TMOM touches sensitive decision flow and trading behavior. Transparency matters. The founders combine AI/ML systems depth, product execution, and firsthand conviction that trading performance is mostly won or lost in behavioral consistency.
Advaith leads vision and market thesis for TMOM. His focus is the gap between a trader's stated edge and actual behavior during stress events. He has pursued this theme through applied ML work and founder-level product execution, with training across strong engineering institutions including SRM Institute of Science and Technology and Cornell's ecosystem.
Prior to TMOM, he worked as an MLOps consultant with Deccan AI, contributing to high-quality AI refinement and deployment workflows. His public technical writing and research interests center on modeling trader behavior, ML optimization in trading systems, and practical ways to convert psychology into measurable, enforceable process constraints. He has also contributed as an IEEE reviewer, reflecting ongoing engagement with technical research quality.
Key highlights
Why I'm building TMOM: Most traders do not lose because they lack strategy. They lose because they cannot enforce process under pressure.
Abhinav leads TMOM's core systems architecture and deterministic kernel development. He is completing an MEng in Computer Science at Cornell Tech after a BS in Computer Science at UCF, with concentration in software engineering, algorithms, and production-ready AI systems.
His experience spans high-performance computing and enterprise AI deployment. At Los Alamos National Laboratory, he worked on clustered compute and accelerated workflows. In industry roles, he has improved model serving quality and latency, and delivered large-scale internal assistants powered by modern LLM and retrieval pipelines. This background maps directly to TMOM's requirement for low-latency, replayable, auditable decision infrastructure.
Key highlights
Why I'm building TMOM: Deterministic supervision should be as rigorous and observable as the trading systems it monitors.
Vallab leads product architecture and experience design for TMOM. He brings multiple years of AI-native engineering work across frontend, backend, and ML-adjacent infrastructure, with previous roles including Standard AI and Omneky. He graduated summa cum laude and has remained focused on turning AI concepts into robust user-facing software.
His strength is translating complex system behavior into clear product mechanics that users can trust. From workflow design to reliability tradeoffs, he focuses on making advanced intelligence usable without losing observability or control. At TMOM, that means interventions that feel natural in-session and post-session reports that are precise, interpretable, and operationally useful for both individuals and desks.
Key highlights
Why I'm building TMOM: Discipline tools only work if they are trusted in the moment and useful after the moment.
TMOM is built around the conviction that strategy leakage is primarily psychological and can be measured in dollars through deviation costing.
The technical stack is designed for replayability, observability, and low-latency policy enforcement with institutional-grade trust requirements.
Graduated interventions are intentionally designed to preserve trader discretion while introducing enforceable friction when process breaks.
Join the waitlist for early access. We are working directly with serious discretionary traders and prop teams who care about process enforcement, auditability, and measurable behavioral improvement.
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