We're a small team doing meaningful research. Our work spans ML, NLP, and Computer Vision — focused on real-world problems that matter to people and communities.
Most of our papers are co-authored with academy students — giving young talent real research experience alongside mentors.
From detecting fake reviews to analyzing payday loan impacts, our research tackles problems that affect everyday people.
All papers are published on Arxiv — free and open for anyone to read, learn from, and build upon.
At Kactii, research isn't an academic exercise — it's how we solve real problems. Every paper we publish starts from a narrow, practical use case: detecting fake product reviews that hurt small sellers, predicting financial risk for underserved communities, automating API testing so lean dev teams ship faster, or analyzing satellite radar data for environmental monitoring. We don't chase broad benchmarks or theoretical novelty. Instead, we go deep into specific domains where applied ML can create tangible impact. Our academy students work alongside experienced researchers on these projects, turning classroom knowledge into production-grade insights. The result is research that doesn't just sit on Arxiv — it feeds directly into the tools, models, and agentic systems we build for our clients. That's what makes Kactii's research different: it's small in scale, focused in scope, and built to matter.
Bagiya Lakshmi S, Sanjjushri Varshini R, S Deepika Sri, Raja CSP Raman