TL;DR: Finding a defect on a battery production line is often quicker than figuring out what caused it. Engineers may spend hours comparing machine readings, material batches, and production logs. Niobia’s battery manufacturing analytics software brings those records together and,
 uses the same natural-language approach for data-security investigations, letting teams question findings while keeping the eventual action subject to human approval. For instance, an analyst can ask how much data each computer on the organization's network transferred over the last 24 hours. The [generated query appears alongside an explanation](https://investigator.corelight.com/docs/nlq.html) of how the system interpreted the question and constructed the search. More importantly, analysts can inspect and change the query instead of treating the AI's answer as final. This makes it easier to understand how a search was constructed, an important part of making natural language network investigation auditable. Rather than relying on an opaque answer, analysts can examine the query and its results directly. So, when evaluating this capability, buyers weighing AI alert triage tools should ask to see the generated query alongside its results.](https://saastake.com/wp-content/uploads/2026/11/Auditable-AI-Investigation-Flow_11zon.png)
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TL;DR: The robotics industry has spent years pouring investment into capability: better models, larger datasets, more generalizable machines. But as robots get smarter, controlling them
