3. Data, Science & AI

Guides implementation of document databases for flexible schema applications, covering schema design, indexing, and aggregation pipelines in MongoDB, DynamoDB, and Firestore.

Provides a systematic framework for selecting and creating data visualizations, including 24+ chart types, accessibility compliance, and performance optimization for dashboards and reports.

Implementation guidance for time-series databases (TimescaleDB, InfluxDB) in metrics, IoT, and financial applications with downsampling and retention policies.

Provides strategic guidance for designing modern data platforms, including storage paradigms, modeling approaches, and governance frameworks.

Provides strategic guidance for operationalizing machine learning models, covering experiment tracking, model registry, feature stores, and monitoring in production environments.

Builds and orchestrates data transformation pipelines using ETL/ELT patterns, dbt, and Python (pandas, polars, PySpark) with Airflow.

Provides graph database implementation guidance for relationship-heavy data models using Neo4j, ArangoDB, and Amazon Neptune.

Calculates accurate training costs for Tinker LLM fine-tuning by tokenizing datasets with the correct model tokenizer.

Enables LLM fine-tuning via Tinker API, supporting supervised learning, LoRA, and vision-language models with high/low-level access.

Enables integration of LLMs with symbolic solvers (Z3, Prover9) for natural language to logic translation and logical reasoning tasks.

Guides creation of high-quality datasets for LLM fine-tuning, evaluation, and data collection strategies in post-training (SFT/DPO/RLHF).

Coordinates multiple AI agents with specialized expertise to execute independent tasks or deliver comprehensive analysis from diverse perspectives.

Simplifies PostgreSQL database interactions including query execution, data management, and schema operations.

Curates high-value information for AI models, reducing token usage by 84% while improving performance by 39%.

Provides subtitle parsing, generation, translation, and speaker identification using Gemini API for end-to-end transcription-to-translation workflows.

Validates database schema, integrity, migrations, and query performance to ensure reliable data persistence and transaction handling.

Provides test reporting, quality dashboards, and predictive analytics for data-driven quality decisions in engineering workflows.

Empowers AI agents to enhance quality engineering workflows using PACT principles, acting as force multipliers for quality assurance tasks.

Optimizes AI-powered quality engineering agents using transfer learning and metrics refinement for continuous performance improvement.

Deep integration with agentic-flow@alpha for SONA learning, featuring Flash Attention acceleration and 9 reinforcement learning algorithms.

AI-powered tool for predicting software defects, identifying patterns, and performing root cause analysis to enhance quality assurance proactively.

Unified memory system for AI agents featuring AgentDB, HNSW indexing for 150x-12,500x faster search, and SONA learning integration.

Extracts YouTube video transcripts, titles, and thumbnails for data ingestion and analysis.

Extracts text and images from PDFs using OCR, with per-page tracking, rasterization, and metadata inspection. Outputs saved locally.