How We Developed an MVP for the Diligence Fund Distribution Platform 

# Fintech # Web development
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  • Digital Information and Connection Hub Development: delivering the first MVP version of the diligence fund distribution platform for our client, enabling him to pitch to investors and raise funds for the next iterations of the startup.
  • Intelligent Search Implementation: providing our Artificial Intelligence and Machine Learning expertise to introduce a highly efficient matching process for users of the product, as well as smart search functionality.


The client is an asset and fund management expert with nearly two decades of experience, introducing a new diligence fund distribution platform. 

The goal of the client’s business is to provide solutions for equities, fixed incomes, commodities, alternatives, and emerging new asset classes – through separately managed accounts, mutual funds, interval funds, exchange-traded funds, and limited partner structures. 

With new partners joining and connecting all the time, the client’s website always needs to be updated with the latest offerings and accurate information, responding to the slightest market changes.

Team Size:


The product is designed to connect investors, asset managers, and wealth managers on a single platform and help them find business partners who match their needs most.

The key components of the product include such features as a landing page, news thread, search and filter, user cabinets, community members matching business goals and working experience, and messaging between users on the platform.

Goals and objectives

  • Build an MVP of a Connection Hub for Experts in the Investment Industry: Create a centralized platform that  fosters a collaborative environment and facilitates the formation of partnerships.
  • Empower the Platform with Effective Search Mechanism: Leverage AI/ML technologies to build a web platform that will help asset managers easily match with wealth managers, shortening their time for searching for a reliable business partner. 
  • Track User Data Activities: Provide the client with information on user activities to find possible bottlenecks and optimize the platform’s performance in the future. 

Project challenge

  1. Limited to No User Data and History:
    Develop a solution to match investors with wealth managers and asset managers, despite the initial lack of necessary information.
  2. Noise Data:
    Manage the crawled data properly, as the AI-powered data crawling process produces a lot of irrelevant data that should be filtered to obtain accurate end results.


Probably, the most interesting part of this project was introducing AI search for matching members of the community when they were looking for potential business partners with common goals.

AI search was intended to give filtered search results in contrast to the general search that would range users by basic information like country, state, years of experience, generation, gender, etc. The AI Search included other variables that could be of value when choosing the potential business partner and increased the likelihood of positive cooperation like vehicle preferences, ESG, Asset class, AUM, and specialty as well as the description text in the bio of each user.

We started by working on the PoC concept of the ML module for matching community members (asset managers, wealth managers, and investors), outlining ideas for matching algorithm approaches based on user profiles. When the research was complete, it was time to conduct some experiments with a Natural Language Processing-based and similarity search approach. Finally, it came to creating a strategy for data collection in the future for building a more advanced matching algorithm.

After the ML module PoC was completed, we received a green light from the client to start on the software development of Digital Information and Connection Hub for Investment, which included implementing a web crawling mechanism with Apify, developing a news line and chat, adding HubSpot CRM integration and user tracking activities with Heap io, Segment, and Google Analytics. 

Tech Stack

  • React.js WebReact.js
  • TypeScript  WebTypeScript
  • Material UI WebMaterial UI
  • Java API Java
  • Quarkus API Quarkus
  • GraalVM API GraalVM
  • Hibernate API Hibernate
  • PostgreSQL API PostgreSQL
Third Parties
  • Auth0 Third PartiesAuth0
  • Apify Third PartiesApify
  • Segment Third PartiesSegment
  • Third
  • HubSpot Third PartiesHubSpot
  • Google Analytics  Third PartiesGoogle Analytics

Our results

We successfully developed the planned functionality on time and on budget.

  1. Introduced AI-based Matching Process for Wealth and Asset Managers: developed key product functionality by effectively implementing AI/ML technologies. 
  2. Created a Robust Web Crawling Mechanism in 1 Week: successfully implemented it with Apify and resolved the challenge of noise data. 
  3. Developed a Fully-Functional Website: delivered the first version of the website including essential features like User Cabinet, NLP-powered Search, and Filters. 
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