3. Data, Science & AI

Builds mobile-first data visualizations with AntV F2, supporting line, bar, pie, and other chart types for data representation.

Provides programmatic access to NMR spectroscopy datasets from nmrxiv.org, supporting searches by experiment type, compound name, or SMILES with JSON output.

Manages data labeling workflows via Rapidata SDK, including order submission, validation sets, and AI benchmarking for model training.

Audits Decision JSONL logs for schema compliance, required metadata, and invariants to ensure data integrity and consistency.

Manages RAG indexes for AI models, enabling indexing of outputs, sessions, and statistical analysis.

Adds lightweight data versioning to ensure dataset reproducibility and track changes in data pipelines.

AI-powered toolkit for analyzing video content, including summarization, key moment identification, transcription, and frame extraction from various video formats.

Dispatches multiple AI agents concurrently to solve independent problems, optimizing task resolution through parallel processing.

Validates data against business rules including required fields, email format, and numeric types for form inputs and data consistency.

Refactors Python research code to enhance run configuration, seeding, and reproducibility for scientific experiments.

Systematically identifies root causes of unexpected metric changes and anomalies in business and performance data through data analysis.

Generates standardized metadata for data assets to enhance discoverability, documentation, and organization in data catalogs and dictionaries.

Retrieves compressed memory summaries by ID, project, or recency for LLM prompts, returning structured JSON with compression metrics.

Analyzes time series data for trends, seasonality, anomalies, and builds simple forecasting models.

Indexes project files for semantic code search with incremental updates and batch embedding generation for AI-powered code retrieval.

Creates new AI agent implementations for the Yui Protocol system, supporting agents like Yui, Eiro, and Kanshi.

Dynamic system for creating, managing, and orchestrating custom AI agents with personality assignment and voice mapping.

Validates metrics across multiple data sources, investigates discrepancies, and ensures data quality during migrations or system integrations.

Conducts rigorous statistical analysis for A/B tests, including significance calculation, sample ratio checks, and pre-launch validation.

Indexes project files for semantic code search using AI embeddings, with incremental updates and batch processing.

Performs time-based cohort analysis to track user retention, behavior, and feature adoption across user groups over time.

Conducts comprehensive data quality assessments against business rules, schema compliance, and referential integrity for data pipelines.

Indexes source codebases for semantic search using vector similarity, enabling natural language queries about code logic.

Provides persistent memory storage for user and conversation context via BrAIny REST API, automatically managing context window limits.