Making collaboration easier: New features for shared Collections and data comparison

During our recent product demo call, we unveiled updates to shared Collections, public links for sharing insights, and improvements in data comparison. Here’s a look at what we covered:

Collaboration with shared collections

We rolled out shared Collections to enhance teamwork and facilitate collaboration. Users can now create and manage collaborative workspaces, invite team members via email, and set access levels as needed. To get started, users simply need to move personal Collections into the shared section or create a new shared Collection. 

Can users invite external partners without requiring them to have existing accounts? The answer is yes! An invitation email guides them through account creation.

Share insights instantly with public links

We launched public links for threads, enabling users to share chat responses externally while keeping full Collection access private. This feature offers a view-only experience, ensuring shared insights remain secure.

We are actively exploring allowing others to contribute to individual threads instead of needing to invite them to an entire Collection to provide more targeted collaboration options.

Enhanced data comparisons for smarter analysis

Users can now easily compare data across Collections, improving analysis from various sources. We refined equal weighting to ensure fair comparisons between datasets, and users can reference Collections using hashtags (e.g., #collectionname).

We plan to introduce file-level hashtag referencing, allowing users to flag key files for priority in AI responses.

Exploring open-source AI for enhanced data access

We are working on integrating open-source AI models to provide more flexibility and security for users. We are exploring models like Llama and other open-source alternatives that can run in a secure environment. This approach allows users to access advanced AI capabilities without relying solely on proprietary models.

We are also considering adding customizable model selection, enabling users to choose between different AI models based on their needs. By incorporating open-source models, we aim to offer greater transparency and control over how AI processes and retains information.

Watch the session here:

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