3. Data, Science & AI

Enables multi-agent coordination through hivemind MCP tools for orchestrating complex AI agent workflows.

Creates PostgreSQL database schemas, Sequelize migrations, and model associations with best practices for database operations.

Allows users to query specialized sub-agents within an AI system for targeted task execution and information retrieval.

Enables production deployment of ML models, MLOps implementation, and scalable ML system development using PyTorch, TensorFlow, and RAG.

Builds scalable data pipelines, ETL/ELT systems, and data infrastructure using Python, SQL, Spark, and modern data stack tools for data modeling and governance.

Provides advanced data science capabilities for statistical modeling, A/B testing, and time series analysis to drive data-driven business decisions.

Provides morphological analysis for Kabardian language, breaking down word structure, verb templates, prefixes, suffixes, and derivational patterns.

Analyzes PostgreSQL and Supabase schemas to evaluate design quality, security, performance, and adherence to best practices.

Specialized prompt engineering for LLM optimization, agentic system design, and RAG implementation in AI product development.

Provides in-memory data storage for caching, session management, pub/sub messaging, and real-time analytics using Redis.

Provides CA-specific BigQuery integration patterns and Flask guidance for querying CA Lobby lobby data using BLN API schema.

Framework for building and training conversational AI models, RAG systems, and multilingual retrieval solutions using NVIDIA NeMo.

Enables building and deploying computer vision systems with object detection, segmentation, and real-time video analysis using PyTorch, OpenCV, and vision transformers.

Provides API documentation and integration tools for NVIDIA's AI services including NIM, LLM, visual models, multimodal APIs, and CUDA-X microservices.

Enables building, training, and deploying machine learning models via Amazon SageMaker, including endpoints, inference, and MLOps workflows.

Deploys and manages AI models via NVIDIA NIM for inference, API integration, and AI application development.

ChromaDB client for storing/retrieving text embeddings with hybrid search, enabling RAG and contextual retrieval in clinical data.

API client for Phi-4 LLM to perform text generation, summarization, and content evaluation via Ollama.

Performs hybrid search (embedding + BM25) to retrieve relevant clinical note passages for evidence-based claims in medical summaries and recommendations.

Enables creation and testing of hypothetical indexes in PostgreSQL to evaluate query performance without modifying production schemas.

Contextual-chunking implements RAG with LLM-generated context prepended to clinical note chunks, improving citation accuracy by 49%.

Calculates optimal portfolio weights using mean-variance optimization to maximize the Sharpe ratio.

Detects clinical note sections (HPI, ROS, Assessment, Plan) using regex and LLM topic segmentation, generating a Table of Contents with byte offsets.

Provides benchmark-based rankings, cost analysis, and use-case-specific recommendations for comparing and selecting LLMs like GPT-4, Claude, and Gemini.