Hi! Welcome to Wedbush Tech. We are building a new cloud for finance that gives teams multi-asset financial unit primitives through domain-specific APIs and applications (try saying that 10x!). Our mission is to empower other builders to unlock financial innovation across any asset class.
We are thrilled to introduce our new website, wedbush.tech, as the place to explore our platform and follow its development. We are a group of fintech builders, building on top of 71 years of Wedbush legacy and institutional capabilities into the future of finance. As builders ourselves, we have experienced first hand the difficulties navigating complex legacy integrations across banking, wealth, and trading domains.
- As fintech builders, building a multi-asset product to compete with Robinhood often means standing up separate, parallel architectures for client onboarding, order execution, and money movement (brokerage and prediction markets require separate funding accounts, while equities and futures are subject to different margin requirements).
- As wealth tech operators, expanding your capabilities today means coordinating across specialized third-party vendors (if you love them, they are partners; if you don’t, they are vendors), each requiring dedicated statements of work (SOW), distinct data schemas, and custom file integrations. All these mean slower innovation and developing solutions constrained by the most archaic part of your stack (the tail wagging the dog!).
In the cloud for compute, providers made computing infrastructure available through services that developers could consume programmatically. A team could provision resources and build products while the provider operated the underlying infrastructure. For our vision of the cloud for finance, we are building reusable capabilities for onboarding clients (whether an individual or an entity), conducting KYC/KYB, moving money, managing orders (equities, options, crypto, futures, and prediction markets), with shared collateral for partners that are building a trading platform, a startup creating a new multi-asset trading app, or an enterprise building a financial super app.
Our partners should be able to adopt these capabilities as their needs grow without re-architecting their platform.
Building APIs that are natively multi-asset
In financial services, a financial API must connect to the institutions and processes that carry out the financial activity:
- When a user hits “deposit” to fund their prediction trade, an API records the deposit memo, and orchestrates with the payment rails within the regulatory regime of a clearing FCM.
- When the same user deposits cash for a different asset class, say brokerage, the same API can be triggered, but the regulatory regime of fund transfer changes to the clearing broker dealer.
- Multiply these intricacies for various asset classes and the complexity increases exponentially.
What makes our approach unique is the institutional and infrastructure capabilities of Wedbush. Wedbush is a multi-asset clearing firm that has a rich history of operating in brokerage, futures, wealth management, and investment banking and services, with partnerships spanning the globe. This means that we can create a unified platform that absorbs the aforementioned complexities (because our regulated footprint allows us to do so).
Consider a startup expanding from equities into options and prediction markets. With a single integration, core elements such as customer identity, account structures, access controls, payment channels, and activity monitoring seamlessly carry over. This single integration also unifies the transition across different asset classes and regulatory frameworks. However, harmonization does not mean treating every asset class identically; rather, it ensures each is handled appropriately based on its specific characteristics, independent of the underlying technology. For instance, margin expands purchasing power for equities and options, but it is excluded for prediction markets since that asset class does not accommodate margin.
Through this shared infrastructure, we aim to provide our partners with these versatile options, ensuring that continuous platform enhancements drive value across all connected products.
Modernizing wealthtech beyond the surface
While retail fintech has pushed the envelope on user experience, wealthtech faces a different, more entrenched friction: vendor sprawl.
A modern wealth platform or RIA is rarely a single software system; it is a precarious mosaic of point solutions. You have one vendor for portfolio rebalancing, another for performance reporting, a third for billing and fee schedules, separate portals for client onboarding, and bespoke connectors for each custodian. Every integration demands its own SOW, custom data mappings, and endless overnight batch reconciliation files.
When your stack is assembled this way, the tail wags the dog: your product roadmap moves only as fast as your most complex legacy integration allows. Engineering teams spend their capacity keeping fragile data pipelines from breaking instead of building proprietary value. Your innovation is throttled by negotiations of SOW.
The data model problem
At the center of this problem is the data model. In the wealth management domain, the data construct is extremely complex compared to that of the typical fintech retail. Consider a platform that organizes client information around individual account records. The same person may appear separately on a brokerage account, a joint account, and a trust account. Establishing that these records refer to the same person becomes an integration problem. Understanding the individual’s different entitlement across those accounts becomes another. Applying margin to the brokerage account and the joint account becomes a third problem. Calculating fees on all three is yet another complexity.
Solving this is non-trivial.
- The 1:1 party-to-account fallacy: Most legacy custody and books-and-records systems were engineered decades ago around a rigid premise: one individual owns one account. Real-world wealth, however, is a dynamic graph. Legacy ledgers cannot natively model these multi-tiered entity relationships. Instead, they force firms to bolt on secondary databases, sync scripts, and manual workarounds just to understand who actually has authority over what capital.
- APIs designed for screens, not domains: When legacy custodians finally built APIs, they did not build them around clean, composable domain primitives. They built them to mirror their legacy web portals (because the web screen existed first). If a back-office clerk historically clicked through three disparate screens to update an address, verify identity documents, and assign an account code, the custodian’s API simply exposed those three fragmented interface steps. They exported their UI navigation debt directly into developers’ codebases.
This foundational data problem is why almost all "Wealth AI" is stuck in the front office.
The industry is saturated with AI tools for recording client meetings, transcribing notes, drafting follow-up emails, and generating marketing collateral. These tools are helpful, but they are surface-level. They sit outside the core ledger where they cannot move money, reallocate risk, check policy, or trigger execution.
True autonomy requires reliable state machines. For an AI agent to execute complex middle- and back-office tasks, it needs a rigorous, deterministic ontology; a semantic graph of accounts, authorities, policy, tax lots, asset classes, and regulatory boundaries. It needs domain-specific APIs to perform both read and write operations efficiently.
When your data model is an ambiguous patchwork of screen-first endpoints and flat 1:1 account tables, giving an autonomous agent direct access to your back-office is a compliance and operational liability. The agent cannot act because the underlying systems cannot deterministically tell it the truth about entity relationships, margin rules, or asset constraints.
Our approach to a cloud for finance starts with rebuilding the ledger and defining these concepts explicitly: parties (people and organizations), accounts, ownership, authority, positions, and transactions. An ontology is the shared definition of these concepts and their relationships.
We believe that to fix the ontology, we need to solve the data problem at the source. This is why we are rebuilding the ledger around native domain primitives and clean entity relationship graphs. When the ledger natively understands multi-asset rules, multi-entity relationships, and regulatory regimes, AI can finally move from taking notes to executing operational workflows. Workflows such as autonomous multi-entity rebalancing & cross-asset tax lot harvesting, proactive collateral and liquidity management, and continuous embedded compliance auditing for Reg BI, suitability thresholds, and account mandate drifts, among other things.
We believe that the future of wealthtech isn't another dashboard or a smarter meeting summarizer. It is a unified financial backbone that turns fragmented operations into composable, autonomous capabilities.