A selection of engagements since 2020. Client names are used where permitted, anonymized otherwise. The outcomes are real.
The company's production platform was unreliable and expensive to operate. The existing architecture couldn't scale efficiently and lacked any machine learning or analytics capability. I led a full replatform into a scalable cloud-native solution, restructuring the engineering and infrastructure approach from the ground up.
Added ML and analytics capacity that did not previously exist, expanding what the product could deliver to its user base. Simultaneously drove cost discipline across the engineering and technology organization.
Outcome: Reduced overall engineering and technology run rate by 50% while expanding product capabilities. Platform reengineered into a reliable, scalable cloud-native architecture serving millions of active users with ML and analytics features that created new value for the business.
A hospitality SaaS company needed technology leadership to scale their engineering operation and prepare the company for growth. I led a re-platform effort to Node/React on AWS cloud-native architecture, scaled the engineering team 4x, conducted a thorough security evaluation that identified and resolved significant vulnerabilities, and implemented company-wide security policies.
Outcome: Technology guidance contributed to increased valuation and a successful acquisition in 2024. The company exited with a modern tech stack, a scaled engineering team, and security practices that withstood buyer diligence.
A business messaging platform needed data strategy and AI leadership. I developed and executed a generative AI go-to-market strategy, recruited senior technical resources, and architected a data analytics strategy using Snowflake, DBT, and Airbyte.
Outcome: 40% reduction in cloud data processing costs through architectural improvements. High-performing data engineering and data science teams established. Generative AI roadmap driving new product development.
The company had no AI leadership and was watching AI-native competitors enter their market. I joined as Head of AI to assess the competitive landscape, identify defensible AI use cases, and lead implementation across the product and operations teams.
Conducted comprehensive competitive intelligence research on AI-native lending platforms. Designed a prioritized AI roadmap spanning underwriting automation, customer communication, and operational efficiency. Led vendor evaluation and technology selection.
Outcome: Transitioned the company from zero AI capability to an active AI implementation program with clear priorities, defined success metrics, and engineering resources allocated. Competitive analysis identified specific threats from three AI-native entrants and informed the product response strategy.
A leadership performance firm serving Fortune 500 clients needed to integrate AI across their service delivery and build an AI-powered product platform. Their consultants were domain experts in human performance, not technologists. Generic AI training wasn't translating to changed behavior.
Designed and delivered role-specific AI enablement programs. Led the development of Neumi, a proprietary AI performance-coach agent that delivers personalized, real-time mindset cues and productivity nudges. The agent underpins their LMXP learning-experience platform.
Outcome: AI-powered coaching platform deployed to enterprise clients spanning pharma, tech, financial services, and consumer brands. The firm now positions AI-augmented performance coaching as a core differentiator, with the AI product driving a material portion of new client conversations.
A middle-market investment bank recognized that AI-native M&A platforms were emerging as competitive threats but lacked internal expertise to evaluate the implications or formulate a response. I joined as Fractional CTO and AI Advisor.
Analyzed generative AI use cases across the deal lifecycle: sourcing, valuation, diligence, and marketing. Evaluated AI-native competitors and their specific capabilities. Delivered a technology roadmap aligned to the firm's deal flow and team structure. Now directing AI tool implementation and building secure data infrastructure for confidential transaction data.
Outcome: The firm operates with a defined AI strategy and is implementing specific tools across the deal process. Technology and AI guidance now extends to portfolio companies and clients preparing for transactions.
A software development firm needed senior AI leadership to set enterprise-wide AI strategy and guide their engineering teams on AI initiatives, including voice agent architecture for national restaurant chains. I advise the C-suite on AI strategy, work directly with senior architects and engineers on system design, and support business development with AI solution positioning and technical due diligence.
Outcome: Enterprise AI strategy implemented spanning engineering, product development, and business development functions. AI governance framework established with technical standards and risk management protocols. Voice agent architecture and engineering guidance ongoing for restaurant clients.
A technology company serving government clients needed a fractional CTO to recruit an engineering team and design a generative AI solution for their product line. I recruited and hired the engineering team, designed and implemented the generative AI system, and provided technology leadership across the full product lifecycle.
Outcome: Generative AI solution delivered, enabling significant staff efficiencies and new data product offerings for government clients.
An automation company in advanced manufacturing needed AI advisory to design and deliver a computer vision product. I designed the product, identified and assembled the technology team, set up processes for image and annotation management, selected the ML tech stack, managed vendors, and directed delivery of an AI model using YOLO for real-time visual inspection.
Outcome: Production computer vision system delivered. The company gained a new AI-enabled product capability and an operational framework for managing vision model development.
Led the full lifecycle of technology strategy, product design, delivery, and operations for large IoT, SaaS, and quantitative ML systems in the energy industry. Managed a $2M+ monthly run rate with multiple quarters exceeding 50% profit margin. Scaled staff and delivery processes by 700%.
Designed systems that processed billions of rows of data across 30M+ end customers with hundreds of automated data pipelines. Reduced ML modeling timeframes from months to days and cut modeling costs by 90%.
Outcome: Production ML systems operating at scale across major utility clients. Built the data infrastructure, engineering team, and operational discipline that supported sustained business growth over nearly eight years.
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