Let’s admit it—you’ve probably tried at least once asking ChatGPT or another AI model of your preference questions about your wind farm, maybe even uploading a spreadsheet or copying some data into the chat.
And while these tools are incredibly useful for many tasks, they quickly show their limitations when it comes to domains that require a deeper understanding, which these generic AIs usually lack.
The problem isn’t that these AI models aren’t smart enough to crack wind power challenges. It’s that they simply don’t have the context or access they need. Even when you share data with them, they’re working with isolated snapshots—not the comprehensive, interconnected operational picture that wind farm management requires.
A true AI agent expert in wind farms would require establishing reliable integrations with multiple data sources—SCADA systems, alarm databases—and maintaining these connections as systems evolve (easier said than done). Then you’d need to provide extensive wind power domain knowledge so the AI starts looking from the same expert perspective you do. Still, with all the access to the data and domain context, these agents are not capable of deriving insights you wouldn’t see in your dashboards visualizing the data. For the actual value that would impact your farm’s availability, production and P&L, the most critical component is the advanced analytical methods that continuously analyze your data to surface insights and feed them to the agent.
Without these layers, you’re essentially asking an AI to read numbers from a spreadsheet or a database without understanding what they mean or how they relate to each other. The result? Generic responses that might sound helpful (and AI models are remarkably convincing at making you believe their answer is exactly what you need), but don’t actually move the needle on operational decisions.
Well, don’t bother trying to build all these, because there is KEN. And for your convenience, KEN is accessible through WhatsApp.
When you ask KEN a question, it searches through your actual wind farm data and the insights Kavaken machine learning models have already extracted. If the relevant information exists—whether it’s raw data or derived analytics—KEN provides accurate, data-backed answers. If it doesn’t exist, KEN tells you honestly: “I don’t know” or “I can’t help you with that.”
This approach addresses a critical issue with AI systems: hallucinations. Recent research from OpenAI published in September explains why generic AI models sometimes make up plausible-sounding but incorrect answers. These models are trained to always provide responses, even when uncertain, because they’re rewarded for answering rather than admitting ignorance. It’s like a student guessing on an exam question rather than leaving it blank. So, if you use ChatGPT or any other generic model, there is a considerable probability that the response you’ll get should be double or triple verified to ensure that it did not hallucinate, even if you provide some data with the question.
KEN operates differently, although behind the scenes it uses a generic model we all interact with every day. Because it only draws from your actual data and Kavaken’s verified insights—not from a generic knowledge base—it virtually eliminates such risks of being misled. KEN isn’t guessing or filling in gaps with probable-sounding information. It’s either finding real data or insights, or acknowledging when it doesn’t have the answer.
So what can you actually ask KEN? We’ve gathered examples across different operational areas to help you understand how to integrate KEN into your day-to-day work. These aren’t just possible questions—they’re the kinds of queries that wind farm operators regularly need answers to.
Test these yourself with our demo version of KEN, which uses a fictitious 3-farm wind portfolio: Chat with KEN now!
Example Questions to Ask KEN:
Live Monitoring
- Are there any stopped turbines right now?
- Give me a status report for my wind farms.
- What’s the current output of my wind farms?
Performance Analysis
- How was the performance of my wind farms against expected in October 2025?
- Give me a waterfall analysis of my wind farms for October 2025.
- Compare my last two months’ production.
- What were the main factors affecting my production last month?
- How was my overall availability yesterday?
Issue Insights
- Any new issues?
- What are my active issue flags right now?
- Give me details about the WTG06 ‘Power Curve Deviation’ flag.
- Show me all turbines affected by yaw misalignment.
- Which flags should I investigate first?
Maintenance & Prioritization
- What should my O&M team prioritize this week?
- Which issues have the highest production impact?
- Are there any long-standing unresolved flags?
- Suggest preventive actions for this week.
Deep-Dive Scenarios — Performance Deep Dive
- How was my production last month?
- Can you break it down by wind resource and downtime?
- Show me the limiting factors for the lowest-performing site.
Deep-Dive Scenarios — Flag Investigation
- Any new flags today?
- Which turbine has the highest financial impact?
- Prepare an email draft with a flag summary I can send to OEM.
Deep-Dive Scenarios — Availability Analysis
- What was the availability in January 2025 for my site?
- Compare it with February 2025.
- Show which turbines caused most of the downtime.
- How much is the lost revenue?
Reporting & Communication
- Create a summary report for all wind farms this week.
- Generate a message I can send to the OEM about a specific flag.
- Prepare a monthly performance summary for management.
- Highlight key issues to mention in our weekly O&M call.
When you try KEN, you’ll notice something different from typical AI interactions: you get either concrete, data-driven answers or honest acknowledgments when information isn’t available. No made-up statistics, no misleading interpretations—just reliable responses based on your actual operations.
