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✦ Product Leader & AI Researcher

Alok
Abhishek

Product leader and independent AI researcher building AI native Agentic Enterprise SaaS products and advancing the field through research and thought leadership.

✦ About

Alok Abhishek

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.

More About Me

✦ Experience

Career Highlights

15+Years of Product Leadership

Defining vision and strategy, achieving product-market fit, and scaling enterprise data and AI SaaS platforms

$10M++ARR per Product

Scaled multiple enterprise platforms from concept to eight-figure recurring revenue, achieving product-market fit

ThousandsEnterprise Customers

Drove adoption of AI and cloud platforms across thousands of large and global enterprise customers

11Research & Industry Publications

arXiv, MIT Science Policy Review, IEEE Computer, and leading industry journals

✦ Expertise

Core Capabilities

Product Strategy

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.

AI Research

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.

AI/ML Productization

Designing, building, and shipping AI-powered products. Operationalizing classical ML, LLMs, RAG, and agentic workflows with a focus on scalability, performance, and enterprise adoption.

Data Platforms & APIs

Architecting multi-tenant medallion data platforms, developer-first APIs, and secure data services for enterprise SaaS. Expertise in pipelines, governance, and modern lakehouse architectures.

✦ Publications

Industry Thought Leadership

Articles on AI, data platforms, product innovation, and technology strategy.

Industry Article

An Integrated Data and AI Strategy For Transformational Leadership In Law Firms

ILTA Peer to Peer Magazine · 2025

Strategic framework for law firm leaders to implement integrated data and AI initiatives that drive operational efficiency and competitive advantage in the legal industry.

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Industry Article

How to Avoid Common Pitfalls in AI-Focused Products

IEEE Computer Society · 2024

Identifies and provides solutions for the most common challenges faced when developing AI-focused products, from technical implementation to user experience design.

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Industry Article

AI’s Potential: Creating a Framework for Driving Product Innovation

TechStrong AI · 2024

Explores how organizations can harness AI’s transformative potential by establishing structured frameworks that drive meaningful product innovation and competitive advantage.

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✦ Published In

MIT Science Policy ReviewIEEE Computer SocietyarXivAWS Partner NetworkILTA Peer-to-PeerDATAVERSITYTechStrong.ai

✦ Research

Published Research

Published research in AI fairness, bias evaluation, and governance frameworks.

AcademicPreprintPublished

SHARP: Social Harm Analysis via Risk Profiles for Measuring Inequities in Large Language Models

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.

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AcademicPreprintPublished

BEATS: Bias Evaluation and Assessment Test Suite for Large Language Models

Alok 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.

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AcademicJournal ArticlePublished

Data and AI Governance: Promoting Equity, Ethics, and Fairness in Large Language Models

Alok 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.

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✦ Talks & Presentations

Talks & Presentations

Industry presentations on product management, AI, trustworthy AI, and data platforms.

Aug 24, 2025·San Francisco, CAFeatured

When Bias Goes Viral: Protecting Your Brand from Biases in Generative AI

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.

Apr 28, 2025·VirtualFeatured

How Aderant Builds Trustworthy AI

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.

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Interested in Having Me Speak?

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✦ Let’s Connect

Interested in Collaborating?

I engage with product leaders, founders, and researchers on open discussions around AI-driven products, data platforms, and applied AI strategy.

Get In Touch

alok@alokabhishek.ai · San Francisco, CA