The Aryng Dispatch
A monthly newsletter with smart takes on data, AI, and decision-making for leaders who want results, not noise.
Welcome to the latest edition of The Aryng Dispatch. We have been up to some truly exciting things at Aryng and AskEnola.ai this month! Let’s kick things off with a note from our CEO, Piyanka Jain:
From the CEO’s Desk
Large Language Models have opened up remarkable possibilities, but they also hallucinate quite often. In business contexts, that is not a minor flaw; it’s a deal breaker. A confident, well-worded answer that is wrong can derail decisions, mislead teams, and quietly erode trust in data-driven work.
Much of today’s GenAI landscape is still built on brittle wrappers, tools that look sleek on the surface but lack the depth, validation, and reasoning needed for real-world decision-making. Therefore, the challenge ahead is not adding fancier UI or plug-ins. It is building AI systems that understand business logic, validate outputs, and deliver insights leaders can act on with confidence.
At Aryng and AskEnola, that is our focus. Enola is not just conversational; it is accountable, because AI that cannot be trusted has no place being in business.
What’s Brewing at Aryng
Enola’s Reports are now Shareable!
When Enola performs a deep analysis for you and generates a Final Report, you can share it with other stakeholders and discuss the findings to inform the overall strategy.
Curious what a report prepared by a Super Analyst looks like? Well, you are in luck because we have prepared a sample report just for you.
In any case, you can try it yourself. Just upload a CSV, ask your questions, and see how fast Enola takes you from raw data to ready decisions.
Can GenAI tools really help with real-world business use-cases?
AI tools like Copilot and Enola promise to make analytics simpler. But when tested on a real supply chain problem, for example, getting insights that can help reduce procurement lead time, their differences became clear. Copilot outlined steps but struggled to connect its recommendations to the actual data. Enola, on the other hand, interpreted the question, analyzed the dataset, and produced targeted, data-backed insights that informed decisions.
The experiment offers a useful lens on how natural language tools handle analytics in practice.
Here’s an Interesting Thing You Must Read, or Watch, or Listen
Well, we generally categorize as “interesting” anything that provides you with value of some sort. But we were drafting this edition of the Dispatch on Halloween, and figured that a cute (if somewhat malicious) cat video is always interesting to watch (or, enjoyable, at any rate).
So, meet Macavity, the Emperor of Delay, who is holding a seance to get insights from business data.