Rethinking the Brokerage Platform for the Age of AI

For two decades, brokerages competed on building a better screen. The next contest is quieter: whether your broker can be operated by the AI you already talk to.

The retail brokerage app has reached a familiar plateau. Across Asia's major markets, commissions have fallen toward zero, charts and screeners have converged on the same handful of features, and onboarding has compressed from weeks to minutes. The interface has stopped being a meaningful differentiator. What remains scarce is not information about the market, but the time and attention it takes to operate the software that holds it.

Meanwhile, the way people work has shifted underneath the industry's feet. Millions now delegate research, drafting, and analysis to conversational AI as a matter of routine. Yet when the same person turns to their portfolio, the workflow snaps back a decade: open a dedicated app, navigate its menus, and do everything by hand. The assistant that helps with the rest of their day stops at the brokerage's front door.

The assistant-in-the-app era

Brokers were not slow to notice AI. From 2023 onward, several Asian platforms embedded conversational assistants inside their own apps. Tiger Brokers introduced TigerGPT in April 2023, built on its content library and a third-party language model to answer research questions and surface company data in seconds. The company was explicit that it is not a robo-adviser and does not issue recommendations. moomoo and others followed with comparable in-app assistants.

These tools genuinely lowered the effort of finding information. Structurally, though, they kept the same shape: the AI is a feature inside the broker's product, and the product remains the place the investor must go. Such an assistant can describe a holding or summarise an earnings report, but it cannot act on the investor's behalf in the environment where they already spend their day. The conversation and the account stay in separate rooms.

From answering questions to acting on them

A more structural change began when brokers stopped treating AI as a single in-app feature and started packaging their own capabilities — quotes, fundamentals, portfolio data, order handling — so that an AI agent could actually use them, rather than merely describe them. Longbridge has pursued this through Longbridge OpenAPI, its developer platform, whose stated purpose is real-time markets built for AI.

The most visible expression is the Longbridge AI Skill: a package an investor can install into the AI assistant they already use — among them Claude, ChatGPT, Cursor, Gemini, and Codex — so that the assistant can pull live quotes, read portfolio data, follow news, and help prepare orders in plain conversation, without switching apps. The same services reach developers through a command-line tool, software development kits in seven languages, and a hosted connection that AI coding assistants can link to directly, spanning the US, Hong Kong, Singapore, and mainland China markets. The plumbing matters less than what it unlocks: the broker becomes a capability the AI can pick up, rather than a destination the user must open.

One point is worth stating plainly, because it defines the boundary. These tools and the language models that use them act on the user's instructions; per Longbridge's own disclosures, they do not constitute investment advice, recommendations, or solicitations. The broker's role is to provide the connection and the execution, not the analysis or the judgement. The thinking still belongs to the investor and whatever AI they have chosen to work with.

The difference from the earlier wave is architectural. Where the assistant model puts AI inside the broker's own product, this approach lets any compliant AI agent use the broker from wherever the investor already works. The destination is no longer the app; it is the conversation.

Why building the foundations first matters

This quietly changes what a brokerage competes on. Once AI is the layer through which people reach the market, the contest moves to less visible ground: latency, reliability, the breadth of markets reachable through one connection, and how cleanly a broker's services can be called by software it does not control.

Longbridge's answer runs across several layers. Inside its own app sits LongbridgeAI, the conversational AI assistant — previously known as PortAI — that lets a user ask about a stock, a holding, or a market in natural language and get an answer without digging through menus. Facing outward, the Longbridge AI Skill carries comparable capabilities into whatever assistant the investor already prefers.

The connecting idea is what the firm calls the Invest Loop: a disciplined, closed-loop way of working that runs from spotting a signal, to analysing it, to planning a position, to executing, and finally to reviewing the outcome so the next decision starts better informed. It borrows the logic of the scientific method — observe, test, act, review — and applies it to investing, with AI handling the legwork at each turn. An AI-native investing app, designed to carry an investor through those stages in one continuous flow rather than scattering them across separate tools, is on the roadmap. In April 2026, the broader strategy was recognised with the AI-Brokerage award at Singapore Business Review's Technology Excellence Awards.

None of this erases the difficult parts, and a serious treatment of the trend has to name them. Agent-mediated trading raises real questions about authorisation, error handling, data accuracy, and the line between assistance and decision-making. Models can be wrong or work from stale information, so a responsible design keeps a person firmly in the loop: the investor confirms, the regulated broker executes, and the AI's output is treated as reference material rather than a command. Brokers also operate under market-by-market licensing, and an execution-only service stays execution-only however natural its interface becomes; order-execution features, in particular, tend to be introduced progressively from one market to the next, only after the necessary regulatory reviews. The platforms that earn lasting trust are likely to be those that build these guardrails into the design from the start rather than bolting them on afterward.

The next standard

The history of financial technology is, in large part, a history of interfaces quietly disappearing. The trading floor gave way to the terminal, the terminal to the web page, the web page to the app. The next thing to fade may be the app itself, dissolving into the assistant a person already uses for everything else. What replaces it is less a slicker screen than a more disciplined way of working: a loop an investor can run, with AI carrying the load at each turn. A broker that lays this groundwork early is not merely shipping a feature. It is helping to define how a generation will reach the markets, and earning a voice in the standard the rest of the industry eventually adopts.

For readers curious about what this looks like in practice, LongbridgeAI answers questions inside the app today, and the Longbridge AI Skill can be installed into an assistant such as Claude, Cursor, or Codex so it works with live market data in plain conversation.

This article is for informational purposes only and does not constitute investment advice, or any recommendation or solicitation. The AI tools referenced assist with market analysis and trade execution based on user instructions and may produce inaccurate or outdated information; users should verify independently before acting. Investing involves risk; please assess carefully before participating.