Custom Digital Wealth Management Platform for High-Net-Worth Portfolios

The project involved building a specialized ecosystem for a multi-family office to manage home equity investments. The system enables forecasting for portfolios where the average family net worth exceeds $30 million.

Digital wealth management platform — project banner

Location

Technologies

  • Pandas
  • Docker
  • GitHub
  • SASS
  • Redux.js
  • TypeScript
  • React.js
  • AWS
  • PostgreSQL
  • FastAPI
  • Python

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Case client

Client

The Client is a multi-family office and independent advisory firm that has provided personalized financial guidance to high-net-worth households for over 20 years. With long-standing client relationships and an average family net worth of $30M, they operate at the more complex end of private wealth management.

Business Challenge

The investment team needed to scale their home equity portfolio without sacrificing accuracy. Every contract required careful analysis, and with data processing and risk evaluation done manually, the margin for error was uncomfortably high for a firm with fiduciary obligations at this level.

Traditional wealth management technology platforms are typically optimized for liquid assets like stocks and bonds. They do not account for the specific structure of home equity contracts.

The Client needed a solution capable of generating reports based on assigned contracts while supporting strategic tracking of both active and pending ownership positions.

The system required an architecture that is well-organized and able to manage the processing, validation, and valuation of the data at the contract level in one single workflow. It had to be centered on an automated ETL pipeline and have the ability to support the development of the forecasting models from historical data without compromising the accuracy of the data.

Solution

Mission was to create a dedicated digital wealth platform that addresses the specific operational and analytical challenges of managing home equity investment contracts. The goal was to provide functionality unavailable in standard wealth management technology platforms, particularly for tracking assigned contracts, forecasting performance, and supporting decision-making.

To ensure system reliability and data correctness during contract imports, valuation calculations, and third-party cross-validation processes. This wealth management solution had to maintain strict validation controls to prevent incorrect data from influencing reporting or forecasting results.

The delivery of the digital wealth management platform required a multidisciplinary engineering team with clearly defined responsibilities.

  • Project Manager
    Oversaw delivery timelines, stakeholder communication, requirement alignment, and scope control throughout the platform development lifecycle.

  • Solution Architect
    Designed the system architecture, including the data validation framework, ETL logic, valuation workflow, and integration with third-party evaluation processes.

  • Back-end Engineers
    Implemented the core business logic using Python and FastAPI, created the ETL process, managed the contract data process, and created the valuation model infrastructure.

  • Front-End Engineers
    Developed the React and TypeScript-based interface, allowing for structured contract tracking, visualization of forecasts, and reporting.

  • QA Engineer
    Validated data integrity, tested the results of the valuation, tested the import process, and verified cross-validation mechanisms between internal and external datasets.

  • DevOps Engineer
    Configured and maintained the AWS-based cloud infrastructure, managed containerization, and ensured system stability and deployment reliability.

Core Component: Automated ETL Pipeline and Validation Engine

This wealth platform is built on an automated ETL pipeline designed from the ground up for contract-based home equity investments. Where most wealth management technology platforms rely on generic import tools, this module enforces strict validation logic at every step before data moves to the valuation layer.

Structured Ingestion Workflow

To keep the database clean, we built a structured workflow that handles contract data as it’s uploaded.

  • Schema Validation: The incoming files are validated against pre-defined structural templates to ascertain appropriate format, consistency, and completeness of the fields before processing the reports.

  • Hierarchical Location Verification: The system verifies that reports are placed within designated folders based on report type and originator. If a file is located incorrectly, the import process is halted.

  • Discrepancy Detection and Blocking Logic: In case of structural issues and misplacement of the report, the system automatically identifies the problem and prevents the database from being updated. A prompt is provided to the user to rectify the issue before reattempting the import.

Functional Core: Valuation Model and Cross-Verification Engine

The valuation model is the core of the wealth platform. It processes home equity contracts within the Client’s Alternative Strategies Fund and turns them into structured valuation outputs.

