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Train employees with AI simulations for sales, service, and leadership.
AI Conversation Coach is a training platform that uses conversational AI simulations to help employees practice realāworld scenarios in sales, customer service, and leadership.
iRoid Solutions delivered a complete ecosystem, Flutter mobile apps, a web app, backend APIs, and an admin panel, so organizations can design custom scenarios, assign them to learners, and track performance improvements over time.
Tools
Xcode
Android studio
VS code
Git
UI/UX
Figma
Cloud/Server
AWS
GoDaddy
Apache
Database
MySQL
Back-end
ExpressJs
Front-end
EJS
Html Bootstrap
HTML5
CSS3
JavaScript
jquery
Mobile
Flutter
Dart
Flutter
1 Member
Node.js
1 Member
QA
1 Member
UI/UX
1 Member
Project Manager
1 Member
Project Description
Traditional roleāplays and classroom trainings often struggle to feel realistic, repeatable, and scalable. The AI Conversation Coach app was created to solve that by giving employees a safe space to practice highāstakes conversations with AI personas that respond dynamically, just like real customers or team members.
Users log in through the mobile app or web app, select a scenario (for example, handling objections on a sales call or managing a difficult performance conversation), and then engage in a simulated dialogue with an AI counterpart. The AI adjusts its responses based on what the learner says, creating a natural flow instead of scripted Q&A.
On the technology side, our team built Flutter apps for Android and iOS, backed by Node.js/Express APIs and a MySQL database, all deployed on AWS and integrated with a web front end built with HTML5, CSS3, Bootstrap, JavaScript, jQuery, and EJS. This stack supports realātime chat, scenario configuration, roleābased access, reporting, and scalable AI usage through tokenābased limits.
The admin panel allows training managers to design their own conversation scenarios, define AI personas, assign modules to user groups, and review performance data, turning the app into a flexible training platform rather than a fixed set of lessons.
Business Goals
The core business goal behind AI Conversation Coach was to help companies significantly improve frontline communication skills, close more deals, resolve issues faster, and lead teams more effectively, without increasing training overhead.
By offering customizable simulations and realātime feedback, the platform aims to shorten rampāup time for new hires, provide ongoing practice for experienced staff, and give training teams a measurable way to see who is ready for live conversations and who needs more coaching.
From the client’s perspective, the product also needed to become a reusable asset: a place where training content can be updated, expanded, and tailored for different industries (e.g., insurance, healthcare, SaaS sales) while leveraging the same technology backbone.
Features
AIāDriven Conversation Simulations
Employees practice sales, service, and leadership dialogs with AI personas that respond contextually, mimicking real prospects, customers, or team members.
Custom Scenarios & AI Personas
Trainers create scenario outlines and define AI personas (e.g., skeptical customer, anxious patient, resistant employee), then assign these simulations to learners based on role and skill level.
RoleāSpecific Training Paths
Sales reps, service agents, and leaders see different scenario bundles tailored to their daily conversations, ensuring practice is relevant and focused.
RealāTime Feedback & Coaching
After each simulation, learners receive feedback on their responses—covering areas like clarity, empathy, objection handling, and closing—along with suggestions to improve future conversations.
Gamification & Progress Tracking
Points, scores, and completion badges make practice engaging, while dashboards show individual and team progress over time.
MultiāPlatform Access (Mobile + Web)
Employees can practice on Android, iOS, or web, making it easy to fit training into busy schedules, whether at the office or on the go.
Admin Panel for Scenario Management
Training managers use a webābased admin panel to create, edit, and publish scenarios, manage user accounts, allocate tokens for AI usage, and monitor training activity.
Scalable Cloud Architecture
Node.js/Express APIs, MySQL, and AWS hosting provide a robust backend that can support multiple organizations, high chat volume, and ongoing content updates.
Typography & Color Palete
Challenges & Solutions
Challenges
Solutions
Making AI conversations feel realistic, not robotic
If AI replies feel generic or scripted, learners don’t take the training seriously and the impact drops.
We integrated configurable personas and scenario logic so the AI can react differently based on user input, giving each simulation a natural backāandāforth flow that mirrors real conversations.
Allowing trainers to design scenarios without technical complexity
Training teams needed the ability to build and modify scenarios themselves, without relying on developers for every change.
The admin panel includes intuitive forms and editors for scenario outlines, persona traits, and assignment settings, making it easy for nonātechnical staff to manage learning content.
Measuring skill improvement beyond completion rates
Simply finishing a scenario doesn’t prove whether someone actually improved their communication skills.
We implemented scoring models and feedback metrics tied to conversation quality (handling objections, empathy, structure, confidence), giving trainers actionable insight into who is progressing and where they need support.
Supporting different industries and use cases in one platform
The app needed to work for sales, customer service, healthcare, leadership coaching, and more, each with their own language and context.
The scenario system was designed to be domaināagnostic, allowing clients to plug in industryāspecific content and personas while reusing the same AI simulation and coaching engine.
Managing AI usage costs while keeping training accessible
AI chat usage is tokenābased, and the platform needed to avoid runaway costs as usage grows.
Admins can set token limits, monitor consumption, and design scenarios with efficient dialogue flows, balancing rich training experiences with predictable resource usage.
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