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Product Ideas Pipeline

1185 Ideas

Feature Request: First and Last Value Aggregates for Granularity-Based QueriesNew

Hello Cognite Support Team, I'm working with the Cognite Data Points API and would like to request a feature enhancement regarding aggregation functions. Current Situation: When querying time series data with a specific granularity (e.g., "1d" for daily), the available aggregates (Sum, Average, Count, Interpolation, StepInterpolation, etc.) don't directly provide the first or last actual data point within each granularity period. Feature Request: Could you add first and last aggregate functions that would:first: Return the earliest data point (by timestamp) within each granularity period last: Return the latest data point (by timestamp) within each granularity period  Use Case Example: For a time series with granularity: "1d" and aggregates: ["first"], the API would return the first recorded value for each day (e.g., the value at 00:00 or the earliest available timestamp that day). Similarly, aggregates: ["last"] would return the last recorded value for each day (e.g., the value at 23:00 or the latest available timestamp). Current Workaround: Currently, we're fetching hourly data (granularity: "1h") and then manually filtering/grouping to extract the first or last value per day, which is less efficient for large datasets. Question: Are there any plans to add native first and last aggregate functions? If this feature is already available through a different approach, I'd appreciate guidance on the best practice.

Dinesh Makked
Practitioner
Dinesh MakkedPractitioner

Process-Aware Knowledge Graphs for Industrial AIGathering Interest

Inspiration“Context is king in the world of AI.”Across research, publications, and industry discussions, one theme consistently stands out — AI without context lacks true intelligence. To unlock the full potential of Industrial AI, we must ground AI solutions in process context.VisionIntroduce Process-Aware Knowledge Graphs (PAKGs) that integrate process understanding directly into the Cognite Data Fusion (CDF) ecosystem. By capturing and structuring the interconnections, interdependencies, and material flows from Process Flow Diagrams (PFDs), we enable context-driven intelligence for Agentic AI solutions built on Atlas AI and CDF.Core Capabilities System Model Extraction Automatically extract process metadata from P&IDs and PFDs (PDF/Image formats). This removes the dependency on CAD files, which are often unavailable or inconsistent. Process-Aware Knowledge Graph Generation Translate the extracted system model into a Knowledge Graph enriched with process semantics. Represent equipment, process streams, and control loops as nodes and relationships, creating a foundation for process discovery, reasoning, and autonomous insights. Value Proposition Enables Agentic AI systems to reason over process context. Accelerates ROI realization from Cognite solutions by improving AI explainability, traceability, and domain relevance. Lays the groundwork for next-generation Industrial AI applications — from automated root cause analysis to process optimization. AskI propose enhancing CDF to support this capability natively, creating a bridge between engineering documentation and context-aware AI models.

Cognite VISION: No-code data engineering for domain expertsGathering Interest

Problem StatementCognite Data Fusion (CDF) offers a powerful suite of tools for industrial data operations, but its adoption remains limited to highly technical users such as data engineers, data scientists, and developers. Today, creating data transformations, writing functions, deploying models, and generating insights in CDF typically requires:Knowledge of Spark SQL for transformations Python programming for custom functions Understanding of data modeling concepts Manual deployment and orchestrationThis steep technical barrier restricts broader usage, particularly among domain experts like production operations engineers, maintenance supervisors, or process owners who possess deep contextual knowledge but lack coding skills. As a result, CDF usage and ROI are throttled by dependence on a small pool of technical resources. VisionEmpower every domain expert to become a CDF power user — without writing a single line of code. Proposed Solution: Cognite vision – AI-Powered No-Code ExperienceIntroduce Cognite VISION, an out-of-the-box AI agent integrated into CDF that uses LLMs to eliminate the need for coding expertise.With VISION, a user can simply ask:"Join sensor data from the compressor with maintenance logs and create a dashboard to predict downtime every 6 hours."VISION handles the rest:Interprets the intent using an LLM Writes Spark SQL transformations behind the scenes Creates and deploys Python functions for processing or inference Builds contextualized data models Schedules pipelines Deploys insights to dashboards or external appsAll within seconds, fully auditable, and explainable for enterprise transparency. Key Features Natural Language Interface: Ask for transformations, models, or dashboards in plain language Automatic Backend Generation: LLMs write code, configure parameters, and deploy pipelines One-Click Deployment: From request to production in a few clicks or a single prompt Insight Builder: Automatically recommends and generates insights based on domain context Governed Execution: Every AI-generated artifact passes through existing governance and logging frameworks

Atlas AI feedback and suggestionsNew

Atlas AI – agent building and chatbot notesThe GoodAble to switch between list and tile view as well as Search. Having a Description is helpful. Nice that there are sample questions. Loading feedback. Ability to stop generation. Legal disclaimer near prompt area. Nice that it shows reasoning steps. Like the suggestions with Show More Bot background color is fine. Reasonable area to write a prompt. Great to be able to view the details using the info icon of what each agent is good at before switching to it.Could be improvedPriority 1Starting a new chat is “risky” because there is no history, copy or chat download/export functionality. Part of the point is to aggregate information for analysis later, sharing or collaboration Chat history/multiple threads Need a way to set expectations up front so that when a new chat is started, we can orient users as to what can and cannot be done. Description helps but gets cut off. Sample questions as defaults are not injected but auto triggered. This can be an issue since the prompt is most likely not exactly what they want. Need some sort of prompt library so that users will be presented with prompts that have been vetted, and we are sure will produce relevant answers and get them quickly started. So not just examples but starter prompts. When it suggests follow ups, it would be great if these may be clicked and injected into the prompt area (but not auto submitted). Priority 2I would expect the naming for Atlas Agents to be in Industrial Tools and Agent Library to be in the Atlas AI. Ability to choose to share threads Edit icon should maybe use a plus. I know Copilot uses that but it’s still odd. Not sure what to do about location action and listing. What does this mean or how does it affect bot? Needs link to terms or docs. No file attachment. Users will probably want need this to override on a case by case basis. Publish / Unpublish flow is a bit awkward when prompt engineering between multiple people. Prompt area should be fixed to a certain height (perhaps one half the viewport) and scroll or be resizable. The use of a full takeover modal with a close button is odd when starting a chat. Switching to a new chat when moving between agents without a chat history is somewhat dangerous since users will lose all their work. It’s good that we can switch between agents while in a thread. However, I would expect that when I am in the agent library, clicking on another agent starts a new thread with that agent. Returning to the original agent would show my previous conversation with the agent. Cannot generate tables easily. Although I did manage to do so one time. I easily miss My Agents and Published tab. This causes a starter problem. Would like to add multiple tools at a time and not need to add them one by one.Possible bugsSeemed to lose past chats bubbles after a certain amount of conversation. Reloading shows them. Need to verify. Ran into an issue when switching between an agent within a thread created a new chat.