3. Data, Science & AI

Token-efficient RAG orchestrator using minimal context (~500 tokens) and on-demand queries for optimized information retrieval.

Computes the Jacobian matrix of partial derivatives for linearization in scientific and mathematical computations.

Generates Excel and CSV files with formulas, formatting, charts, and data analysis capabilities for data-driven workflows.

Guides AI model fine-tuning processes to optimize performance for specific tasks and datasets.

Analyzes financial data to identify cost-saving opportunities while maintaining quality standards.

Spawns PAI agents via MCP factory tool, managing identity, RAG context injection, and task execution for Claude Code agent deployment.

Expert guidance for implementing user behavior tracking, event logging, conversion optimization, and A/B testing to generate data-driven insights.

Analyzes survey data to uncover patterns, derive insights, and provide data-driven recommendations for decision-making.

Designs statistically valid A/B tests to optimize marketing campaigns through data-driven experimentation.

Generates detailed social media analytics reports with data-driven insights and actionable recommendations for performance improvement.

Optimizes natural language prompts for large language models to improve output quality and relevance via systematic analysis.

CP-SAT constraint model for TAMC Family Medicine scheduling optimization, documenting hard/soft constraints, decision variables, and objective functions for OR-Tools solver.

Builds and executes multi-step prompt chains to break down complex AI tasks into manageable steps for efficient processing.

Converts raw data and analytics into compelling visual narratives and presentations for clear insights.

Integrates AI tools and APIs into business applications and workflows to enhance functionality and automate processes.

Analyzes investment opportunities across asset classes using risk assessment, return modeling, and portfolio optimization techniques.

Performs scalable text sentiment analysis with deep contextual understanding for accurate emotional tone detection.

Curates and cleans training datasets to ensure high quality for machine learning model development.

Manages conversational context and memory to enhance AI response coherence and relevance in chat applications.

Enables aborting runaway solvers and monitoring progress during schedule generation, with support for integrating abort checks into solver loops.

Builds interactive dashboards to define, track, and visualize KPIs for data-driven business decisions.

Expert guide for designing efficient database schemas, implementing RLS policies, and optimizing PostgreSQL/Supabase data models for performance and security.

Generates and manages text embeddings for semantic search, clustering, and similarity tasks using AI models.

Builds RAG pipelines to ground LLM responses in user-specific data, enhancing accuracy and relevance.