Deasy Labs

Deasy Labs is an AI data-curation platform with APIs and a Python SDK for preparing unstructured organizational data for RAG systems, search, and AI agents. It connects to sources such as SharePoint and S3, OCRs, parses, chunks, and normalizes files, then builds taxonomies, applies metadata, detects sensitive data, and evaluates file quality and relevance against a use case. Users can slice the enriched corpus by relevance, topic, time, quality, or sensitivity, write metadata back to source systems, or deliver datasets to downstream retrieval pipelines. Data slices are continuously monitored and refreshed as source content changes. The SDK can also generate context files and folder indexes for coding agents, as demonstrated in the video.

Collibra is an enterprise data-governance and AI-governance platform described by its maker as an Enterprise AI Control Plane. It governs context and control across data, models, and AI agents, including structured data and governance workflows for AI systems. In the described unstructured-data workflow, Collibra supports AI-assisted taxonomies and metadata tagging with evidence and confidence scores, sensitive-data detection, duplicate and conflict checks, freshness monitoring, scheduled data-slice refreshes, and generation of context files for coding agents.

Collibra Unstructured Data for AI is a Collibra solution for preparing and governing unstructured document data for AI use cases. It supports document curation, AI-assisted taxonomy and metadata tagging with evidence and confidence scores, sensitive-data detection, and quality checks for duplicates, conflicts, and stale content. Data slices can be refreshed on a schedule so AI-ready datasets remain current. Collibra describes the solution as providing governance for AI use cases, models, data, and agents with end-to-end traceability, oversight, and compliance.

DatologyAI is a data curation service for AI model builders. It processes proprietary enterprise data, public web data, and licensed data for pre-training and mid-training on open models or privately deployed foundation models. Its four-stage pipeline cleans poorly formatted, empty, short, or evaluation-contaminated data; curates it by quality, task, and taxonomy; creates synthetic data for task relevance and diversity; and composes sources into staged training datasets. The company also describes seeded rephrasing methods for generating synthetic data at trillion-token scale.

Deft is a self-hostable, open-source workspace where people and AI agents share chat, tasks, knowledge, calendar context, approvals, and action history. Its core workflow captures discussions as durable knowledge, lets the Defty agent use workspace context to propose task details, and routes proposed agent actions through configurable approval and trust policies before recording the approved work and its receipts. It also provides real-time conversations, task views, notes, knowledge graphs, calendar features, personal MCP connections, agent employees, native workspace tools, scoped access, revocation, and provider-neutral support for hosted or local AI providers. Deft can run without an AI provider as a conventional workspace. The self-hosting path uses Docker and PostgreSQL with pgvector; the repository is an alpha intended for technical evaluation, internal use, and controlled pilots. It is licensed under AGPL-3.0-only.