3. Data, Science & AI

Transforms CSV, Sheets, or database data into executive reports with visualizations, key metrics, and trend analysis for leadership decision-making.

Designs optimized database schemas for SQL and NoSQL, including ERD diagrams and migration scripts.

Analyzes Reddit threads for sentiment, consensus, top arguments, and discussion patterns to provide community insights.

Compares sports player statistics side-by-side, adjusting for era, pace, and league differences, with advanced metrics explained in plain English.

Identifies companies matching your best customers' profiles using AI similarity scoring on firmographic, tech stack, and growth data for target account list building.

Predicts March Madness bracket outcomes by analyzing historical trends, upset potential, and strategic confidence levels.

Aggregates and analyzes customer reviews from multiple platforms, performing sentiment analysis, identifying pain points, and generating competitive insights for marketing decisions.

Analyzes meeting transcripts to extract decisions, action items, blockers, and sentiment, then generates structured summaries and follow-up emails for team productivity.

Converts SRT subtitle files to structured JSON, extracting timecodes and text for data processing and analysis.

Automates Metabase REST API interactions for managing questions, dashboards, and data exports with standardized visualization settings.

Builds custom AI tools for competitive AEO search monitoring, including API integration, web scraping architecture, and compliance considerations.

Validates and manages database schemas with type safety, index configuration, and migration strategies for seamless schema evolution.

Builds AI agents with Convex, enabling RAG, tool integration, and workflow orchestration for efficient agent development.

Analyzes plain text documents for semantic structure, token distribution, and provides visualizations including waffle charts for comparative analysis.

Provides access to livestock and animal data from GCS for tracking, welfare monitoring, and regulatory compliance reporting.

Queries Danish agricultural datasets in GCS, enabling data discovery, schema understanding, and joins via CVR/CHR/BFE identifiers using Parquet and DuckDB.

Provides access to agricultural land and field data including boundaries, crop types, land use, and cadastral information from GCS.

Retrieves financial and economic data including subsidies, farm finances, property values, and ownership details from GCS.

Manages end-to-end data pipeline operations including ETL, transformation, and validation using Python, GCS, and medallion architecture for large-scale data processing.

Provides access to Google Gemini AI for complex reasoning, research, and iterative assistance with session continuity.

Processes and analyzes CSV, JSON, and text files with data transformation, cleaning, analysis, and visualization capabilities.

Comprehensive guide for implementing RAG systems, covering vector databases, chunking strategies, embedding models, and retrieval optimization.

Optimizes SQL queries via EXPLAIN analysis, index design, and query rewriting for 10x-1000x speedups in PostgreSQL, MySQL, and SQLite.

Provides best practices for model loading, tokenization, fine-tuning, and inference optimization with Hugging Face Transformers for NLP and AI tasks.