@shubhere2003 Hey Shub. Yup we wanted to get away from the generic mainstream LLMs like the GPTs and create something useful for the finance community,

@alison_oakley @shubhere2003 Thanks guys for the comments :)

@narcismirandes Hey Narcis, thanks for your insightful feedback. I completely agree that while there are several players in the LLM market like OpenBB using Claude 3 Haiku and Finchat's use of OpenAI GPT3.5, specializing in a niche like finance significantly enhances functionality and user experience. Here's why AllMind stands out: Specialization in Finance: Unlike general-purpose models that cover a broad range of topics, AllMind is finely tuned for finance. This means when you ask about complex financial models, like a DCF analysis for Apple, AllMind provides detailed, relevant answers. General-purpose LLMs often lack depth in specialized areas, leading to generic or less useful responses. This is an issue users experience when using a product like Finchat as their responses lack depth and knowledge. Tailored Financial Insights: AllMind excels in identifying key investment insights. For instance, in catalyst event analysis—such as market reactions to significant geopolitical events—AllMind offers specific, actionable intelligence rather than the generic or restricted output typical of broader LLMs. If you ask about complex geopolitical scenarios, such as the impact of conflicts like Hamas attack on Israel and how it impacts financial markets. Products such as Finchat, OpenBB, or similar AI-driven financial applications will give you vague error messages due to their Terms of Service constraints, or even nonsensical generic AI responses. In contrast, AllMind AI is designed to provide detailed, contextually accurate analyses in such situations, ensuring that financial professionals receive precise and actionable insights that are critical for informed decision-making Sustainability and Independence: Leveraging proprietary technology rather than depending on third-party updates ensures that AllMind can rapidly adapt and innovate without waiting on external developments. This independence from major AI models means continuous improvement on our own terms, leading to better customization and enhanced user experience. When your entire technology is built on the foundation of someone else’s, it's not a sustainable model. Many companies in this situation create situations such as $5,000 in revenue for every $50,000 spent on OpenAI credits. This is clearly unsustainable. It leads to a reliance on venture capital funding—if they can secure it in the first place—and limits the company's ability to customize and tailor their product. When products like Finchat want to upgrade their models, they must wait for OpenAI to release the next update. How long could that take? One month? Six months? A year? Possibly never? Should users really have to wait for an external company to update their product before seeing improvements in the service they are paying for? This is similar to being told that you will not be seeing any updates to your Gmail account unless Amazon first updates its systems, which clearly makes no sense since you are using Gmail, not Amazon. In summary, AllMind isn’t just another LLM or generative AI financial tool; it represents a brand-new approach to financial research and analysis. It is specifically designed to empower financial professionals with precise, actionable insights. Our focus on avoiding generic fillers and reducing 'hallucinations'—common in other AI tools—sets us apart, ensuring our users receive trustworthy and valuable information. This approach not only maximizes the utility of our tool for investors but also demonstrates our commitment to quality and relevance in a highly specialized field.

@adeel_new Thanks adeel :)

@anurag_singh_kushwaha I am glad you liked the product. If you have any feedback let me know :)

@ranvir011545 Hi Ranvir. Thank you so much for trying out the product. If you have any feedback, please let me know!

@ravikar_bagri Hey Ravikar thank you :)

@mohit_praja Hey Mohit. Thank you for the feedback :)

@kiran_singh17 Thanks Kiran :)

@himanshu_mishra18 I am glad you liked the product. If you have any feedback let me know

@manak_jaat Thanks Manak

@farah_sennane I am glad you liked the product. If you have any feedback let me know

@akansha_singh13 Hey Akansha thank you for your comment. I am glad you liked the product. If you have any feedback let me know

@jordan_retro Thanks Jordan!

@nitish_sharma20 Hey Nitish. Yup we are trying to break into the field and need as much user feedback as possible. I am glad you liked the product. If you have any feedback let me know

@m_tudenham3 Thanks :)

@larry_lusam Thanks larry!

@sentry_co Hm that's an interesting question? It depends on the person who's using it, their experience and what they are asking? However I will say is that analysts save between 90-95% of their time researching and collecting information when using this tool

@anwaar_malik1 That's why I ask. It's one thing to claim people save a lot of time. it's another to provide proof right? So my question remains. What insight did this tool provide you for instance, that you would have not found otherwise. Usually LLMs only provide surface level conclusions. Has it your experience provided an insight that surprised you that it could uncover? Like finding some correlation that the market had no priced in etc? that you then exploited and earned money on? Like a concrete example?

@sentry_co This conclusion isn’t based on my personal analysis; rather, it was derived from user interviews conducted during our private tests with over 200 different analysts from various financial institutions. For instance, an analyst typically spends between 5-8 hours to complete a group analysis review of a stock, comparing it to its major competitors and forming conclusions about its position within the industry or sector. In contrast, using our product, analysts completed this task in under 10 minutes, obtaining most of the desired information from just one or two prompts, with only minor details requiring additional prompts. There are many other similar examples. The reason most large language models (LLMs) provide surface-level responses is that they are trained on a vast array of information. For example, if you have a 70 billion parameter model like Llama 3, which includes training on topics from differential equations to baking a cake, it will deliver roughly the same quality of answer whether you ask about baking a cake or solving a differential equation. At AllMind, we do not follow this approach with our models. We are hyper-focused on providing financial insights, so we tailor the design and training of our models to that end. For instance, if you ask Finchat, which uses GPT-3.5, and AllMind the same question about analyzing the impact of an attack by Hamas on financial markets, AllMind will perform better because our models are specifically trained for financial analysis.

@anwaar_malik1 Gotcha. So it's more about quickly uncovering potentials. Which used to take a long time before. then with that data, you make your assessment. It won't make the assessment for you. that's still a human task. If I understand correctly?

@sentry_co Hi Andre, that's exactly right. Our tool accelerates financial analysis and research by presenting findings and enabling the quick retrieval of information and insights from data, along with potential actionable insights. However, the final decision-making is still in the hands of a human individual. We are not a financial advisory tool who takes action on behalf of someone else.