A machine learning mobile app that assists salespeople in acquiring new contacts and accelerating sales.
  • The app seamlessly integrates CRMs with AI capabilities through machine learning.
  • Blockchain technology ensures the integrity of data by synchronizing information across the community of sellers.
  • Refactoring the existing codebase resulted in significant improvements to the app.
  • Meteor
  • React
  • Apollo
  • GraphQL
  • Meteor
  • React
  • Apollo
  • GraphQL
Project Duration
  • May 2015 - June 2021
  • Technology & Innovation
Provided Services
  • Mobile App Development
  • Technological Audit
  • Quality Assurance
  • 1 Tech Lead
  • 1 Full Stack Developer
  • 1 Test Engineer

What challenges did the client face?

  • Enhancing Customer Relation Management systems with AI capabilities

    Integrating machine learning and AI into established CRM systems requires a thoughtful approach to ensure seamless compatibility and efficient utilization of innovative technologies. The client faced the complexity of bridging the gap between traditional CRM practices and the integration of intelligent algorithms that automate complex sales processes, analyze vast amounts of data, and reduce the need for manual labor.
  • Achieving the integrity of contact data

    The client sought to streamline sales processes by maintaining complete and consistent data. This posed a challenge as they needed to address data quality issues, eliminate duplicates, and ensure that the information synchronized across their CRM systems. Blockchain technology would synchronize dispersed knowledge, ensuring data accuracy.

  • Accelerating sales with automated data entry

    The client aimed to leverage real-time data insights to expedite the sales process. However, achieving this goal without burdening salespeople with extensive manual data entry tasks presented a challenge. They needed a solution that would provide them with up-to-date information and actionable insights while minimizing the effort required to input and update data manually.

About the project

Our task was to develop a hybrid mobile application that combines standard Customer Relationship Management (CRM) functionalities with innovative automation solutions.
The primary objectives were to enable natural language processing, voice chat, and command interpretation through machine learning. To guarantee contact data integrity, we planned to leverage blockchain technology for synchronizing knowledge across the seller community.
Refactoring was required as the application was built on pre-existing code. We initiated the project with a comprehensive technological audit, which identified areas in the application's architecture that needed improvement. Based on the audit results, we recommended rewriting the front end using React and adopting a loosely coupled services architecture to enhance the scalability and future-proofing of the back end.
After successfully refactoring the application, we began implementing new functionalities to meet the project's requirements.

Technical solutions we implemented

We developed a custom Cordova plugin, utilizing the Microsoft iOS Bing SDK, to enable speech-to-text conversion on mobile devices within the app. Machine learning functionalities were integrated using Microsoft Cognitive Services, particularly the Language Understanding Intelligent Service (LUIS). This allowed, among others, the automatic extraction of new entities and activities from processed emails. Additionally, an intelligent SalesBot was implemented using the Microsoft Bot Framework.
To establish a unified and reliable source of contact data, we leveraged GraphQL technology. We created a robust and scalable single point of truth for contact information. This centralization of data facilitated efficient data management and synchronization across various platforms.
Moreover, we successfully implemented data synchronization with popular CRM platforms, including:
  • Google Contacts,
  • Google Calendar,
  • and Salesforce.
This integration ensured that contact data remained consistent and up-to-date across multiple systems, enhancing productivity and reducing manual data entry efforts.

Key functionalities

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