Honest Opinions on AI Startup Idea
I already have a job lined up at a BB but I’ve been building a side project for a while and just wanted some feedback.
The idea is a prompt-based portfolio risk copilot for clients and advisors of non-institutional firms (small RIAs, WMs, FOs) that turns typical client “what-if” scenarios into quantified portfolio impact + actionable hedges tailored to each clients holdings.
Ex “What happens to my portfolio if Trump invades Greenland?”
From the prompt it runs factor sensitivities, historical analogs, and scenario stress tests then outputs exposure maps, expected drawdown/risk, and hedging ideas explained instantly.
The value prop is giving smaller firms institutional-grade risk analytics without Aladdin-level pricing and letting clients self-serve these scenarios so advisors spend less time answering theoretical daily questions and more time on actual portfolio/advisory work.
Please be blunt, is this a good idea? What makes sense and what doesn't?
This idea has potential, but there are a few key points to consider:
What Makes Sense:
Target Market Fit: Smaller RIAs, wealth managers, and family offices often lack access to institutional-grade tools like Aladdin due to cost constraints. Offering a more affordable, user-friendly solution could fill a significant gap in the market.
Value Proposition: Automating "what-if" scenario analysis and providing actionable hedging ideas is a strong selling point. Advisors are often bogged down by client questions, and this tool could save time while enhancing client engagement.
Client Empowerment: Allowing clients to self-serve scenarios is a clever way to reduce advisor workload while increasing client satisfaction. It also positions your product as a differentiator for firms looking to offer a tech-forward experience.
Scalability: If the tool is prompt-based and leverages AI effectively, it could scale well across different client bases without requiring significant manual input.
What Needs Work:
Complexity of Scenarios: While the idea of running factor sensitivities and stress tests is appealing, the execution could be challenging. For example, scenarios like "Trump invades Greenland" are highly speculative and may not have clear historical analogs or factor data to model accurately. You’ll need to manage user expectations about the tool’s limitations.
Data and Accuracy: Institutional-grade risk analytics require robust data sources and sophisticated models. Ensuring the tool provides reliable and actionable insights without oversimplifying complex scenarios will be critical.
Advisor Adoption: While the tool aims to save advisors time, some may view it as a threat to their value proposition. You’ll need to position it as a complement to their expertise rather than a replacement.
Pricing Strategy: Competing on price against institutional tools like Aladdin is smart, but you’ll need to ensure your pricing aligns with the budgets of smaller firms while still covering development and data costs.
Regulatory and Compliance Concerns: Financial advisors operate in a heavily regulated environment. Your tool will need to ensure compliance with industry standards and avoid providing advice that could be construed as fiduciary responsibility.
Suggestions for Improvement:
Focus on Specific Use Cases: Start with a narrower set of scenarios that are easier to model and have clear data support. For example, focus on interest rate changes, sector-specific shocks, or geopolitical events with historical precedents.
Advisor Integration: Build features that allow advisors to customize or validate the tool’s outputs, reinforcing their role in the advisory process.
User Experience: Ensure the interface is intuitive and the outputs are easy to understand for both advisors and clients. Avoid overwhelming users with overly technical data.
Partnerships: Consider partnering with data providers or existing platforms to enhance the tool’s capabilities and credibility.
Pilot Testing: Run a pilot program with a few small firms to gather feedback and refine the product before scaling.
Final Thoughts:
The idea is promising, especially if you can deliver institutional-grade analytics at a fraction of the cost. However, execution will be key, particularly in terms of data quality, scenario modeling, and user adoption. If you can address these challenges, this could be a game-changer for smaller firms looking to level up their risk management capabilities.
Sources: PE professional, what's your process while judging an investment?, Best way to read a CIM?, https://www.wallstreetoasis.com/forums/the-only-post-about-active-investing-you-will-ever-need-to-read?customgpt=1, Investment is more about the poetry than the math, Pitch me a Stock Interview Question for Investment Banking?
It's a worthwhile idea to pursue.
I think you'd find a fairly good user base amongst technical retail - you know anons on X managing 5-8 figs of their own money - as well as small firms like you mentioned.
The biggest concern is how you'd discover and acquire the expert knowledge base to actually be able to run these simulations at scale better than competitors. That's assuming you know how to fine-tune AI and have a good sense for UX design. Like, if you're just gonna rely on Mercor or maybe you have a specific alpha where you can do it much more cheaply and more effectively. Depending on that kinda determines whether it makes sense to build a company or just pitch a feature to existing companies.
If you have some thoughts on that feel free to DM. I'm looking to either put money or time (my own expertise building AI and working with expert level data) into stuff in this category.
Really appreciate this, I DM'd earlier this week. One of my main focuses is helping advisors handle the constant client “what-if” questions faster and more consistently, not just building a smarter model. From your perspective, what actually makes advisors trust and use a tool in their real client workflow, instead of it just being another cool AI feature?
What feedback have you gotten from potential customers? If you haven’t spoken with any, stop building and get on the phone with them first. Validating an idea starts with problem/product discovery with prospects.
I’ve been leaning into it, my earlier calls were more about how advisors use risk tools but now I’m focused on how often clients reach out with “what-if” questions, how much time that takes, and what actually helps calm them. I’ve been asking things like when those calls turn into reallocations and what parts of those conversations feel most repetitive or draining. Sample size is somewhat small but promising so far.
Is the main pain point saving time? Generally, that's a poor value proposition since it's hard to quantify how much that's means in $ unless it's at scale or dramatically frees up resources.
What I would focus on is solutions that drive growth or maximize $ generation on the top line. It's much easier to convince someone to buy a product that generates more money for them, directly or indirectly vs. saves them costs. With cost saving, you're limited by the amount they already spend, with revenue generation, they can scale the ROI better.
For example Finny helps Advisors grow their book of business intelligently. It's easy to spend $25K on something that helps generate $100K+ of revenue. Obviously this is literally a prospecting tool so it's easier to draw a line to revenue generation, but often times there's other paths to generating revenue, whether it's an additional service that unlocks a new customer segment or a tool that increases the ability to cater to larger AUM clients.
bump
An AI agent can already do all of this ... We have to understand that there is no original ideas anymore, tomorrow, 1000 new way to use AI will emerge. I think the real value in the coming years will be the deep understanding on how tech can solve problems, reduce costs and scale businesses.
Don't get me wrong, the idea is good, but your value here is in the problem you have identified and the way you sell it it to other businesses . The real value come from the capacity to identify threat and respond to it .
So yes the idea is good, but your business here is on the consulting side, not the tech. You can launch this copilot in 12 hours, but being recognized as serious problem solver is the real deal ;)
I agree that the tech itself is becoming commoditized fast so what would you say is the biggest difference between a tool that feels like “consulting in a box” vs one that just feels like generic AI?
the tool has to be designed as reverese engineered . What is the prblem and what would be the ideal solution, then build with agentic and other tools from there. the main problem in applied AI is people fall in love with their tech before knowing if this tech nsolve the real problems of your target cusomer. Building the other way around solve this and your consulting in a box makes perfect sense. Once you have 2/ 3 projects successful with proper use case, you scale .
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