Let's be fair: these are capable companies with good products, an enormous amount of functionality, and decades of earned trust. But they were built for a different era. Large, established platforms naturally move more slowly, and being AI-first is not the core of how they operate. Times have changed, and that opens room for a different kind of product

Most of these platforms started as data, analytics, terminal, or search products, with AI added later to make those experiences more efficient. AdvEngine starts from the opposite direction: the core product is an AI workspace designed to execute investment workflows from start to finish, such as building models, synthesizing research, and preparing investment materials, while using data, documents, and market information as inputs

We won't pretend we win on every single dimension; every platform has advantages and disadvantages. But for most users, AdvEngine covers everything they actually need day to day, and if something is missing, we can add functionality for you. And more importantly, we are AI-first. That is our focus, and that is where we offer the best possible solution: the result is an optimal information system combined with the best AI system. There is also nothing wrong with using both: plenty of teams run AdvEngine alongside an existing platform, and the two complement each other well

One more consideration: we offer extensive customizations and integrations, and we take care of them for you. So the system you end up with is the best possible one for how your team works

General AI platforms take a general approach: one assistant for everything, extended with plugins. We offer a harness specifically designed for investors. That focus lets us go much deeper: more tools built for investment workflows, our own proprietary instruments, and extensive customization

We are also multi-model. We always choose the most capable and optimal model for each task, users can override that choice, and new innovations are implemented shortly after they launch. You are not committing to one provider, so you never miss an opportunity when another lab releases a more capable model

Just as important, we don't use AI for everything. AI incumbents try to, and it leads to subpar results. We have a large set of deterministic tools that in many cases simply work better than AI, and we apply AI where it genuinely helps

And the most important point is data. On their own, AI models are only as good as the information they can reach, and in finance much of that information sits behind separate subscriptions that take real effort to set up. Even then, marrying the data with the models, so they interpret it and use it correctly, is a task of its own. We take care of this aspect for you: the data connections you need are already in place, and we manage the relationships with data providers. You focus on your business; we take care of the rest

Honestly, this is too broad a category for definitive statements, and we're sure that somewhere in a garage someone is building a good product. But here is what we know about ourselves: we are professionals with decades of experience on both the sell-side and the buy-side, and we've been working in AI for over a decade. We know how AI works, what it's good at, what it's bad at, and therefore where to apply it and, more importantly, where not to

That leads to a systematic approach. We design systems. Where determinism wins, we build deterministic systems. Where AI is the right tool, for example when working with unstructured data, we use AI. The result is a much lower risk of hallucinations and much better outputs. This is in contrast to what most startups in the field do, which is build a wrapper around LLM providers

The product reflects that difference: it is considerably more advanced than most in the field, and we're building toward a comprehensive suite of connected solutions that speak to each other and compound your team's productivity. We've watched many startups pivot, and many eventually pivot toward exactly what we've been developing, which tells us we had a better understanding of what investors need from the start

Finally, our system is customizable. We can easily adjust the solution for your team, and if there's functionality you want that doesn't exist yet, we are always open to building it

Large investors are obviously capable of building solutions themselves. But even for them it's a challenge, and the challenge is bigger than it looks

One might have the illusion that you can just vibe code it. Good luck with that. Yes, vibe coding has improved a lot, but in many cases it simply doesn't deliver the results people expect. A real example: we've seen people build a dashboard that looked great and worked much better than we could imagine, until we asked to look at the underlying code. Almost all of it was simple simulation. Important decisions could have been based on something that was pure fake. That is the reality of vibe coding

To mitigate such issues, you can hire developers who review the code and ensure things like this don't happen. Now consider: hiring good engineers is genuinely difficult. Separately, evaluating the quality of their work is also challenging, especially if you don't have the expertise to assess it. Many people take shortcuts. It is also expensive: talented engineers can easily cost seven figures a year, likely more than the solution itself. You need to manage them, keep them motivated, and ideally find people with real experience in both investing and development, which is a rare combination

In addition, following the latest developments in the AI space is almost a full-time job in itself. Things change so quickly that keeping up takes enormous effort, and it requires a team of experienced people to track what matters, evaluate it, and fold it into the system

And while some investors could theoretically build something very good, for most the result will be subpar compared to existing market solutions. Providers serve many clients, so they have better access, better understanding of technology, and economies of scale: the same work is done once for a thousand clients instead of once for you. It's easier to ask your provider (we hope us) to customize the solution to how you imagine things than to build it yourself. And if there's something you don't want to be available to third parties, it's better to ask your provider to develop a custom part of the system that is available only to you. That is possible with us. Yes, it will cost you, but likely still far less than supporting your own system would

Building in-house also means managing infrastructure, maintaining contractual relationships with data providers, ongoing development, patching, and new additions. Take just one example: the different platforms your stack runs on. Many claim they don't use your data, yet the agreements can include terms that effectively allow them to do so. We read those agreements, speak to people, and even run a system that checks them every hour to make sure nothing has changed in a way that is unacceptable. If it has, we take action. That is the level of diligence an in-house build would require across the entire stack, and it is rarely accounted for. There is a lot of hidden complexity. You can do all of it yourself, most likely for much more money, or you can choose a partner, outsource it, and focus on your main business. Our goal is to make sure you focus on capital allocation and serving your clients, while we take care of the rest. We are a client-first company: it's our job to make sure you like the product, or we're out of business