Building AI systems at the intersection of finance, machine learning, and engineering.
I have spent the past decade working across financial services, software engineering, and AI — first as a practitioner inside institutions, then building tooling for them. That dual perspective shapes everything about how Data Concept Studio is designed: systems that are rigorous enough for real money, but fast enough for a startup.
On the finance side, I have worked across trading operations, risk frameworks, and capital markets technology — understanding not just how markets work, but how decisions are made under uncertainty and time pressure.
On the engineering side, I have designed and delivered full-stack systems end-to-end — from data pipelines and APIs to cloud infrastructure and production deployments. I have led teams, shipped products, and maintained systems that cannot afford downtime.
The convergence of large language models with real-time market data is the most significant opportunity I have seen in my career. MATHS is my attempt to build the kind of AI-assisted trading infrastructure that previously required a quant team and years of development — available to a much wider set of participants.
Trading operations, risk management, capital markets technology, and quantitative analysis across multiple asset classes.
Multi-agent systems, LLM integration, retrieval-augmented generation, ensemble modelling, and production ML pipelines.
Cloud-native architecture, APIs, data pipelines, and infrastructure — designed, built, and operated end-to-end.
Leading engineering teams, defining technical strategy, and delivering complex projects from concept to production.
Institutional trading desks have always had an edge: teams of analysts, proprietary data feeds, and risk systems built over decades. Individual traders and smaller funds have had to make decisions with a fraction of that infrastructure.
AI changes that equation. The goal of Data Concept Studio is to bring institutional-grade decision support — systematic, auditable, risk-aware — to a much broader set of market participants, without sacrificing the human oversight that good risk management requires.
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