Case Study

AI-Powered Sales Simulation Platform for a Leading Investment Firm 

AI-Powered Sales Simulation Platform for a Leading Investment Firm
Tech Stack used
Financial servicesInvestment Management

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47Billion developed a multi-agent AI simulation platform for a leading financial services firm to modernize sales representative training. The solution enabled reps to practice real-time, persona-driven conversations with intelligent feedback and contextual support. Powered by RAG and OpenAI, it personalized learning and accelerated onboarding. The platform led to a 20% increase in closure rates and 60% faster ramp-up for new hires. It transformed traditional training into a scalable, data-driven performance accelerator.

Client Challenge 

A prominent financial services and investment management firm needed a scalable way to continuously upskill its widespread salesforce. Traditional training methods and LMS systems were falling short — unable to keep pace with: 

  • Constantly evolving financial products
  • Diverse and complex customer personas
  • Stringent compliance and regulatory demands

The client required a realistic, intelligent simulation environment that could mimic live sales conversations, provide instant feedback, and reduce dependence on manual coaching. 

The AI Agent Solution 

We engineered a multi-agent AI-driven sales training platform designed to simulate complex financial sales conversations. Acting as a virtual sparring partner, the platform provides personalized, feedback-rich simulations to boost real-world readiness. 

Key Features 

Multi-Agent Roleplay Engine 
A triad of intelligent agents creates immersive, real-time training sessions: 

  • Salesperson Agent (User): The trainee engages in a dynamic, text and voice-based pitch.
  • Financial Persona Agent: Emulates clients (e.g., HNIs, conservative investors), using CRM and product data to respond authentically.
  • Sales Helper Agent: Offers smart nudges, rebuttal cues, and reference snippets from internal playbooks.

Knowledge-Driven RAG Layer 

  • Integrates Retrieval-Augmented Generation (RAG) via Azure AI Search
  • Pulls contextual data from internal product guides and competitor documentation
  • Enables informed and accurate responses during simulation

Scenario Customization Engine 

  • Tailors simulations across different customer archetypes, tone preferences, and complexity levels
  • Helps sales reps practice objection handling, upselling, and customer psychology navigation

Intelligent Feedback Loop 
Post-simulation reports assess: 

  • Tone, empathy, and fluency
  • Product knowledge accuracy
  • Objection-handling and conversion triggers

Promotes continuous, adaptive learning at scale.
Technology Stack 

  • Frameworks: Azure AI Foundry, Autogen, PromptFlow
  • Vector Search: Azure AI Search
  • Memory Management: Mem0
  • Generative AI: OpenAI APIs
  • Observability: Langfuse
  • Frontend: Flutter
  • Backend: Python

Business Impact 

  • 20% Increase in closure rates across trained sales reps
  • 40% Boost in call quality and objection-handling effectiveness
  • 60% Faster onboarding for new hires through automated, repeatable simulations
  • 30% Quicker product rollout across sales teams

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Tech Stack used
Financial servicesInvestment Management

Explore More