An Australia-based wealth management firm brought us in to build a custom platform that consolidates client portfolio management, financial data, and advisory workflows into a single system.
They were already running Microsoft Dynamics and Power Platform as their core CRM, managing client relationships and financial records. As the advisory processes grew in complexity, the existing setup couldn't keep up. Our client needed a tool that could pull together data from their internal systems, external financial platforms, and custom portfolio modeling tools quickly and effortlessly.
The platform gives advisors a unified workspace to review client data, break down portfolio composition, run "what-if" scenarios, and build more accurate financial recommendations.
Currently in works: AI-driven scenario generation and advisory suggestions trained on historical client data and past advisor decisions.
The client's advisory team was juggling multiple disconnected systems. As a result, managing portfolio data and turning it into actionable financial recommendations took far longer than it should have.
The team had a Model-Driven CRM inside the Microsoft ecosystem, but advisors still had to hop between several external financial platforms to pull together portfolio data, client investment details, and forecasting insights. That fragmentation slowed down daily operations and made it hard to maintain a clear picture of each client's financial position. Portfolio calculations added another layer of complexity. Every equity and investment asset carried dozens of interdependent parameters, affecting portfolio performance, profitability, tax implications, and forecasting models. That means, changing one variable led to recalculations ripple across the entire portfolio.
Keeping the system performant while processing those calculations and syncing data from external APIs in real time turned into one of the core technical challenges of the project. Beyond the technical side, the client wanted to scale their advisory operations and cut down on the manual effort involved in preparing client proposals and financial scenarios.
The key request was to build a centralized platform that consolidates data, supports advanced "what-if" portfolio modeling, and lays the groundwork for AI-assisted recommendation workflows down the line.
We built a custom wealth management platform that connects a fully tailored React frontend with the client's existing Microsoft Dynamics and Power Platform ecosystem, giving advisors a single interface for portfolio management, financial forecasting, and client advisory workflows.
The project kicked off with a deep-dive into the client's existing Model-Driven CRM, along with its architecture, database structure, and third-party financial integrations. That audit shaped the solution design: a platform capable of consolidating portfolio management, forecasting, and advisory workflows without ripping out what was already working. The core stack included React, .NET 8, Microsoft Dataverse, Power Platform, Power Automate, and Azure. This combination let us bridge the client's existing Microsoft infrastructure with a web application experience flexible enough to meet their custom requirements.
To bring financial operations into one place, we integrated with Netwealth, Fin365, and Lonsec APIs. Advisors can now access portfolio data, investment details, and financial metrics from multiple sources directly within the platform, no tab-switching required.
A significant portion of the build centered on portfolio forecasting and scenario modeling. Advisors can simulate changes in portfolio composition, investment performance, tax exposure, and profitability across different financial scenarios. The challenge: even a minor update to a single equity can trigger recalculations across the entire portfolio. Keeping that performant required targeted database and API optimizations, caching strategies, and ongoing refinement of performance-critical workflows throughout the project.
The platform replaced a fragmented multi-system workflow with a single environment where advisors can access client data, portfolio metrics, and scenario modeling tools in one place. Day-to-day operations got noticeably simpler. Visibility into complex portfolio structures improved, and the time needed to prepare client proposals and financial recommendations came down. Consolidating external financial integrations and automating the bulk of portfolio calculations gave the client a more scalable foundation to grow their advisory operations.
The platform was built with the next phase in mind. Upcoming work focuses on AI-assisted advisory workflows using LLM integrations, OpenAI APIs, Microsoft Foundry, and semantic search. The aim is to help advisors surface relevant portfolio scenarios and generate recommendations based on historical client data and past advisory decisions.
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