Query Memory

What is Query Memory?

Query Memory is a platform for document intelligence that helps teams parse documents, manage the resulting data, and deploy AI agents from a unified workspace. The core purpose is to give AI agents reliable access to the right document information by organizing what’s extracted and how it can be queried.

Instead of treating document parsing and agent execution as separate steps, Query Memory brings them into one workflow. This is intended to let teams move from document ingestion to agent use without rebuilding the same data access layer each time.

In practice, the platform focuses on organizing document-derived information so it can be used for downstream agent tasks, with query-focused access to the extracted content.

Key Features

Document parsing: Parse documents to turn unstructured content into usable data for downstream work.
Data management: Store and manage extracted document data in a structured way so it’s available for later querying and agent tasks.
Unified workspace: Use a single interface/workflow to move through parsing, data handling, and agent deployment.
AI agent deployment: Deploy AI agents that can leverage the stored document data to support information needs during agent workflows.
Query-focused access: Organize document intelligence around queryable information so agents can use relevant document context for responses or actions.

How to Use Query Memory

A typical workflow described for Query Memory follows these steps:

Parse documents: Provide documents to be parsed so their contents can be extracted into usable data.
Manage the extracted data: Use the platform’s workspace to review and manage the stored document information.
Set up agent usage: Prepare or configure AI agents so they can use the managed document data.
Deploy and query: Run the agents so they can access document-derived information while performing their work.

The key idea is that parsing, data management, and agent deployment are part of one connected workflow, so the document context is organized and ready for agent use.

Use Cases

Customer support knowledge grounding: Parse support documents (such as policies and FAQs) and deploy an agent that answers customer questions using the relevant document context.
Internal research and reporting: Ingest internal documents and deploy agents that retrieve and synthesize information when responding to queries from colleagues.
Document-driven workflows: Use parsed document data as a consistent information backbone for agent-driven tasks that require access to specific sources.
Team knowledge consolidation: Consolidate multiple document sets into one workspace, so agents can draw from organized document intelligence rather than scattered files.
Developer-facing document querying: Build applications or agent behaviors that rely on queryable document intelligence produced by the platform’s parsing and data management steps.

FAQ

What does Query Memory do?

Query Memory parses documents, manages extracted data, and supports deploying AI agents that can use that document intelligence from a unified workspace.

What problem does it solve for AI agents?

It provides a structured way to convert documents into queryable information, so agents can access relevant context instead of relying only on raw files.

Do I need separate tools for parsing and agent deployment?

Query Memory is designed to bring document parsing, data management, and agent deployment into a single workspace workflow, which reduces the need to stitch together separate systems for the same data access layer.

What kinds of tasks can agents perform with document memory?

Agents can be deployed for document-driven tasks where document context is needed—such as information retrieval and response generation based on the stored document-derived data.

Where can I learn how to get started?

You can follow the product’s described workflow (parse documents → manage extracted data → deploy agents). For detailed steps, you would typically rely on the product’s on-site documentation and/or guided setup in the workspace.

Alternatives

RAG (Retrieval-Augmented Generation) using a vector database + document ingestion pipelines: Alternative approach where documents are chunked and indexed, and an agent retrieves relevant passages for responses.
Document management systems with an AI search layer: Use a centralized document repository with query/search capabilities and an agent that consults those results.
Agent frameworks with custom document parsing/data plumbing: Alternative approach where the agent behavior is built on top of your own parsing and data-access layer rather than a unified workspace.
Knowledge base tooling with structured knowledge sources: Use a curated knowledge base (e.g., wikis or support knowledge bases) as the source of truth that agents can query.