Latent Space: The AI Engineer Podcast
00:00:00 · Aman Singh, Richard MacManus
[00:00:00] S0: We're actually not a SaaS company. We are, we are the firm ourselves. Agents prompt people more than people prompt agents, and it is a proactive kind of, service that is running for them twenty four seven. [00:00:12] S1: We're here at the, AI Engineer New York conference twenty twenty six, and I'm here with Aman Singh from Arca. And Arca's recently raised $64,000,000 for an AI native wealth management firm. So first of all, Aman, can you tell us a bit about your role at Arca and the financial services you provide and how you're using AI agents to run them? [00:00:38] S0: Yeah, for sure. Thanks for having me. My role at Arca is I lead the AI platform team. So we're building the underlying sort of harness and our capabilities to kind of run the business with AI, which is our ultimate goal. In terms of the services that we provide to our clients, with... This is a broad spectrum of financial services. So that includes sort of financial advice, financial planning for for their assets and sort of tax planning and insurance and estate planning as as people get wealthier. Maybe you are someone who just sold a business, for example. Mhmm. And there's lots of complexities with all the different partners, etcetera. Right? So we're gonna help you think through that. We also have clients that are, you know, really large technology executives that have... Are trying to figure out a real plan for retiring retiring early. Right? Mhmm. And also small families that are trying to buy their first home, for example. Yeah. Yeah.
00:00:38 · Aman Singh, Richard MacManus
[00:00:38] S0: Yeah, for sure. Thanks for having me. My role at Arca is I lead the AI platform team. So we're building the underlying sort of harness and our capabilities to kind of run the business with AI, which is our ultimate goal. In terms of the services that we provide to our clients, with... This is a broad spectrum of financial services. So that includes sort of financial advice, financial planning for for their assets and sort of tax planning and insurance and estate planning as as people get wealthier. Maybe you are someone who just sold a business, for example. Mhmm. And there's lots of complexities with all the different partners, etcetera. Right? So we're gonna help you think through that. We also have clients that are, you know, really large technology executives that have... Are trying to figure out a real plan for retiring retiring early. Right? Mhmm. And also small families that are trying to buy their first home, for example. Yeah. Yeah. [00:01:40] S1: So the clients that you have so far, do you have to kind of talk them through using agents and why you're using agents and tell them a little bit about the technology behind it?
00:01:40 · Richard MacManus, Aman Singh
[00:01:40] S1: So the clients that you have so far, do you have to kind of talk them through using agents and why you're using agents and tell them a little bit about the technology behind it? [00:01:51] S0: Yeah. So the role of AI at Arca is not to replace the adviser. It is to replace all of the work around the adviser. So our clients don't really interface with AI directly. All they experience is a level of service that has not been been been possible in this industry before delivered through the empathy and care of a human adviser. So we're building the machinery behind every client that's constantly thinking about, you know, what's... What is the market doing and how does it impact Richard? You know, what what what else is going on in your life? Like, what are you... What are those... The the textures of your life that impact your finances? And we're able to be way way more proactive, way more responsive and just deliver a better kind of level of client service using AI that does all of the the back office stuff. [00:02:41] S1: Okay. So let's get into the... Some of the technical aspects of it. Which models are you using to power your agents and how do you decide which model is best for for which action?
00:02:41 · Richard MacManus, Aman Singh
[00:02:41] S1: Okay. So let's get into the... Some of the technical aspects of it. Which models are you using to power your agents and how do you decide which model is best for for which action? [00:02:52] S0: Yeah. So today we're using the Frontier models from from the Frontier labs like Anthropic and and OpenAI. Claude is kind of our primary model has been, but Mhmm. We've gone through several iterations of models over the years. We used to be in Gemini way back when and then OpenAI, and then and then Anthropic. And what's guiding all of our decisions are our benchmarks that we have internally for all of the various capabilities of our general harness that it has to express in in running our business. And so just recently, for example, despite the external benchmarks claiming great things for Llama, it didn't really quite work out for us as as... Well, as we had expected, it was way too slow or... And it was costing a lot. And so our internal benchmark is gonna guide our model decisions. And as a principle, we we want to be model agnostic and and sort of not, you know, take a dependency on on a a certain provider or a certain harness from one of these labs and kind of keep that that optionality open for us. [00:04:01] S1: Okay. So you just mentioned harnesses. So you're using different harnesses depending on which model you're using? [00:04:08] S0: No. We have we have our our own Okay. Kind of built internally that is more model agnostic. Okay.
00:04:08 · Aman Singh, Richard MacManus
[00:04:08] S0: No. We have we have our our own Okay. Kind of built internally that is more model agnostic. Okay. [00:04:14] S1: So, you know, when when you're... When a financial adviser is doing something like a taxation report or something like that, how does the harness decide which model to use? Is that automated? [00:04:25] S0: Well, yes. So we have certain tiers of models based on different complexities of of [00:04:31] S1: tasks. Mhmm. [00:04:32] S0: So a task like assisting with a tax plan or doing a tax analysis would get our, like, heaviest tier of models. Inside of that tier, it is a little bit flexible, so you could use... So, like, an opus tier model or a sonnet tier model or a a a heavy GPT model. Mhmm. But based on... There's a lot of, like, background activity that our agents are participating in. Right? That is using the... A lower tier of model based on the complexity of the task. So that's kind of how we how we make decisions Yeah. On on which model serves which task. [00:05:04] S1: Okay. So is it the the financial adviser that's actually making that decision while they're using the product? [00:05:11] S0: Not really. They're they're they're not aware of Okay. Of that happening. We've abstracted this away Okay. From them entirely. They kind of just interact with our product as they would, and those decisions are made in... Internally by us. Okay.
00:05:11 · Aman Singh, Richard MacManus
[00:05:11] S0: Not really. They're they're they're not aware of Okay. Of that happening. We've abstracted this away Okay. From them entirely. They kind of just interact with our product as they would, and those decisions are made in... Internally by us. Okay. [00:05:25] S1: So in your presentation at at the conference earlier today, talked about a tax bill example. I just wondered if you could maybe walk us through an example of adjusting a client's portfolio, investment portfolio, you know, from identifying a need to maybe rebalance the portfolio to executing trades. Can you talk us through, you know, what parts of that process the agent does and and what parts the human advisor does? [00:05:52] S0: Right. So just like in the talk, this is a thing that will span ten days. And so agents are ultimately responsible for delivering that outcome, but there are certain parts that they do and certain parts that they do not. So in this example, when when the client emails us with this whole tax bill, there's a there's a piece of the analysis of, like, where is this client's money allocated today, and what is the most optimal way to liquidate some of that to to produce a $100,000 that is best suited for agents Right. To take on as an analysis task. [00:06:27] S1: Yeah. So that they do exactly that. Do they have certain limits though that they can, you know, they can execute a trade at [00:06:32] S0: Correct. [00:06:32] S1: X amount.