Systems Built

Projects

These are not just things I built. They are examples of how I think through systems in different domains.

Real-time Anomaly Detection for Industrial Data

Framework: Sensor Noise → Anomaly Signal → Decision

Problem

Industrial sensors produce massive amounts of high-velocity time-series data. Traditional threshold-based monitoring fails to detect subtle, compounding failure states early enough to prevent downtime.

Approach

Instead of relying only on rigid rules, I approached this as a prototype for continuous analysis. The goal was to establish a moving baseline for normal behavior and flag deviations earlier than static thresholding.

System Design

▶Input: Raw industrial sensor metrics from simulated or streaming sources.
▶Processing: Sliding-window statistical analysis and model-based anomaly scoring.
▶Output: Severity-based anomaly alerts for monitoring workflows.

Key Features

Dynamic baseline calculation adapting over time.
Comparison between learned behavior and static thresholds.
Severity-based alerting for easier monitoring.
Prototype logic for reviewing unusual machine behavior.

Outcome

Built a system prototype that could detect irregular machine behavior earlier than static thresholding.

Finance Copilot - AI-Driven Analysis System

Framework: Historical Noise → Pattern Signal → Prediction

Problem

Financial analysts spend excessive time aggregating disparate market data and extracting insights from unstructured reports to make investment decisions.

Approach

The goal was decision support, not replacement. I explored how fragmented financial data could be structured into clearer signals, iterative forecasts, and usable summaries.

System Design

▶Input: Historical market data, live signals, and structured financial sources.
▶Processing: Pattern analysis across time-series data with iterative refinement.
▶Output: Dashboard-style insights, forecasts, and structured decision support.

Key Features

Multi-source financial inputs across historical and live data.
Iterative refinement across different time horizons.
Dashboard-style summaries for patterns and forecasts.
Structured outputs designed for easier review.

Outcome

Built a concept for turning fragmented financial data into clearer trend analysis.

Agentic AI Workflows

Framework: Input Chaos → Structured Workflow → Output

Explored multi-agent systems for automating redundant engineering and operational workflows.

QueryWhiz

A system concept that translates natural language into structured queries so non-technical users can explore data with less manual SQL work.

MoM (Meeting Operations Master)

A workflow concept that turns technical discussions into structured summaries, action items, and outputs designed for downstream task creation.

Private Operations Workflow Platform

Framework: Sensitive Operations → Structured Access → Internal Workflow

Overview

Designed a private workflow system for sensitive operational use cases, with emphasis on controlled access, traceability, and structured internal processes.

Focus

▶Controlled access across sensitive internal workflows.
▶Traceability around how information moved through the system.
▶Structured processes that reduced ambiguity in day-to-day operations.

Freelance Product Work

Worked with founders on MVP-style systems and workflow tools, helping translate ambiguous business needs into structured product flows and working prototypes.