Expert AI and ML Consulting Services
About East Village AI™
Founded in 2021, East Village AI helps clients turn modern AI into working products and measurable outcomes—especially where the problems are complex and the constraints are real. We support both startups and established organizations, from strategy through implementation: integrating LLMs, machine learning, and decision systems into existing data platforms, software, and workflows.
How We Engage
Engagements are flexible by design. I can embed directly with your team—coaching, unblocking, and accelerating delivery while building internal ownership—or assemble a small bench of trusted engineers and domain experts when specialized depth is needed. The goal is consistent: ship reliable capabilities, earn adoption, and translate genuinely novel work into durable IP.
Analytics, AI,
and all things in between.
Chris Clarke, Ph.D.
I turn hard problems into working AI. From enterprise-grade LLM and ML integrations to novel algorithms and decision workflows, I help teams ship systems that are secure, evaluated, and actually adopted—often in high-complexity domains where correctness matters.
Education
Ph.D., cum laude — Theoretical Physics, New York University
Thesis: Time Dependent Generalization of Floquet Method Applied to Pulsed Interactions
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M.S. — Computational Physics, New York University, New York
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B.A. — Applied Physics, Eng. Emphasis, UNC, Greeley, CO.
Thesis: Construction of a Cylindrical Radiofrequency Ion Trap​
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I bring a physics mindset to AI—theory-informed and pragmatically applied—modeling systems that evolve over time, behave nonlinearly, and resist simple answers. That perspective continues to shape both my applied AI work and ongoing independent research, with a consistent bias toward building solutions that hold up in production—especially in domains where ambiguity is unavoidable and correctness, not flash, is the real requirement.
In practice, this has meant designing AI capabilities that sit inside real products and operations: agent-driven decision workflows, retrieval-based knowledge systems, forecasting and optimization tools, and hybrid ML/LLM systems embedded in enterprise platforms. Much of this work lives in high-complexity environments—legacy data, competing objectives, security constraints—where novelty must translate into something durable.
Alongside delivery, I work closely on invention capture and IP strategy, helping ensure that genuinely new technical approaches—often emerging during integration rather than ideation—are identified, shaped, and protected as long-term assets.
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About
I’m an independent AI researcher and integration lead focused on deploying modern AI in production environments. I help teams move from promising prototypes to reliable, auditable systems—combining LLMs, machine learning, and decision logic with existing data platforms and workflows.
My work spans agentic AI, retrieval-augmented systems, forecasting and optimization, and the infrastructure and evaluation practices needed to scale these capabilities responsibly. I’ve led cross-functional efforts from strategy through delivery and frequently advise on intellectual-property strategy, including invention discovery, patent capture, and long-term portfolio development.
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