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We have provided information on the cost of launching an MVP for three projects of varying levels of complexity, enabling you to compare what each project entails.
FAQ
How can an investor trust an AI's prediction if its reasoning is a "black box"?
That's a critical point, and it's addressed by "Explainable AI" or XAI. Modern platforms don't just give an answer; they provide the rationale. They can highlight which data points, like a sudden rise in building permits, most heavily influenced a prediction, allowing investors to validate the logic.
If everyone uses predictive AI, won't that create self-fulfilling bubbles in the hotspots it identifies?
It's a valid concern. However, not all AI models are the same. Different firms use proprietary data and unique algorithms with varying risk tolerances. This diversity should prevent a single point of failure and may even lead to a more efficient, faster-correcting market rather than uniform bubbles.
Is this technology only for large institutions, or can smaller investors access it?
While the most powerful custom models are for large institutions, the technology is rapidly becoming more accessible. A growing number of specialized SaaS platforms now offer sophisticated predictive analytics on a subscription basis, leveling the playing field for smaller firms and even individual investors.
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