the design and launch of a low-code and no-code conversational AI and knowledge platform, shipping 20+ features across agent assist, voice intelligence, and multimodal retrieval, driving +20 percent CSAT and −15 percent AHT.
• Led collaboration across Design, Product, Engineering, and Data Science to evolve core AI workflows, define experience quality metrics (latency, time to resolution (TTR), model accuracy), and scale enterprise-ready AI capabilities into production.
• Made high-judgment product decisions in ambiguous problem spaces, balancing user needs, platform constraints, and long-term scalability.

• Led the productization of AI and data capabilities for a real-time supply-chain transaction system, embedding pricing, routing, and demand forecasting into core workflows to improve decision accuracy and operational efficiency.
• Led global Product, Engineering, and Data Science teams to build cloud-native data and ML pipelines combining transactional and unstructured data (contracts, events, signals) to enable real-time analytics and decisioning.
• Designed analytical data models and data governance frameworks to support predictive and operational workloads, improving query performance and insight latency.
• Shipped production predictive models that cut routing costs 12% and improved forecast accuracy 25%, driving Fortune 500 adoption and multi-year data investments.

• Founded and scaled three AI-first startups across MarTech, collaboration platforms, and sports technology, leading to two successful acquisitions.
• Defined and executed end-to-end product strategy, including roadmap ownership, rapid prototyping, and deep AI/ML integration across multiple product lines.
• Executed GTM strategy that drove early enterprise adoption and scaled ARR from zero to $5M+ within 18 months.
• Owned full P&L, product vision, GTM strategy, and fundraising, securing venture capital, strategic partnerships, and leading acquisition negotiations.
• Recruited, built, and scaled cross-functional teams across product, engineering, and GTM, balancing startup velocity with enterprise-grade execution and operational rigor.

• Directed strategy and execution of ML-driven demand forecasting and dynamic pricing for a multi-billion-dollar hospitality portfolio (150+ properties).
• Built and scaled data pipelines and predictive analytics workflows (NLP on transcripts, reviews, social signals) to surface proactive insights for enterprise pricing decisions.
• Partnered with GTM and marketing leadership to operationalize model outputs, enabling real-time pricing agility, stronger market responsiveness, and measurable revenue lift.

• Led predictive and attribution modeling to measure how online marketing drove offline sales, partnering with Google, Facebook, Mastercard, and Visa to combine engagement and transaction data for real-time cross-channel performance optimization.

• Developed data pipelines and signal-processing features for computer-vision models, and built evaluation tools to support continuous testing and retraining of perception models.