Most software teams have experienced the same moment at least once in the last two years: someone types a sentence into an AI builder, waits a few seconds, and watches a complete application appear in the browser. You can add a table, wire an API, build a dashboard, adjust logic, and publish a usable interface almost instantly. For many teams, it feels like the future arrived early.
It’s easy to see why this creates a growing belief that if AI can build internal tools, CRM-like systems, customer portals and modern user-facing lifestyle applications, then building financial products should not be far behind. If an app is “just workflows, inputs and API calls,” then a lending product or a digital wallet starts to look like a slightly more complex variant of the same thing.
This assumption only holds until you examine how financial products actually behave. Once you look deeper, the difference between “build an app” and “build a financial product” is not a matter of degree; it is a matter of nature. One is a functional construct. The other is a regulated behavior carried across multiple systems, each with obligations, constraints, and expectations that must remain correct under stress, scrutiny and scale. Generic AI builders are not built for this, and the gap shows up quickly when teams attempt to cross that line.
The surface similarity that misleads teams
AI app builders have matured rapidly. There are several platforms that turn natural language prompts into working software. They wire interfaces and APIs, automate workflows, handle permissions, and produce outputs that satisfy the majority of non-regulated software needs.
This works exceptionally well for many categories of software: internal operations tools, data-entry systems, customer portals, productivity apps, reporting dashboards, or API-driven business workflows. Their strength lies in assembling general-purpose applications quickly and reducing the amount of routine code teams need to maintain. The misunderstanding begins when financial products are treated as another instance of the same pattern.
Why financial products aren’t “just apps”
Financial products —whether a lending workflow, a wallet, a payment instruction, or a savings construct —are regulated behaviors with obligations, constraints, and pathways that must comply with rules set by banking authorities, regulators, card networks, and risk frameworks. They sit across multiple systems: core banking, payment processors, ID verification systems, fraud engines, ledgering layers, PSPs, and CRMs. Each of these systems has expectations about sequencing, idempotency, reconciliation, and auditability. This is the part most generic builders are not designed to understand.
The bulk of a financial product’s complexity lives below the user interface and outside the basic workflow logic. Exposure enforcement, velocity limits, interest schedules, settlement windows, multi-entity obligations, dispute flows, reconciliation pathways, ledger correctness, failover behavior for transaction retries, and mandated compliance sequences all fall into this category. These elements are not simply “conditions” or “rules”; they are domain-specific behaviors with legal and operational consequences.
The real gap becomes visible once you dig deeper
It’s important to avoid the common oversimplification that generic builders “cannot build financial products.” They can certainly produce an app that resembles one on the surface—screens, entry points, conditions, and API calls to payment or banking providers. They even support authentication, permissions, and automation.
The gaps start becoming visible once we go deeper into the nuances of financial products. Generic AI builders allow you to write logic for domain-specific behaviors, but they do not natively provide domain awareness, safety rails, or structural guarantees. They treat everything as a general rule rather than a domain-bound obligation, even when some of it is critical to the integrity and legality of the product.
A generic AI builder can call a payment API, but it does not understand that different rails (ACH, RTP, UPI, PIX, card networks) require a specific sequence of validation and clearing steps. It can create decision flows, but it cannot distinguish between an ordinary conditional and a regulatory checkpoint that must always occur. It can store data, but it does not understand the difference between a normal update and a ledger movement that must be atomic and auditable. It allows developers to build, but it does not prevent them from assembling something that is non-compliant, fragile under load, or vulnerable to race conditions.
This is why engineering teams which often start quickly with a generic AI tool but end up writing and maintaining extensive financial-specific logic outside the builder—risk scoring, ledger movements, settlement rules, dispute flows, and regulatory pathway. Eventually, the “quick build” becomes an unexpectedly lengthier and complex engineering project.
In other words, these platforms give teams a canvas, not financial grammar. None of this is a flaw in these platforms; it is simply a reflection of their design intent. They are meant to be general-purpose builders, not finance-aware infrastructure.
The custom logic problem: Two categories, one bottleneck
All software contains custom logic. Most of it is healthy and expected. The real distinction lies in the type of custom logic required. Financial products introduce a different category of custom logic that generic AI builders do not reduce.
Application-Level Logic (manageable) – UI transitions, field validations, routing decisions, notifications, and basic workflow logic. Generic builders excel here.
Domain-Level Logic (high risk and expensive) – Exposure enforcement, interest calculation, repayment cycles, risk evaluation, regulated triggers, ledger posting rules, multi-entity settlement sequences, dispute paths, ledger integrity rules and reconciliation flows.
This second category is where most fintech engineering complexity resides. It is also where the consequences of incorrect behavior are significant. Errors in this layer do not just break the application; they break obligations to customers, partners, and regulators. This is the part fintech teams spend years encoding and maintaining, and it is where most engineering time is consumed. Generic builders do not reduce this burden; they simply move it into custom scripts, backend services, or external systems.
What a finance-native platform must provide
A platform intended for composing financial products needs a fundamentally different architecture. Its design must centre around domain primitives that reflect common financial product behavior—interest logic, repayment rules, transfer primitives, limit engines, KYC/KYB flows, settlement logic, audit recording, and risk checkpoints. It must enforce safe defaults and ensure that workflows adhere to compliance and regulatory conditions. It must treat transactions as multi-system, multi-obligation events that require deterministic behavior, not simple API calls. And it must generate the traceability required for audits and regulatory reviews.
These are not enhancements to a composable fintech architecture; they are foundational requirements.
How Tapestry plugs the gaps in this landscape
Tapestry was built to address these domain-specific constraints directly. It is not trying to replace the variety of vastly popular and evolved general-purpose AI-product builders; those platforms solve a different set of problems and do it well. Tapestry, instead, addresses the specific challenge of composing financial products without forcing teams to reinvent the underlying financial logic each time.
Its components are designed around financial constructs—transfers, limits, fee logic, interest models, KYC/KYB stages, ledger behaviors, and multi-rail orchestration. The financial workflow orchestration layer understands the sequence, integrity and timing requirements of regulated flows. Its AI is trained to construct domain-correct flows, not just functional workflows. And it embeds governance, auditability, and compliance directly into the composition process.
The result is not an elimination of all custom logic — no platform can do that — but a reduction of the high-risk, complex domain-level business logic that typically slows down financial product development and introduces operational risk. What remains is the application-specific logic that teams should own anyway.
A more realistic view of the landscape
Generic AI builders for fintech will continue to play an important role in accelerating software development across industries, including in ancillary functions around financial systems. They are excellent for portals, dashboards, back-office tools, and customer-facing interfaces that sit around regulated systems. But using them as the primary platform for composing financial products introduces risks and overheads that most organizations only discover once they try to scale or seek approval from compliance and risk teams.
Financial products require platforms that understand their structures, regulated constructs and the domain they operate in. As financial services continue to embed themselves into every type of digital business, the need for domain-native composition platforms will only grow. The industry is discovering that the complexity of finance cannot be abstracted away by treating it as a generic application problem. Building fintech products requires a platform that understands these nuanced structures from the inside out.
If you want to explore how Tapestry reimagines fintech product composition, contact us here for a demo.