Contract-Level Processing

The model processes active and pending ownership contracts via the following mechanisms:

  • Spatial Mapping: The addresses of the contracts are mapped to Metropolitan Statistical Areas and Core-Based Statistical Areas to account for local market conditions.

  • Singular Query Dataset Generation: A single structured query pulls all contracts marked for valuation into one unified dataset, keeping outputs consistent across the entire fund.

Cross-verification Engine

To strengthen portfolio risk management and valuation reliability, the system includes a structured cross-verification mechanism.

  • External CSV Generation: The wealth platform automatically generates a formatted CSV dataset for submission to a third-party evaluator.

  • Parallel Internal Valuation: At the same time, the internal valuation model runs calculations on the identical dataset.

  • Manual Cross-Check Support: The platform enables a manual cross-check against third-party data. This way, the investment team can be sure that every valuation is sound before it’s finalized.

Platform Architecture (Front-end Layer)

The user interface was built using React, Redux, and TypeScript to support structured contract tracking and forecasting visualization.

The dashboard enables:

  • Monitoring of active and pending contracts

  • Review of valuation outputs

  • Controlled management of imports

  • Structured reporting for investment oversight

Backend and Infrastructure

Jelvix’s Backend engineers used Python and FastAPI to enable the scalability of contract data and valuation operations.

  • Data Processing Layer: Pandas is used to manipulate structured datasets within the valuation model.

  • Cloud Infrastructure: The application is hosted on the AWS cloud using EC2 and RDS to provide compute and relational database services.

  • Containerized Deployment: Docker ensures consistent deployment environments across development and production stages.

This architecture supports secure and scalable wealth management software development tailored to complex contract-based real estate investment portfolios.

Discovery and Analysis

Our development experts analyzed the client’s existing contract tracking and valuation workflow to define the system requirements and establish the logic for the automated valuation model.

SRS and Architecture Design

We developed a comprehensive specification that includes details about the data structures, contract attributes, folder validation rules, and requirements for MSA/CBSA mapping within the valuation process.

ETL Development

The “Imports” module was implemented as an automated ETL pipeline with schema validation, folder hierarchy verification, discrepancy detection, and controlled retry mechanisms.

Valuation Engine Development

Development experts from Jelvix built the internal valuation model to process active and pending contracts. The system includes automated CSV generation for third-party evaluation and structured support for cross-verification.

QA and Validation

The wealth platform was tested to ensure correct data mapping, valuation consistency, validation logic performance, and reliable cross-checking between internal and external datasets.

Deployment

The system was deployed on AWS-based cloud infrastructure with containerization of environments to guarantee consistent system operation.

Value Delivered

The project produced a purpose-built wealth platform for contract-based real estate investment portfolios. Where standard wealth management technology platforms fall short, this solution handled the specifics: tracking assigned contracts, managing pending ownership, and forecasting valuation outcomes.

The firm now operates from a centralized wealth management solution that cuts the operational risk tied to manual data handling, giving its team greater confidence in portfolio risk management decisions. Structured financial reporting automation and controlled cross-verification brought more transparency to valuations and laid the groundwork for long-term fund scalability.

Project Results

The solution was delivered in full accordance with the defined SRS and technical scope.

  • Data Ingestion Is Automated and Error-Proof
    The ETL pipeline validates schema, checks folder structure, flags discrepancies, and handles retries without manual intervention, eliminating a major source of operational risk.

  • Contract Valuations Run with Geographic Precision
    The valuation engine processes both active and pending contracts with accurate MSA and CBSA mapping, giving the team localized analysis they could not reliably produce before.

  • Internal and Third-Party Review Happen in Parallel
    The cross-verification engine generates CSV datasets for external evaluators at the same time internal valuations are processed, cutting the back-and-forth that used to slow down decision-making.

  • The Platform Runs on Stable, Scalable Infrastructure
    Deployed on AWS using EC2, RDS, and Docker, the system is built to grow with the fund without requiring significant re-architecture.

Digital wealth management platform — project results

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