My Work

Non-technical users needed to access and analyze data from massive public blockchain datasets (Bitcoin, Ethereum, Solana) available in BigQuery, along with real-time crypto market data and trends. The problem wasn't just data access—it was making complex blockchain data understandable and actionable for people without technical or SQL expertise. Additionally, we faced significant technical challenges: queries against massive datasets were extremely expensive, some queries took several minutes to execute, and users didn't know how to optimize their questions to avoid costly data retrievals.

Financial analysts needed to research complex queries across multiple data sources—SEC filings, market data, internal databases, and documents—but existing tools required switching between platforms and manual data synthesis. The internal team needed a unified dashboard with deep research capabilities that could automatically gather, analyze, and synthesize information from diverse sources to answer sophisticated financial questions.

Businesses needed powerful AI bots with complex workflows, but building custom bots required significant development time and technical expertise. Each use case—from community management to transaction automation—required custom code, making it impossible for non-technical teams to deploy and iterate on bot behaviors.

Growing furniture cover e-commerce brand needed to: 1) Scale their Shopify platform with better performance and customization, 2) Improve organic visibility through technical SEO, 3) Make data-driven decisions with proper analytics infrastructure, 4) Help customers find products through AI-powered visual search.

Raw videos contained valuable product information, but extracting and making this data searchable at scale was impossible without manual processing. The client needed an automated system to convert unstructured video content into structured, queryable data for analytics and decision-making.

Medical laboratories struggled with managing test workflows efficiently and providing actionable insights from patient reports. Pathologists spent significant time manually reviewing each report to identify critical values, and there was no systematic way to leverage patient history for contextual analysis. The lab needed a system that could not only streamline operations but also provide AI-powered analysis to help identify critical cases quickly and offer intelligent suggestions based on patient data.