DocArray
ATH Infosystems · Software Development
Certification per Microsoft Marketplace.
Evidence tier Source Confirmed · 1 capture on record
What the publisher says
As described on Microsoft Marketplace.
DocArray is an open-source Python framework designed for representing, processing, and transforming multimodal data for artificial intelligence and machine-learning applications. It provides a flexible data abstraction for developers, researchers, application teams, and organizations that need to work with structured and unstructured data such as text, images, documents, tensors, embeddings, and other AI-related objects.
The solution supports common AI and machine-learning workflows including preparing and processing documents, managing embeddings, handling multimodal data, performing similarity-based operations, preparing model inputs and outputs, and integrating structured data into AI and retrieval pipelines. DocArray is suitable for AI developers, machine-learning engineers, researchers, Python developers, and organizations that require a flexible self-hosted framework for data representation, document processing, embeddings, multimodal AI, and intelligent application development.
Show the rest of the publisher’s description (18 more lines)
Features of DocArray:
- Open-source Python framework for representing and processing AI and machine-learning data.
- Provides structured representations for documents, text, images, tensors, and embeddings.
- Supports multimodal data processing for AI and machine-learning applications.
- Supports document and text processing workflows.
- Provides data structures for managing embedding and tensor-based information.
- Supports similarity-based data processing and retrieval workflows.
- Can be integrated with machine-learning and deep-learning models.
- Supports data preparation for AI and retrieval-augmented generation (RAG) pipelines.
- Can be integrated into Python applications and custom AI workflows.
- Suitable for development, research, experimentation, document processing, multimodal AI, and self-hosted machine-learning applications.
Usage instructions:
Activate Virtual Environment:
$ sudo su
$ cd /opt/docarray
$ source venv/bin/activate
# Check installed DocArray version: $ pip show docarray
Disclaimer: DocArray is provided “as is” under its applicable open-source license. Functionality, performance, supported features, dependency requirements, resource consumption, and compatibility depend on the installed DocArray version, Python environment, integrated machine-learning frameworks, models, and system resources. Users are responsible for securing applications and services, managing dependencies, configuring access controls where required, and complying with applicable software licenses and data-protection requirements. This solution is suitable for self-hosted AI applications, multimodal data processing, embedding workflows, machine-learning development, research, experimentation, document processing, and production AI workflows.
Agent build and provenance
See the full provenance
The layer-by-layer build, the evidence behind each claim, the risk basis and the cross-marketplace links are open to any account. Some rows are disclosed, some the source leaves Unknown; a free account shows you which.
Compliance
- FedRAMPConfirmedNot listed90%, registry-checkedNo FedRAMP Marketplace entry matched this vendor's domain, checked 2026-08-27registry recordas observed 2026-08-27
Confirmed means matched to a public authoritative registry. Claimed means the vendor or its listing states it, not yet cross-checked. A framework not shown was not found in any source we hold, which is not evidence against it. Not listed means a scoped registry check found no match for this vendor's domain: a No is a scoped registry check, not a compliance judgment. Confidence bands: 95% domain-verified, 90% registry-checked, 80% self-attested, 70% weak signal. Self-attested items marked “vendor's site” are gathered from the vendor's own website and are not verified by us.
Vendor
External enrichment · as of 2026-08-29
Reconciled on 9/3/2026
Sources
Publisher resources
1 linkLinked repositories
Unknown means this listing does not publish a repository. It is not a statement that the code is closed, and a linked repository is not a claim that the publisher wrote it: the registry computes that relationship privately and does not publish it.
Evidence risk is the share of the build you cannot see before you deploy, not a security rating. Sign in to see the layer-by-layer basis for this band.

