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Product Ideas Pipeline

1170 Ideas

Matthew SemanickPractitioner

InField - Task Centric Focus with Selectable Asset and Conditional Response RequirementsGathering Interest

This product idea has a few parts to it that can be separated into different releases. The main focus would be to have a task centric form where we can select the asset and use the same checklist on it. The second focus is to have conditional customer response triggers based on responses provided. The third is to leverage AI to create a summary of the checklist (or parts of the checklist) in a standardized format that meets a customer’s needs. The main value of this is accelerated time to complete tasks and generate reports (less time writing) with tangential value towards analytics generated behind the responses made back into CDF’s contextualization engine. The task should end up residing with the asset selected and be used for the purpose of closing out a required inspection (PDF) back into that system or automatically passed through to it.   As an inspector, I would like to be able to complete a generic task similar to this previous request (linked below) but with the addition of custom conditional response requirements that would trigger a required action including taking a photo or describing the damage in greater detail. GPS location functionality (tell the app where you are located) should also be logged to build out 3D views with lon/lat data when it is not yet populated.  The checklist would be able to export to a PDF when completed to be logged into my inspection data management system and also store the data in a way that I can run analytics on all inspections completed with that checklist (or multiple checklists with similar formatting).  I would also like the checklist to auto-generate AI drafted responses crafted to an initial prompts set up with the set-up of the checklist to accelerate my report writing time so I can spend more time inspecting and less time transferring insights from my paper checklist to the computer. The responses should be tailored to specific checklist items that are linked to that section and any prompt created to support it.   The value that this provides includes the time savings from completing the inspection and automated report writing in addition to the analytics and insights gained to recommend focusing more time on certain equipment or inspections that show higher hit-rates.  Future functionality could include linking events that are planned from the inspections data management system into the task (Event ID below). Also the ability to create a work notification with this task as a PDF linked to it automatically generated (based on deficiencies found). Another feature would be to have ‘workflows’ where a task flows from one-person to the next with ‘hold-points’ such as a final reviewer on the inspection before it is complete.   Example Task: Pressure vessel external visual inspection checklist (Inspection Task 1.1)Event ID: (link this to an event from the inspection data management system or manually enter)Date: Auto populatedName of vessel: (select from asset hierarchy, auto restrict if created with a feature to a specific asset type or unit)Location: (3D configuration - get lon/lat data)Inspection Details:   - Is the name tag present: (Y/N)   - Is corrosion (pitting) greater than 0.25” present (Y/N)              - If a user selects (Y), prompt to take a photo and provide a description [each option should be able to be added independently (photo vs. description or both)]   - more tasks as needed.Summary:   - Create a paragraph that is consistent between all reports of this type with deficiencies noted Signature: Sign with finger or mouseName: MattAPI Certification Number: User enter    

Hugo Lopes
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Hugo LopesSeasoned

Integrate Git with Streamlit for Robust VersioningGathering Interest

This Cognite Hub Post outlines a workflow where development is done locally with the repository and then synchronized with CDF. Here at Celanese’s CDP (Citizen Developer Program) we have an different process/dynamic. Most of our citizens do not have access to local developer tools like VSCode, etc. Rendering the mentioned approach problematic to be applied in our use cases. A Git-based solution, similar to what Azure ML Studio: Git integration - Azure Machine Learning | Microsoft Learn offer, would ensure a more inclusive version control process. Azure ML Studio allow users to specify their Git repository details—such as the URL, branch, or even a specific commit—to connect their code base directly to the platform. Additionally, the integration could capture Git metadata (like commit hashes and messages) and associate it with each version, providing clear traceability and making it easier to track which code changes correspond to specific deployments.The CDF would benefit from this by adopting a structured Git-based workflow. The repository would be organized with two main branches: one dedicated to versioning, where each commit represents a stable version, and another serving as a work-in-progress branch for ongoing development. Once the work-in-progress branch reaches completion, it would be merged into the main versioning branch, creating a new stable release. A built-in selector could allow users to easily switch between different versions from the main branch or continue working on the latest updates in the work-in-progress branch, ensuring flexibility and control over the development process.With Git integration, multiple developers could collaborate efficiently, even without local development environments. The platform would maintain a consistent history of code changes, enabling better debugging and rollback capabilities while ensuring a seamless and transparent development lifecycle.