3. Data, Science & AI

Tool for implementing and evaluating recommendation algorithms including collaborative filtering and matrix factorization, with visualization and report generation for personalized recommendations.

Performs regression analysis and predictive modeling using linear regression, decision trees, and random forests for continuous target variables with automated feature engineering and visualization.

Deploys optimized AI/ML inference workloads on GKE using Google's Inference Quickstart for model discovery and manifest-based deployment.

Enables Elasticsearch integration and synchronization patterns for NestJS applications, enhancing data search capabilities.

Provides a foundational standard for token-efficient agent instructions and CLI-based automated activation of AI agents.

Standardizes creation of high-density agent skills with optimal token usage for AI systems.

Optimizes AI model context by compacting memory, reducing token usage, and archiving learnings when memory is full or tokens are high.

Manages session lifecycle, memory state, and context windows for AI applications, optimizing token usage and ensuring memory health.

Builds frameworks to evaluate AI agent performance, validate context engineering, and measure iterative improvements.

Creates and edits Excel spreadsheets with formulas, formatting, and financial modeling standards, addressing recalculation and common pitfalls.

Builds production RAG systems with LangChain orchestration and Qdrant, covering multiple architectures and evaluation metrics.

Creates reusable prompt templates that encode domain-specific patterns for efficient, consistent AI task execution.

Builds AI agents with Google's ADK, featuring Gemini integration, multi-agent workflows, and Vertex AI deployment.

Provides AI-powered repository analysis and RAG-based Q&A for open-source codebases on GitHub, GitLab, and Bitbucket.

Builds AI agents using OpenAI's Agents SDK and LiteLLM for multi-model support, including tool implementation and agent handoffs.

Optimizes AI agent context capacity to overcome limitations, reduce costs, and improve performance in long-running systems.

Builds persistent memory systems for AI agents using Mem0, claude-mem, or custom solutions for conversation memory, preferences, and contextual recall.

Designs and implements evaluation frameworks for AI agents, testing reasoning quality, building graders, and conducting error analysis for robust agent performance.

Diagnoses and mitigates context degradation in AI agent systems, resolving performance issues from excessive context length during agent operation.

Builds Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search for knowledge-grounded LLM applications.

Optimizes LLM prompt design for enhanced performance, reliability, and production-ready output control in AI applications.

Optimizes selection, routing, and credit management of over 150 data enrichment providers for company and contact intelligence.

Provides database schema patterns for InsForge covering social graphs, e-commerce, content publishing, and multi-tenancy with Row Level Security (RLS).

Validates database schema, ensures data integrity, tests migrations, and analyzes query performance for reliable data persistence and system stability.