This concept introduces automated comment summaries for individual proofs or connected sets of proofs, such as those involved in a single campaign. The goal is to compile feedback from various reviewers into a useful summary, highlighting key insights and critical issues, streamlining the feedback analysis and consolidation process.

We want your feedback! Does this concept resonate with you? Are there specific use cases where you see this proposed feature particularly useful in your workflow?

Imagine a system where comments on proofs are instantly sorted and labelled by relevance and theme. This proposed auto-categorization feature in Ziflow aims to simplify how feedback is organized, enabling teams to quickly zero in on pertinent issues, which could significantly boost efficiency in handling feedback, making the review process smoother and more focused.

Seeking your insights! Could this approach to managing comments make your review processes more efficient? What specific aspects of your feedback management could benefit from auto-categorization? We’d love to hear your thoughts and suggestions.

Do you spend too much time verifying that changes made on new versions align with prior feedback? This proposed feature in Ziflow automates the verification of new proof versions to ensure specified changes from previous rounds have been correctly implemented. It seeks to reduce and possibly eliminate the need for manual verification of each revision.

We value your input!

If you feel this feature would be valuable for your review processes and have specific use cases or feedback, please let us know.

Do you ever wish you could easily explore different visual or textual options while reviewing proofs? Ziflow's proposed AI assistant feature could be your solution. Designed to provide alternative images or text suggestions when users give feedback on proofs. This AI-powered tool analyzes the content being reviewed and offers relevant alternatives, enabling a straightforward comparison and selection process. These suggested options are then attached as comments within the proof, making them readily accessible for further review and decision-making by all stakeholders involved.

We're keen to hear from you! Would this AI assistant make your review process more efficient or creative? Are there specific functionalities you would like to see in this tool? Let us know how it could best serve your needs.

This concept introduces a dynamic markup capability within Ziflow, designed to intelligently track and attach annotations to specific content in video proofs. As the video plays, any comments, highlights, or notes made by reviewers will move in sync with the designated elements, ensuring that feedback remains precisely positioned relative to the content, even as elements move across frames. This idea aims to enhance the accuracy and context of feedback during video reviews, making the process more efficient and effective.

Does this sound helpful? If this appeals to you, please share your thoughts on how this could benefit your review workflow.

Simplify your review process by allowing you to approve or reject individual assets in a batch review or specific pages in multi-page proofs. This feature ensures clear decision-making, reduces rework, and streamlines workflows for complex projects.

Vote to prioritize this improvement and share how it could help your team!

A significant enhancement to user management within Ziflow ensures that any changes in group membership are automatically reflected in all assigned proofs. Eliminate the need for manual updates when adding or removing members, reducing administrative overhead and minimizing errors. With Dynamic Groups, proofs are always up-to-date, guaranteeing that the right reviewers evaluate each proof according to their current roles and skills.

Tell us what you think! How much time do you spend changing people on proofs? Do you have a solid group structure today that can be relied on? How frequently do people change in your groups?

Minimize notification and email overload with streamlined updates that guide users to review proofs in Gallery View. Easily review and approve individual proofs within a set of related proofs, all from one organized interface. This feature simplifies asset approval, saving time and reducing the noise of multiple alerts.

Briefs in Ziflow serve as the foundational document that guides the creative and project execution processes, providing detailed instructions and objectives. Potential planned enhancements include:

• Email Integration: Ability to send briefs directly via email when creating proofs, ensuring stakeholders are immediately informed and engaged.

• PDF Export: Option to export briefs to PDF, allowing for easier sharing and archiving outside the platform.

• Intake Form Description: Functionality to add descriptions to briefs during the proof upload via intake forms, enhancing clarity in initial instructions.

• Brief Templates: Capability to create and manage brief templates, streamlining the setup process for recurring projects and ensuring consistency in project initiation.

• Folder or project-level briefs: Add a brief that applies to a collection of proofs in a folder rather than a single proof.

Help prioritize these enhancements! What enhancements are most valuable to your team? How do you use briefs today, and how could the enhancements improve your workflow?

This proposed feature will enable the export of both public and private comments in Ziflow proof reports, enhancing documentation and archiving capabilities. Maintain a complete record of discussions and feedback by including all communicative aspects of the proofing process in PDF exports.

Tell us more! What users would have access to this feature? What types of projects or proofs do you need this on? Where do you store Proof Reports after they are exported?

To enhance the usability and efficiency of Ziflow's proof reports and PDF exports, the feature will allow users to selectively export comments based on criteria such as label, author, reaction, and more. This targeted export capability will enable users to generate customized reports focusing on the most relevant feedback, reducing the need for manual post-export editing.