3. Data, Science & AI

Guides selection, configuration, and optimization of Apache Superset charts for business intelligence, finance, and compliance scenarios.

Expert guidance for writing optimized SQL queries in Apache Superset, emphasizing performance, dataset design, and PostgreSQL features for finance and compliance use cases.

Extracts and validates text from receipts and BIR forms using PaddleOCR, ensuring accurate data capture for financial processing.

Automates Apache Superset dashboard, dataset, and chart generation for finance, compliance, and operational analytics, reducing BI tool costs.

Guides creation of professional Apache Superset dashboards with optimized layouts, intuitive navigation, and data storytelling for Finance, compliance, and operations.

Ingests SEC EDGAR filings (10-K, 10-Q, 8-K) via official API with source tracking and stores in pgvector for data analysis and AI applications.

Automates Apache Superset dashboard, dataset, and chart generation for financial, compliance, and operational analytics, reducing BI tool costs.

Analyzes Reddit discussions to validate product viability through user sentiment, pain points, and market trends using data scraping and visualization.

Develops and extends WARNERCO's Robotics Schematica, an agentic RAG application using FastAPI, LangGraph orchestration, and Azure AI Search for backend, flow management, and memory tiers.

Integrates LLM providers (Claude, GPT) with sentiment analysis to deliver emotion-aware conversational responses in chat interfaces.

Extracts plain text from text-based PDF documents without OCR or DOCX support.

Creates optimized prompts for Claude-to-Claude pipelines enabling multi-stage workflows (research -> plan -> implement) where outputs feed into subsequent prompts.

AI-powered validation to detect outdated documentation, version mismatches, and architectural drift in software systems.

Provides standardized templates and patterns for integrating skills into AI agent prompts, reducing token overhead with reusable syntax and guidelines.

Standardizes output formats for AI agents to ensure consistent data structure in research, planning, implementation, and review tasks.

Manages scientific Python dependencies and environments with unified conda-forge and PyPI support, enabling reproducible workflows for complex scientific projects.

Enables creation of complex, interactive data visualizations using HoloViews, including multi-dimensional plots, overlays, and dynamic maps for data exploration.

Handles labeled multidimensional scientific datasets including climate, satellite, and oceanographic data with NetCDF/HDF5 support and Dask integration for efficient analysis.

Enables selection and application of perceptually uniform, colorblind-friendly colormaps for enhanced data visualization clarity and inclusivity.

Enables natural language querying of databases and generation of SQL/visualizations via AI, supporting conversational data analysis and RAG for domain-specific analytics.

Optimizes high-performance visualization of massive datasets (100M+ points) using Datashader, leveraging rasterization and colormapping for efficient rendering.

Builds no-code data dashboards with Lumen YAML, connecting multiple data sources and enabling interactive visualizations without coding.

Enables rapid creation of interactive data visualizations using hvPlot, supporting basic plot types and pandas DataFrame integration for analysis.

Creates interactive geospatial visualizations including maps, choropleth maps, and spatial analysis using GeoViews for geographic data representation.