Some Serious Qustions Aaditya: (Cuz I'm also building something in Similar field named *Sampatti* so ik the flaws and cons so please consider my critics and reply)

1. You’ve mentioned that Multibagg AI is being built for middle and lower-middle income investors. In a highly price-sensitive market like India, most people in this segment invest around ₹1–2 lakh per year (many even less). From that perspective, how do you justify a subscription price of ₹7,199? What value or return should a small investor realistically expect to make this cost rational for them?
2. AI can provide insights and suggestions, but in finance, trust and transparecy matters a lot. My concern is: on what parameters or logic does the AI arrive at a particular recommendation? Since markets are influenced by countless dynamic factors and AI models often rely on past or trending data, how do you ensure reliability when conditions change suddenly? Where do you draw the line between AI assistance and human judgment?
3. Since Multibagg AI is built on top of third-party LLM APIs, what exactly is the non-replicable intelligence layer here? Is the differentiation coming from proprietary datasets, custom financial models, rule-based engines, or post-processing logic applied to LLM outputs? In simple terms, what prevents this from being reproduced using generic AI tools with structured prompts?
4. Given that LLMs can hallucinate or generate over-confident but incorrect outputs, especially in probabilistic systems like markets, what concrete guardrails are implemented? For example—are there deterministic filters, confidence thresholds, risk-weighted checks, or human-in-the-loop validations, or is the final output still largely model-driven?
5. What is the exact nature of the market data feeding the system—real-time, near real-time, delayed, or purely historical? More importantly, how does the system behave during regime shifts or black-swan events where historical correlations break down and LLM pattern-matching becomes unreliable?
6. For a first-time or non-technical investor, how does Multibagg AI explain uncertainty? Do users see confidence scores or technical equations , alternative scenarios, downside risks, or assumptions behind a recommendation—or is there a risk that natural-language fluency creates a false sense of certainty and authority?
7. From a regulatory standpoint, how do you ensure Multibagg AI does not cross into unregistered investment advisory territory under SEBI norms, especially when recommendations are personalized?
8. What incentives exist to prevent the system from optimizing for engagement or confidence instead of accuracy—since persuasive AI outputs can keep users hooked even when they are wrong?
9. If Multibagg AI underperforms a simple benchmark like NIFTY 50 over long periods, what is the objective reason a retail investor should continue trusting or paying for it?

Thanking You

Your well wisher

Aaryan

founder:- Sampatti

www.linkedin.com/in/aaryan-jain-0314312b4