Catch old versions, drafts, contradictions, irrelevant files, or sensitive details old versions drafts contradictions irrelevant files sensitive details before
they become context

Enterprises with a lot of unstructured data building AI that businesses depend on. Everyday Deasy users are engineering orgs, data science teams, and knowledge managers.

RAG systems and agents that need knowledge that lives in unstructured sources like SharePoint and Confluence. For example:

• Asset management firms build agents to help analysts close deals faster
• Insurance providers build assistants that answer claims questions from policy documents
• Pharmaceutical companies build assistants over clinical trial data to get new drugs to market more quickly

Retrieval is only as good as what's behind it. Most teams solve this once for one project. The AI and data leaders we talk to tell us it breaks down as they scale up: rebuilding the pipeline for every new use case, on data that keeps changing, is unwieldy and too costly to sustain.

• Sources: Deasy connects to company knowledge in systems like SharePoint, Confluence, S3, Azure Blob, and Google Cloud Storage. Or your existing vector database, including Qdrant and PostgreSQL.
• Destinations: Curated data and metadata can be published to PostgreSQL, Azure SQL, or Qdrant, or write back into SharePoint, OneDrive, Databricks, and Azure Blob.
• File types: PDF, PowerPoint, HTML, .docx, .txt, .md, and more.

We add connectors and file types often, so book a chat for anything you don't see.

Two reasons:

1. According to recent research from BARC, fewer data science teams are using vector search and increasingly relying on agents going directly to source documents. In this world, ensuring files are continually enriched with high-quality contextual metadata becomes critical.
2. There are some questions agents can't answer from more context. Ask your agent "How many times has a customer brought this issue up?" and no single call transcript has the answer. Deasy uses retrieval that answers from the metadata layer across your whole corpus, so correct answers to these aggregate questions become possible where they weren't before.

Source systems stay connected. New files get qualified, stale ones drop out, and curation runs on a schedule you set, so accuracy holds steady even as your files change.

It works until it has to scale.

Many teams build a pipeline that handles a few thousand files, and it works for the first project. At a few million files, every quality dimension becomes its own engineering problem: near-duplicate detection, freshness scoring, catching contradictions between documents, all of it running continuously as files change. We built Deasy so your team doesn't have to solve that more than once.

Deasy builds a metadata database in your environment describing the content, quality, and sensitivity of every file. From there you can:

1. Query it through our APIs
2. Build a queryable vector database automatically from any curated file set
3. Curate data slices for specific AI projects and expose the file content and metadata your agents need
4. Publish slices to Collibra, Databricks, and Snowflake
5. Push contextual metadata back into your source systems
6. Materialize metadata, entities, and your own ontology as a context graph your agent can query. Learn more about Live Map.

Definitely. The core of Deasy is a metadata generation engine that lets you define, upload or use AI to suggest a taxonomy. Deasy then uses a mixture of ML models & LLMs to generate chunk and file-level metadata from the contents of every file. Then you can use that metadata to filter out everything that doesn't meet the mark on quality and create the perfect knowledge base for whatever you're building.

With Live Map, you can define ontologies that describe how your data relates, and Deasy builds the graph and retrieval to match.

Deasy is SOC 2 Type 2 certified. With Deasy, you can tag and filter PII, PHI, and other regulated content before it reaches a model. In our benchmark, PII appeared in the context window for 22.9% of questions. With Deasy, exposure goes to zero.

Our pricing is based on data volume. Book time and we'll walk you through it.