3. Data, Science & AI

Provides multi-agent workflow orchestration patterns for Clojure+Vertex AI, including state management, error handling, and monitoring for complex AI pipelines.

Enables deep learning model training loops through AI plugin integration, optimizing model development workflows.

Context management patterns for Clojure-based Vertex AI agents, including caching, TTL, and LGPD-compliant data handling for multi-source AI systems.

Extracts API request/response schemas from Korean Public Data Portal documentation and outputs structured JSON for seamless data integration.

Aggregates and analyzes user/expert reviews from multiple sources, performing sentiment analysis and extracting pros/cons to synthesize consensus patterns.

Collects and validates technical product specifications from multiple sources, ensuring data completeness against predefined schema requirements.

Manages persistent workflow state using AgentDB (DuckDB) for analytics, providing dependency graph queries, historical metrics, and checkpoint recovery from TODO files.

Classifies historical entities (e.g., hotels, churches) in newspaper text as building, organization, or both using contextual NLP analysis for Getty AAT facet assignment.

Automates database query optimization using EXPLAIN analysis, indexing strategies, N+1 prevention, and performance monitoring for efficient SQL execution.

Adds Qdrant vector database embedding support to WordPress v3 components for RAG chatbot functionality, enabling structured content chunking and searchability.

Analyzes assessment data to calculate mastery rates, identify achievement gaps, and generate learning analytics dashboards for educational outcomes.

Automates LLM performance evaluation with dataset management, metric computation, A/B testing, and experiment tracking.

Automates structuring LLM prompts with templates, few-shot examples, and security to enhance model performance and safety.

Coordinates multiple AI models (Claude, GPT, Gemini, DeepSeek) for parallel trading analysis, decision aggregation, and performance tracking in competitive trading environments.

Answers analytical carbon accounting questions using internal datasets, APIs, and emission factor calculations.

Automates stock trading via AI decisions, executes Alpaca paper trades, and sends Telegram notifications.

Optimizes algorithmic trading strategies via QuantConnect's API with Phase 4 parameter tuning capabilities.

Enables backtesting of trading strategies using QuantConnect's API for quantitative analysis and strategy evaluation.

Optimizes AgentDB performance via quantization, HNSW indexing, caching, and batch operations for memory reduction and faster vector search at scale.

Enables advanced vector search, multi-database coordination, and distributed AI system integration using AgentDB features.

Provides tools for evaluating and implementing AI integrations within learning technology platforms.

Enables development of algorithmic trading strategies using QuantConnect's component library for backtesting and execution.

Enables semantic vector search with AgentDB for RAG systems, semantic search engines, and context-aware document retrieval.

Enables adaptive learning systems with AgentDB's vector database for self-learning agents, featuring trajectory tracking, memory distillation, and pattern recognition.