3. Data, Science & AI

Expertly designs and evaluates prompts for LLM applications, optimizing structure, context, and output formatting for enhanced model performance.

Tests and benchmarks LLM agents using behavioral analysis, capability assessment, and reliability metrics for real-world performance evaluation.

Provides persistent memory systems for LLM conversations, supporting short-term, long-term, and entity-based memory to enhance context awareness and chat history retention.

Provides PostgreSQL performance optimization techniques, query best practices, and schema design guidance for database management.

Routes user requests to external AI models (DALL-E, GPT) and real-time data sources (Twitter API) when Claude lacks native capabilities.

Enables real-time voice AI application development using Deepgram, ElevenLabs, and OpenAI for speech-to-text and text-to-speech processing.

Optimizes LLM context windows using summarization, trimming, and routing to prevent context decay and manage token limits effectively.

Provides best practices and optimization techniques for ClickHouse, a high-performance columnar database, for analytical workloads.

Enables semantic search, similar content discovery, and structured research via Exa's AI API for efficient information gathering.

Creates, alters, and validates Snowflake semantic views using Snowflake CLI, including DDL validation and setup guidance.

Merges fine-tuned AI models using techniques like SLERP and Task Arithmetic to combine domain expertise without retraining, enhancing performance and enabling rapid experimentation.

Provides SHAP-based model interpretability for explaining predictions, feature importance, and bias analysis across ML models.

Comprehensive statistical analysis toolkit for academic research, including hypothesis testing, regression, Bayesian methods, and APA reporting.

Queries PubChem database for chemical compounds, supporting searches by name, CID, SMILES, and retrieving properties, bioactivity, and similarity data.

Autonomous AI framework for biomedical research, executing complex tasks in genomics, drug discovery, and clinical analysis using LLM reasoning and integrated databases.

Comprehensive toolkit for protein language models (ESM3, ESM C) enabling sequence, structure, and function prediction, design, and engineering tasks.

Enables 4/3/2-bit quantization of LLMs without calibration data, accelerating deployment via vLLM and HuggingFace Transformers.

Provides a reference-free, efficient alternative to DPO for LLM preference alignment, achieving better performance with simpler training.

PyTorch-based toolkit for biomedical graph machine learning, featuring GNNs for molecular property prediction, protein modeling, and drug discovery tasks.

Direct REST API access to KEGG database for academic research, enabling pathway analysis, gene mapping, and metabolic pathway exploration.

A Python library for machine learning tasks including classification, regression, clustering, and model evaluation.

Accesses Human Metabolome Database for searching metabolites, retrieving chemical properties, spectra, and pathways to support metabolomics research.

Evaluates code generation models across HumanEval, MBPP, and MultiPL-E benchmarks using pass@k metrics for quality assessment.

Provides a fully managed vector database for production AI applications, supporting RAG, semantic search, and scalable recommendation systems with low latency.