AppSavvy Launches AI Practice to Retrofit Existing Business Software

Singapore-based software firm AppSavvy has launched an AI transformation practice aimed at companies looking to add artificial intelligence to software they already use, as businesses move from experimenting with AI tools to integrating the technology into core operations.

The practice, launched on August 31, starts with an audit of a company’s operations, data and existing technology. AppSavvy then maps potential AI use cases and builds the highest-impact items such as AI-powered search and recommendations inside existing applications, as well as internal assistants and workflow automation. The company works across the no-code platform Bubble, Airdev’s Bubble-based Canvas framework and conventional software stacks.

The recent launch aims to address a widening implementation gap. Singapore’s ministry of manpower (MOM) found in April that 28.5% of private sector firms with at least 10 employees had begun adopting AI, but only 3.8% were integrating it into core processes. Another 7.4% were still in the planning stage and 6% were running pilots.

That leaves a potentially large market between buying an AI tool and making it work inside an existing business.

“The fastest way to get AI into a product is to build on what’s already working, not throw it away,” AppSavvy founder Will Driscoll said while announcing the practice. The company says it has shipped or supported more than 50 applications across Bubble, Canvas and custom code.

Its core focus is retrofit. AI capabilities are added to applications that continue to perform their existing functions, allowing companies to work with the data, workflows and systems they already have.

But that poses a different engineering problem from building an AI-native product from scratch. Existing applications may already contain years of customer information, payment flows, permissions and operating rules. Integrating AI means connecting new models to that infrastructure without disrupting the processes the business still depends on.

A retrofit also has its limits. If the underlying system is genuinely obsolete, its architecture will restrict how far an AI retrofit can go. In that case, migration becomes a separate technology decision, not another stage of the retrofit itself.

AppSavvy offers that service separately. Its Bubble migration practice moves applications to conventional code in stages while keeping the existing application running during the transition. However, migration isn’t always necessary, and some applications can continue with refactoring or extensions.

The real question for a business, therefore, is whether its existing architecture can support the AI capabilities it wants to add.

AppSavvy points to the US subletting marketplace Ohana as an example of an application that can become substantially more complex after starting on a no-code platform. The company says Ohana started as a Bubble application and that AppSavvy has served as a primary engineering partner, working on areas including payments, search and internal systems. Those claims come from AppSavvy and Stripe has not independently verified them.

Ohana’s payment scale can be independently established, though.

Stripe says Ohana’s monthly payment volume has roughly tripled from an average of $3 Mn to more than $10 Mn during peak periods, while the company is on track to process $60 Mn in payment volume in 2026. Hosts have received more than $38 Mn through the marketplace since 2024.

The $60 Mn figure is projected payment volume, not Ohana revenue, AppSavvy revenue or $60 Mn of growth. Also, Stripe does not attribute Ohana’s expansion to AppSavvy. The case, therefore, shows that an application originating on a no-code stack can support a sizeable transaction business. But it does not independently establish AppSavvy’s contribution to that growth.

Nor does Ohana validate the new AI transformation practice. Its relevance is architectural. AppSavvy uses it to show how software built for an earlier stage of a company’s development can remain central as transactions, workflows, and data requirements become more demanding.

Why the Retrofit Model Matters

Singapore’s AI adoption numbers suggest more companies are approaching that point. AI adoption among SMEs rose to 14.5% in 2024 from 4.2% a year earlier, while adoption among non-SMEs increased to 62.5% from 44%, according to the Infocomm Media Development Authority (IMDA). The government’s National AI Impact Programme, launched in March, aims to help 10,000 enterprises advance adoption and deepen AI use over three years.

But adoption is proving easier than integration.

In MOM’s survey, 44.9% of firms cited high implementation costs as a constraint and 42.4% cited a lack of in-house expertise. Among larger companies, 56.1% cited integration complexity and 55.4% raised concerns over data security.

The difference becomes clearer in what companies are actually doing. Smaller firms adopting AI still focus on basic steps such as training employees and providing tools, including ChatGPT, DeepSeek, and IBM Cognos Analytics. Larger firms are moving further into governance frameworks and workflow redesign.

That shifts the problem from access to implementation.

A standalone GenAI tool can sit alongside existing software. AI embedded in customer service, finance, payments, document processing or internal operations has to interact with company data and applications. It raises questions around permissions, security, reliability and how much control should remain with employees.

Singapore’s policy response is increasingly focussed on helping companies make that transition. In May, IMDA, SkillsFuture Singapore and Workforce Singapore introduced an AI for Enterprise Impact Playbook after engaging with more than 1,000 companies. The agencies said businesses struggled to determine where to start, which support suited their stage of adoption and what steps should follow.

The software industry is also beginning to organise around the modernisation problem created by older systems.

Gartner published its inaugural Magic Quadrant for AI-Augmented Code Modernisation Tools on August 3, covering vendors such as Amazon Web Services, Anthropic, IBM and Microsoft. Gartner defines the category as software that uses AI to accelerate the transformation of legacy systems through areas including documentation, testing, and code transformation.

That is not the same market AppSavvy is entering. Gartner is assessing software tools for large-scale code modernisation, while AppSavvy is selling engineering and implementation services. The connection is the underlying problem. AI is increasing pressure on companies to work out what they can preserve from existing technology and what needs to change.

That pressure is especially relevant to the no-code applications AppSavvy specialises in.

Airdev describes Canvas as a Bubble-based framework that uses templates and reusable components to help developers build applications faster. No-code platforms lowered the engineering barrier to launching software by reducing how much had to be built from scratch.

AI introduces a new requirement. Applications that were good enough to launch a business now have to accommodate new data flows, model calls, automation and security demands that may not have existed when they were designed.

For some companies, the existing architecture can support those additions. For others, its limitations will become the reason to migrate.

It makes AppSavvy’s positioning more nuanced than a simple argument for preserving old software. The company sells AI integration into functioning applications and offers migration separately when those applications reach their limits. Its commercial opportunity lies in making the distinction correctly.

That judgement matters because unnecessary replacement creates cost and disruption, while forcing new capabilities onto unsuitable architecture creates a different set of problems.

AppSavvy remains a small engineering firm, and launching one practice is not evidence of a broader industry shift. Its Ohana relationship also provides no independent proof that the company can execute complex AI transformations across larger or more regulated businesses.

The wider data, however, would explain why the service is appearing now.

Singapore already has a substantial number of businesses adopting AI, while only a small fraction have integrated the technology into core processes. As that gap starts to close, companies will have to decide not only which AI models or tools to use, but whether the software beneath them can support the change.

That creates room for a new layer of AI engineering focussed less on replacing existing technology and more on making intelligence work inside it.

AppSavvy’s retrofit practice is built for that gap.