3. Data, Science & AI

Creates and configures Claude Code subagents for task delegation, guiding users through agent setup, tool selection, and system prompts.

Enables structured entity extraction from text using Cohere v2's JSON Schema mode for validated outputs in NER and data pipelines.

Optimizes AgentDB vector search performance through quantization, HNSW indexing, caching, and batch operations for memory reduction and speed gains at scale.

Provides documentation and guidance for building AI agents, tools, and model integrations using the Pydantic AI framework.

Automates data pipeline coordination for ETL, transformations, and quality checks to enforce 95% data quality standards.

Routes user queries to the most suitable Claude model (Haiku, Sonnet, or Opus) based on complexity and requirements.

Delivers personalized AI conversations by leveraging user profiles and vectorized notes for context-aware responses and memory of past interactions.

Proactive tool for analyzing AI task complexity, generating chunking strategies, and ensuring execution stays within token budget limits.

Systematic framework for analyzing, creating, and refining AI prompts using evidence-based techniques to enhance model performance.

Analyzes email communications to cluster conversations, detect business opportunities, and surface high-potential prospects for sales outreach.

Splits large AI tasks into manageable chunks to prevent context window overflow during extended processing.

Converts custom PyTorch models to Hugging Face Transformers format, including creating configuration classes and preparing for Hub upload.

Extracts text and content from PDF documents using Python, enabling data processing and conversion for analysis pipelines.

Processes CSV files, validates data integrity, and provides structured analysis for data-driven tasks.

Validates and processes Unicode characters and emoji content for proper encoding and text data integrity.

Integrates Azure AI services for search, speech processing, document intelligence, and agent-based workflows.

Enables deep knowledge graph traversal to uncover entity relationships and provide comprehensive context for concept-based queries.

Guides memory curation by updating outdated entries, marking obsolete content, and building knowledge graph connections for high-quality knowledge organization.

Enables efficient querying and analysis of large-scale data in Azure Data Explorer using Kusto Query Language (KQL) for log, telemetry, and IoT data insights.

Assists in model discovery, deployment, RAG application building, AI agent creation, and Microsoft Foundry troubleshooting.

Generates or edits images using Google Gemini API via the nanobanana platform for user image creation requests.

Initializes the Cortex AI framework for first-time setup, configuring essential components and dependencies for AI model development and deployment.

Analyzes meeting transcripts and notes using NLP and AI to extract action items, decisions, topics, sentiment, and generate summaries.

Processes PDF documents for data extraction, generation, and manipulation using libraries to handle text, forms, and document structure.