3. Data, Science & AI

AI-powered research suite leveraging vector embeddings to solve complex transit challenges including accessibility and last-mile optimization.

Provides implementation guidance for database sharding strategies, including time-based partitioning (year/week), shard query services, and table name generation for efficient data distribution.

Provides probabilistic estimation for incomplete data (e.g., unknown birth times), replacing 'Unknown' with calculated values in data modules.

Extracts structured tour planning data (attractions, hotels, transportation, itineraries) from historical DOCX files.

Configures analytics tracking systems to collect user behavior data for actionable marketing and product insights.

Builds conversational systems that gather evidence via natural dialogue to resolve data uncertainties and incomplete information.

Assists in designing, building, and managing data pipelines, ETL processes, and data infrastructure with Apache Spark, Airflow, and Kafka.

Provides design patterns for LLM integration, including factory usage, prompt engineering, fallback strategies, and activity logging.

Optimizes research agents for API efficiency, rate limit handling, and enhanced web search integration with real-time progress tracking.

Assists with database management tasks including migrations, backups, replication, optimization, and scaling for efficient data handling.

Enables deployment, monitoring, and management of ML models using MLOps tools like MLflow and TensorFlow Serving.

Specializes in implementing AI features with Vercel AI SDK, including streaming responses, useChat, useCompletion, and generative UI interfaces.

Expertly designs PostgreSQL schemas and optimizes data access using Prisma ORM, with raw SQL query capabilities for database state inspection.

Guides on Antigravity IDE architecture, skill creation, and multi-agent orchestration using the Antigravity Kit and MCP integration.

Provides enterprise AI architecture patterns for production systems, including RAG, Multi-Agent, AI Gateway, and LLMOps.

Automates ML/DL project setup by analyzing structure and generating reproducible environment configurations.

Provides principles for creating scalable and maintainable database schemas using established design methodologies.

Optimizes Google SCANN for billion-scale vector search, enabling efficient learned indexing and enterprise-scale similarity search on massive embedding datasets.

Automates ML/DL project execution including training, inference, and evaluation based on .autoflow configuration, with error repair and parameter tuning.

Analyzes Reddit and forum data to identify complaint clusters and assess niche market viability through data-driven scoring.

Provides testing patterns for SpoonOS agents, including unit tests, integration tests, and LLM response mocking for debugging agent behavior.

Extracts design tokens and copy rules from templates to create reusable prompt recipes for AI applications.

Ensures consistent relationships between school data entities through database integrity constraints and validation.

Resolves database schema conflicts by selecting the most recent timestamp version, ensuring consistent data schema management in versioned systems.