Today iPulse AI is live on Uneed.

I started investing at 15. My first stock was Apple in 2007. Years later, while managing my family’s global portfolio, I found that the hardest part was not finding another opinion—it was reconstructing why a conclusion was reached, which evidence supported it, what would invalidate it, and whether earlier forecasts had actually been right.

In parallel, I studied AI, worked at J.P. Morgan, and later consulted on AI topics at PwC and Accenture. When GPT-3 arrived, I became convinced that machine reasoning would eventually outperform much of routine financial analysis—but only if the workflow was inspectable.

That became the product constraint behind iPulse AI: multiple independent agent perspectives, historical forecast records, cross-asset comparison, stress testing, and evidence that users can inspect instead of a polished black box.

We began on AWS, later moved core experimentation to GCP, and support from the Google for Startups AI programme helped us test much more aggressively. The biggest lesson was that model quality is only one layer. Source traceability, workflow provenance, explicit stop policies, evaluation, and transparent limitations matter just as much.

I’m the founder, so this is openly self-promotional. But I want hard questions, not polite praise: what would you need to inspect before trusting AI-assisted investment research?

If the idea resonates, an upvote on today’s Uneed launch would genuinely help. If it doesn’t, tell me what is missing—that feedback is even more useful.

Russlan Ramdowar

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