3. Data, Science & AI

Automates real-time synchronization between physical systems and digital models to maintain alignment and detect drift.

Splits tasks across subagents with defined contracts, interfaces, and merge strategies for efficient multi-agent workflow orchestration.

Enables forecasting future outcomes through predictive modeling using historical data and trend analysis for data-driven decisions.

Fast local speech-to-text transcription with GPU acceleration, word-level timestamps, and identical accuracy to OpenAI Whisper.

Runs what-if scenarios to test hypotheses, evaluate alternatives, and predict system behavior under varying conditions.

Runs a local video processing pipeline to generate structured documentation including overview, timeline, and highlights with artifact links.

Analyzes ML/RL research codebases to generate concise primers documenting architecture, algorithms, and key patterns for robotics and reinforcement learning projects.

Creates and configures AI agents, chatbots, and assistants on the VectorChat platform through interactive setup.

Analyzes Axiom query patterns to identify unused data and builds dashboards for cost optimization, reducing data waste and ingest spend.

Enables SQL querying of tabular data in Parquet, CSV, and JSON files using DuckDB, including S3 integration for data analysis and inspection.

Guides creation of Model Context Protocol (MCP) servers to integrate external APIs with LLMs, supporting Python (FastMCP) and Node/TypeScript (MCP SDK).

Guides coordination of multiple AI agents through workflow phases, autonomous execution, and scale determination in LLM-based systems.

Reviews SQL migrations and Spark scripts for performance and safety across T-SQL, PostgreSQL, and Spark environments.

Configures search result prioritization and penalty rules in GrepAI to optimize search relevance and user experience.

Command-line tool for Excel data operations including reading, writing, transforming .xlsx files with export to CSV/JSON/PDF/PNG.

Initializes GrepAI framework in a codebase for AI workflow integration and project configuration.

Manages Oracle Autonomous Database on OCI, focusing on performance troubleshooting, cost optimization, and HA/DR implementation with ADB-specific insights.

Provides advanced search in GrepAI with JSON output, compact mode, and AI agent integration.

Configures LM Studio as a local embedding provider for GrepAI, enabling GUI-based management of embedding models on user machines.

Configures Ollama as a local embedding provider for GrepAI, enabling private vector embedding generation without cloud dependencies.

Configures Ollama for private, local embedding generation with GrepAI, enabling on-premises vector database creation for AI applications.

Configures PostgreSQL with pgvector for vector storage in GrepAI, enabling efficient similarity search for large-scale AI applications in team environments.

Assists in implementing OCI GenAI inference APIs, troubleshooting token and rate limits, optimizing costs, and ensuring PHI compliance.

Configures Qdrant vector database for GrepAI, enabling high-performance vector search for AI applications.