Financial advice technology is entering a new phase—one defined not just by better tools, but by better connections between them. As practices continue to invest heavily in new systems, a key question is emerging: are these tools actually delivering meaningful efficiency gains?
In this conversation, Andrew Gardner and Eric Tetik provide a detailed look at how RetireMap has evolved over the past two years, and how it is positioning itself at the centre of a more connected, AI-enabled advice ecosystem.
RetireMap’s origins are grounded in real-world frustration. Developed initially within Andrew Gardner’s own advice business, the platform was created to solve a familiar problem: traditional modelling tools were slow, clunky, and difficult to use in front of clients.
What began as an Excel-based solution has since evolved into a fully-fledged modelling platform designed for both simplicity and complexity. Advisors can now build, clone, and compare scenarios in seconds—turning what was once a back-office task into a live, collaborative experience with clients.
This focus on usability remains central. The goal is not just accuracy, but engagement—allowing clients to see and shape their financial future in real time.
A key distinction highlighted in the discussion is that RetireMap is not simply a modelling tool—it is a client engagement tool through modelling.
Rather than preparing scenarios behind the scenes and presenting them later, advisors can now build and adjust plans live in meetings. Clients can ask “what if” questions—whether it’s buying a caravan, selling an investment property, or retiring earlier—and see the impact instantly.
This creates a fundamentally different dynamic. Instead of being told what to do, clients become active participants in the advice process. The result is greater clarity, stronger buy-in, and ultimately, reduced rework after meetings.
Despite rapid adoption of new technology, many advice firms are still struggling to realise efficiency gains.
Gardner notes a growing frustration among practices that are upgrading systems but not seeing corresponding improvements in productivity or profitability. The issue, he suggests, is not the tools themselves, but how they are built and integrated.
Much of the existing technology stack is based on older infrastructure, making it difficult to connect with newer systems—particularly AI-driven tools.
This creates a fragmented environment where data is siloed, workflows are duplicated, and the promised benefits of technology remain unrealised.
In response, RetireMap underwent a complete rebuild—resulting in what is now known as RetireMap Quantum.
This new architecture is designed to be:
The impact of this rebuild is significant. Updates can now be deployed in hours rather than months, and integrations that once required major projects can be completed in weeks.
More importantly, it provides a foundation for the next phase of advice technology—one centred on interoperability and AI.
One of the most important developments discussed is the move toward a fully connected advice workflow.
Traditionally, modelling tools, CRMs, and advice generation platforms have operated independently. Advisors would need to manually transfer data between systems, creating inefficiencies and increasing the risk of error.
RetireMap is now helping to close this loop by integrating with advice generation tools such as Claris, Paradino, and others. In practice, this means that modelling outputs can flow directly into Statement of Advice (SOA) generation tools—removing the need for manual re-entry.
This shift represents a move away from “all-in-one” platforms toward a best-of-breed ecosystem, where specialised tools work together seamlessly.
Perhaps the most forward-looking aspect of the conversation is the role of AI.
Rather than simply adding AI features, RetireMap is rethinking how users interact with the platform altogether. Through integration with tools like ChatGPT, Copilot, and Claude, advisors will be able to use natural language to perform tasks that previously required manual input.
This includes:
In practical terms, this means an advisor could describe a client situation verbally and have the system build and model multiple strategies in seconds.
This dramatically shortens the learning curve and shifts the focus from “how to use the tool” to “what outcomes do we want to achieve.”
Despite the move toward AI and automation, the importance of compliance remains central.
RetireMap’s modelling engine includes built-in guardrails to ensure that calculations remain within regulatory limits—such as contribution caps or minimum pension requirements.
This creates a balance between flexibility and control. Advisors can move quickly and experiment with scenarios, while still maintaining confidence that outputs are accurate and compliant.
Another defining feature of RetireMap’s evolution is its feedback-driven development process.
Rather than building features in isolation, the team works closely with advisors—listening to their needs, prioritising improvements, and delivering updates rapidly. In some cases, enhancements are implemented and released within the same day.
Over the past 12 months alone, more than 80 updates have been delivered, spanning areas such as:
This iterative approach ensures that the platform continues to evolve in line with real-world usage.
The broader implication of this discussion is a shift in how advice technology is structured.
Rather than relying on monolithic systems, the industry is moving toward modular, interconnected tools. In this model, a typical tech stack may consist of:
Each component performs a specific function, but all are connected through shared data and integrations.
This approach offers greater flexibility, allowing firms to adapt their tech stack over time without being locked into a single ecosystem.
RetireMap’s evolution reflects a broader transformation in advice technology.
The focus is no longer just on building better tools, but on creating systems that work together—reducing friction, improving efficiency, and enhancing the client experience.
AI will undoubtedly play a major role in this future, but its effectiveness will depend on the strength of the underlying systems. Platforms like RetireMap, with structured data, built-in guardrails, and strong integration capabilities, are well positioned to act as the foundation for this next phase.
Ultimately, the goal is not just faster advice—but better advice.
Delivered through systems that are not only powerful, but intuitive, connected, and built around how advisors and clients actually work.