One of the unique Rockr features is its use of AI to discover infromation relevant to tasks users work on, to ensure project teams connect the dots between different areas of a project.
How it works
Rockr is the hardest working team member within your project. Rockr uses a natural language processing (NLP) model that constantly analyses all deliverables project teams create. It tries to establish relationships between different written responses that are created within your project.
It analyses the title of the deliverable users work on against other titles as well the written summaries of the deliverables other users create within Rockr. For each pair that is analysed (title vs other title or title vs written paragraph within a summary), Rockr creates a similarity score that can range from 0% to 100% (100% being a perfect match).
Rockr does more than simply match keywords, it determines the meaning and context of information it discovers. So when your refer to “project risks” in one deliverable and “possible budget overruns” in another, it knows that budget overruns are a project risk.
IMPORTANT: Rockr only analyses responses that were signed off to ensure what the information shown to users is correct.
You get out what you put in
Rockr is smart, but relies on the data users create. The more details users provide in their summaries, the better the recommendations Rockr generates.
There may be instances where content that Rockr discovers isn’t quite relevant. It’s always the users decision whether to use or ignore recommendations.
NOTE: Rockr recommendations are suggestions to improve a users response to a deliverable. Users should still look for any content that Rockr may not have been able to identify which they can do directly through the the search feature on responses, where all responses are searchable.
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