A Singapore-based AI-first startup partnered with InteligenAI to transform its innovative sales training platform into a robust, enterprise-ready SaaS platform. We re-engineered their AI modules, rebuilt the core architecture, and created a unified workspace designed for performance, scalability, and real-world enterprise adoption.
The client set out with a clear vision:
to reinvent how sales teams practice, learn, and manage customer conversations using AI.
Their platform combined:
- Generative AI
- Avatar-led simulations
- Workflow automation
- Real-time content generation
Together, it functioned as a flight simulator for sales teams, enabling them to practice difficult conversations, refine pitches, and automate post-meeting workflows.
As interest grew from enterprise sales organizations across APAC, the founders realized their early prototype would not support the scale, security, or performance demands of large teams.
The challenge:
1. Disconnected AI prototypes
The initial system contained several independent AI experiments—sales simulation, transcription, and deck generation.
Each worked in isolation and wasn’t built for long-term extensibility or cross-platform usage.
2. Performance & latency constraints
Real-time AI workloads involving avatars and conversational agents required reliable, low-latency responses.
The existing architecture struggled with spikes, inconsistent output times, and lacked the infrastructure needed for smooth, simultaneous training sessions.
3. Limited productization & UX coherence
The platform still felt like multiple standalone tools rather than a single SaaS product.
Key gaps included:
- Non-standardized authentication
- Inconsistent data flow
- Fragmented UI/UX
- No centralized middleware for routing, security, or session control
These limitations affected demos, slowed feature delivery, and made enterprise onboarding complex.
Our approach:
InteligenAI came onboard as the startup’s offshore technology team responsible for transforming the product into a scalable, enterprise-ready solution.
Our work focused on three pillars:
- Rebuilding the core AI capabilities
- Re-architecting the backend and frontend systems
- Productizing the sales training platformfor enterprise use
Our AI solution:
1. Modular AI framework
We redesigned each AI feature to function as a standalone yet easily composable component:
- AI Pitch Simulator
- Avatar-based conversational agent
- Deck and collateral generator
- Real-time transcription and summarization flows
Each module was containerized, enabling:
- Independent scaling
- Lower operational cost
- Consistent performance
- Faster experimentation and iteration
A unified control layer was introduced to manage prompts, model configs, and agent behaviors, making the entire AI stack easier to extend.
2. High-performance application architecture
To support real-time training simulations and content generation, the application needed a complete backend overhaul.
We implemented:
- Message queues for asynchronous AI tasks
- Caching layers to reduce repetitive calls
- WebSocket-based event streaming for real-time interactions
- Structured middleware to manage:
- Authentication
- Authorization
- CORS
- Session routing
- Security policies
This upgrade significantly improved reliability and responsiveness during peak usage.
On the frontend, we rebuilt the entire interface using React, creating a unified workspace where users could simulate conversations, collaborate, generate content, and manage workflows seamlessly.
3. Enterprise-ready productization
With the architecture modernized, the next phase was building around enterprise expectations.
We introduced:
- Multi-tenant workspace support
- Enforced access controls and role-based permissions
- Clear separation of user, team, and session data
- Consistent UI patterns across all modules
- Streamlined navigation for simulation, coaching, and review flows
The sales training platformnow presented itself as a single, cohesive SaaS application rather than a set of AI tools stitched together.
Results & impact:
By the end of the engagement, the product had evolved from an early-stage prototype into a scalable, global-ready AI training sales training platform.
1. Performance & experience:
- Up to 70% lower latency across avatar and simulation flows
- Real-time responses felt natural, improving training engagement
- Stable performance even during concurrent sessions
2. Product & delivery velocity:
- Modular AI components reduced the time needed to ship new features
- Team could launch new simulation scenarios without architectural changes
- Clear separation of AI services simplified maintenance and updates
3. Enterprise readiness:
- Secure multi-tenant access and data controls
- Consistent UX enhanced adoption across sales teams
- Backend architecture supported future AI agent expansion
4. Business outcomes:
- The client was able to confidently pitch to enterprise customers
- The platform reached a production-grade quality level
- Infrastructure was prepared for APAC and global rollouts
For many companies building AI-driven sales enablement tools, the challenge isn’t the idea—it’s the execution at scale.
This project demonstrates how a vision-led prototype becomes a market-ready, enterprise-grade AI sales training platformwith the right:
- Architecture
- Product decisions
- AI engineering
- UX strategy
The client now has a strong foundation to lead the next wave of AI-powered sales training and workflow automation.