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features
design
support

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Description

Find out what companies such as Cisco, Reveal Data and Absolute Software already know about Catalyst, Catalyst, and others! Document Filters is the perfect toolkit to allow file inspection and processing functionality within applications for ediscovery, data protection prevention, text analytics and content management. It also allows you to search, archive, and search for files. Are your end users lost in file formats and document volume? We explain how Document Filters Drives Efficiency & Customer Value and how it can make a huge impact on all users. Document Filters allows software developers to integrate industry-leading file identification functionality in their solutions. File inspection and identification are essential first steps if your application relies upon processing files it didn't create. Document Filters uses intelligent file identification to inspect source content without relying only on the filename extension.

Description

KitOps serves as a robust system for packaging, versioning, and sharing AI/ML projects, leveraging open standards to seamlessly integrate with existing AI/ML, development, and DevOps tools, while also being compatible with your enterprise container registry. It has become the go-to choice for platform engineering teams in the AI/ML domain seeking a secure method for packaging and managing their assets. With KitOps, you can create a comprehensive ModelKit for your AI/ML projects, encapsulating all elements necessary for local reproduction or production deployment. Additionally, the ability to selectively unpack a ModelKit allows team members to optimize their workflow by only accessing the components pertinent to their specific tasks, thereby conserving both time and storage resources. Given that ModelKits are immutable, can be signed, and reside within your established container registry, they provide organizations with an efficient means of tracking, controlling, and auditing their projects, ensuring a streamlined workflow. This innovative approach not only enhances collaborative efforts but also fosters consistency and reliability across AI/ML initiatives.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

No images available

Integrations

.NET Yes 
C Yes 
C# Yes 
C++ Yes 
Hyland RPA Yes 
Java Yes 
Python Yes 

Integrations

.NET No 
C No 
C# No 
C++ No 
Hyland RPA No 
Java No 
Python No 

Pricing Details

No price information available.
Free Trial Yes 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App Yes 
iPad App Yes 
Android App Yes 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person No 

Types of Training

Training Docs No 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Hyland

Founded

1991

Country

United States

Website

www.hyland.com/en/solutions/products/document-filters

Vendor Details

Company Name

KitOps

Founded

2024

Country

Canada

Website

kitops.ml

Product Features

Document Management

Access Controls No 
Archiving & Retention Yes 
Collaboration Tools No 
Compliance Tracking No 
Document Archiving Yes 
Document Assembly No 
Document Capture No 
Document Conversion No 
Document Delivery No 
Document Indexing No 
Document Retention No 
Electronic Signature No 
Email Management No 
File Recovery No 
File Type Conversion No 
Forms Management No 
Full Text Search No 
Offline Access No 
Optical Character Recognition No 
Print Management No 
Version Control No 

Product Features

DevOps

Approval Workflow No 
Dashboard No 
KPIs No 
Policy Management No 
Portfolio Management No 
Prioritization No 
Release Management No 
Timeline Management No 
Troubleshooting Reports No 

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Alternatives

Alternatives