Ben Sweet
AI Product Manager
I help organizations design and ship Agentic and GenAI products, from autonomous workflows to LLM-powered systems that deliver real business impact.
AI Product Manager
I help organizations design and ship Agentic and GenAI products, from autonomous workflows to LLM-powered systems that deliver real business impact.
I'm a Product Manager focused on Agentic AI and GenAI systems. I specialize in clarifying ambiguity and bridging AI strategy with hands-on technical execution.
With a decade in product and 4+ years designing and building enterprise AI platforms, I've shipped over 20 enterprise-scale AI products 0-to-1. I start with structured discovery: identifying high-value use cases, assessing data and technical readiness, and aligning AI capabilities with business objectives and ROI. I partner with executives to define AI vision, roadmaps, and governance frameworks, then translate that strategy into detailed PRDs specifying orchestration logic, prompt workflows, and success criteria.
My work spans Agentic AI, LLM + RAG pipelines, multi-agent orchestration, and predictive systems across SaaS, financial services, healthcare, legal tech, and government contracting. These products have delivered millions in ROI and 65–82% efficiency gains.
As co-founder of a GovTech AI platform, I owned product strategy and GTM, securing pilot commitments within 60 days. I also developed a proprietary Agentic AI Accelerator that validates technical feasibility and business value in under 48 hours per concept, cutting development time by 90%.
The AI products featured here represent my consulting and venture work: from autonomous workflow engines and intelligent decisioning systems to conversational AI and recommendation platforms. I lead cross-functional teams from discovery through deployment and monitoring.
My approach blends technical depth with a human-centered mindset and global lens. Shaped by experience in 60+ countries and fluency in Portuguese and Spanish, I bring wide-ranging perspectives to design, collaborate effectively with distributed teams, and build AI that resonates across varied users and contexts.
End-to-end AI product specifications with strategic clarity and technical depth.
Agentic AI system for investment analysis, combining risk scoring with explainable AI for portfolio decisions.
Internal AI platform with contextual code intelligence, technical debt scoring, cross-repo impact analysis, and automated knowledge synthesis.
Multi-agent LLM orchestration for financial advisors, with Monte Carlo simulations and compliance guardrails.
Hybrid RAG + fine-tuned classifier system for automated RFP analysis, requirement extraction, predictive analytics and bid/no-bid recommendations.
AI-powered workforce optimization with semantic skill matching, demand forecasting, and bias-aware recommendations for enterprise staffing at scale.
NLP-powered clause extraction and risk classification using Legal-BERT and transfer learning from CUAD dataset.
AI-driven test generation with self-healing locators and flaky test detection for enterprise QA workflows.
Predictive ML scoring with LLM-powered personalization for real estate lead qualification and outreach automation.
Note: Client names and proprietary details have been modified for confidentiality.
Functional applications demonstrating AI product thinking in action.
First-pass AI solution scoping and market viability assessment. Enter your concept and get instant analysis of demand signals, differentiation, build complexity, regulatory risks, and responsible AI considerations.
Production-grade multi-agent workflow engine that coordinates specialized AI agents across complex task chains. Features dynamic routing, context-aware handoffs, parallel execution, and human-in-the-loop checkpoints for high-stakes decisions.
Proprietary system that transforms structured product prompts into production-ready AI platforms in hours, not weeks. Automates architecture generation, component scaffolding, and integration wiring while validating technical feasibility and business value in under 48 hours per concept.
Comprehensive frameworks for operationalizing AI across organizations.
The end-to-end process I use to transform vague business challenges into production AI. Covers problem diagnosis, feasibility assessment, LLM/ML/software decision frameworks, technical framing, iterative de-risking, and scaling strategy.
How I operationalize AI governance in enterprise products. Covers evaluation datasets, hallucination taxonomies, LLM regression testing, layered guardrails, and alignment with NIST AI RMF, ISO 42001, and EU AI Act requirements.
End-to-end guide covering agent architecture patterns, orchestration frameworks, governance, continuous learning, and production deployment.
Strategic framework for mature organizations adopting AI; covering readiness assessment, build vs. buy decisions, and change management.
Practical techniques for effective LLM prompting, covering chain-of-thought, few-shot patterns, structured outputs, and evaluation methods.