✦ Product Leader & AI Researcher
Product leader and independent AI researcher building AI native Agentic Enterprise SaaS products and advancing the field through research and thought leadership.
✦ About
I'm a product leader and independent AI researcher with over 15 years of experience building and scaling enterprise SaaS and AI platforms. I have led multiple zero-to-one initiatives from concept to product-market fit, with products reaching eight-figure ARR and broad enterprise adoption across global markets.
I define product vision and strategy across the lifecycle, driving innovation by framing customer problems, shaping monetization models, and guiding technical execution through production scale. Combining strategic product leadership with deep AI fluency, I build high-performing autonomous agents, AI services, ML models, and secure data platforms for regulated enterprises. In close collaboration with customers, design, and engineering teams, I translate complex operational challenges into scalable products that deliver durable business value.
My research focuses on responsible AI, risk evaluation, and governance. I developed BEATS, a benchmark for assessing bias and structural fairness in large language models, and SHARP, a risk-based framework for analyzing and quantifying social harm in AI systems. My work has been published in arXiv, MIT Science Policy Review, and IEEE.
I operate as a strategy-to-production builder, translating customer insight into product vision, defining economic models, designing scalable architectures, and shipping AI systems that deliver measurable business outcomes.
✦ Experience
Defining vision and strategy, achieving product-market fit, and scaling enterprise data and AI SaaS platforms
Scaled multiple enterprise platforms from concept to eight-figure recurring revenue
Drove adoption of AI and cloud platforms across global enterprise accounts
arXiv, MIT Science Policy Review, IEEE Computer, and leading industry journals
✦ Expertise
Advancing the field through independent research on bias, fairness, hallucinations, and systemic risks in LLMs. Author of BEATS (arXiv), SHARP (arXiv), and contributor to MIT Science Policy Review.
Designing, building, and shipping AI-powered products. Operationalizing classical ML, LLMs, RAG, and agentic workflows with a focus on scalability, performance, and enterprise adoption.
Architecting multi-tenant medallion data platforms, developer-first APIs, and secure data services for enterprise SaaS. Expertise in pipelines, governance, and modern lakehouse architectures.
Full-spectrum product management — from vision and roadmap to requirements, pricing, and go-to-market. Skilled in design thinking and human-centered design to deliver differentiated products.
✦ Publications
Articles on AI, data platforms, product innovation, and technology strategy.
arXiv preprint · 2026
Introduces Social Harm Analysis via Risk Profiles (SHARP), a framework for multidimensional, distribution-aware evaluation of social harm in LLMs. SHARP models harm as a multivariate random variable and integrates explicit decomposition into bias, fairness, ethics, and epistemic reliability with a union-of-failures aggregation.
Read PaperMIT Science Policy Review · 2025
Covers approaches to systematically govern, assess, and quantify bias across the complete lifecycle of machine learning models. Building on the BEATS framework, discusses data and AI governance approaches to address Bias, Ethics, Fairness, and Factuality within LLMs.
Read PaperarXiv preprint · 2025
Introduces BEATS, a novel framework for evaluating Bias, Ethics, Fairness, and Factuality in Large Language Models. Presents a bias benchmark measuring performance across 29 distinct metrics spanning demographic, cognitive, and social biases, as well as ethical reasoning, group fairness, and factuality.
Read Paper✦ Research
Published research in AI fairness, bias evaluation, and governance frameworks.
Alok Abhishek · arXiv preprint · 2026
Introduces Social Harm Analysis via Risk Profiles (SHARP), a framework for multidimensional, distribution-aware evaluation of social harm in LLMs. SHARP models harm as a multivariate random variable and integrates explicit decomposition into bias, fairness, ethics, and epistemic reliability with a union-of-failures aggregation. Application to eleven frontier LLMs reveals that models with similar average risk can exhibit more than twofold differences in tail exposure and volatility.
Read PaperAlok Abhishek · arXiv preprint · 2025
Introduces BEATS, a novel framework for evaluating Bias, Ethics, Fairness, and Factuality in Large Language Models. Presents a bias benchmark measuring performance across 29 distinct metrics spanning demographic, cognitive, and social biases, as well as ethical reasoning, group fairness, and factuality. Empirical results show 37.65% of outputs from industry-leading models contained some form of bias.
Read PaperAlok Abhishek · MIT Science Policy Review · 2025
Covers approaches to systematically govern, assess, and quantify bias across the complete lifecycle of machine learning models. Building on the BEATS framework, discusses data and AI governance approaches to address Bias, Ethics, Fairness, and Factuality within LLMs, suitable for practical real-world applications enabling rigorous benchmarking prior to production deployment.
Read Paper✦ Talks & Presentations
Industry presentations on product management, AI, trustworthy AI, and data platforms.
San Francisco Bay Area Professional Chapter of the ACM (Association for Computing Machinery)
Explored the critical risks of bias in generative AI systems and provided practical strategies for organizations to protect their brand reputation while deploying AI technologies responsibly.
Aderant Studio A
In-depth discussion about Aderant's approach to building trustworthy AI systems for legal professionals, covering technical implementation, ethical considerations, and real-world deployment strategies.
✦ Reading
Curated reading recommendations across product management, leadership, technology, and personal development.
James Clear
Stephen R. Covey
Chris Voss
Abhijit V. Banerjee & Esther Duflo
✦ Blog
Insights on product management, AI/ML, data platforms, and technology strategy.
FeaturedA deep dive into building an AI-powered food recommendation system using generative AI, vector embeddings, and modern ML techniques for personalized user experiences.
Learn how to leverage ML techniques like clustering and sentiment analysis to understand user feedback at scale and build more empathetic products.
Comprehensive guide to building developer-first SaaS products with API-centric design, focusing on adoption strategies and monetization models.
✦ Let’s Connect
I engage with product leaders, founders, and researchers on open discussions around AI-driven products, data platforms, and applied AI strategy.
Get In Touchalok@alokabhishek.ai · San Francisco, CA