A six‑year wait, by design.

To everyone following this work,

In 2019, I learned that microorganisms growing on conductive surfaces could generate electricity from wastewater, produce hydrogen from organic waste, and synthesize valuable chemicals from carbon dioxide. I learned that these microbial electrochemical systems could, in principle, transform the economics of clean water, renewable energy, and sustainable chemistry simultaneously. I was smitten — not just with the elegance of the science, but with the sheer scope of what it could solve.

I also learned why it hadn’t solved those problems yet.

The research was scattered across more than ten thousand papers in hundreds of journals. Experimental results were reported in incompatible formats, with critical parameters missing or buried in supplementary files. Researchers in São Paulo were repeating work done in Beijing because they never found the paper, or found it but couldn’t compare the data. The field didn’t have a science problem. It had an information infrastructure problem.

I recognized it immediately because building information infrastructure is what I’ve spent my career doing — from behavioral health to agricultural technology to circular construction to biodiversity monitoring to humanitarian response. Every role sharpened the same conviction: that the most leveraged thing you can build is the infrastructure that lets an entire community work from shared, structured, accessible truth.

I knew by 2019 that MES needed exactly this. I also knew the technology to build it didn’t yet exist. The AI models available at the time couldn’t reliably extract quantitative parameters from the messy, heterogeneous text of scientific papers — not at the accuracy or cost that a real platform would require. So I did something that felt counterintuitive for someone obsessed with a problem: I waited. I studied the field deeply. I mapped its data landscape, its institutional players, its commercial trajectories, and its failure modes. And when the models finally matured to the point where I could build what I’d been designing in my head for years, I built it.

The path opened during my Master of Science in Sustainability at the City College of New York. I went searching for expertise on microbial electrochemical systems, and none of the faculty in the architecture school could help me — they pointed me to the bioprocess engineering department. That’s where I met Dr. Lane Gilchrist, who now serves as our Chief Scientific Officer. The fact that I am not a bioelectrochemist — not a biologist, not an electrical engineer, not a chemist — is not a liability. It is precisely what made this vision possible. A domain expert sees the next experiment. I saw the missing platform. MESSAI lives in the gap between disciplines, by design.

The platform is now operational. We have ingested and enhanced 23,596 research papers — effectively the entire historical MES literature, with the field publishing roughly 2,000 new papers per year. We have extracted 201,954 structured measurements across 835 distinct parameters, organized in a knowledge graph of 2,819 causal couplings — the only queryable database of its kind. Our AI has surfaced 42 critical research gaps that the field’s own practitioners hadn’t systematically mapped.

I have spent six years preparing to build this company, and I intend to spend the next decade making it indispensable — the shared, evidence-based foundation that an entire emerging industry can build on.