Squinia

Squinia is an AI simulation platform for improving soft skills communication using chat, voice and video simulations, It also allows training teams to train their learners.

Comment

Inspiration

Technical knowledge alone isn't enough to succeed.

Every year, thousands of learners graduate from bootcamps and training programs with strong technical skills, yet many struggle to communicate their ideas effectively during interviews, client meetings, and workplace conversations. The gap isn't a lack of knowledge, it's a lack of realistic practice.

Traditional role-playing depends on mentors, classmates, or managers being available, making practice expensive, inconsistent, and difficult to scale.

We built Squinia to solve this.

Squinia is an AI-powered communication simulation platform where learners and professionals can practice interviews, stakeholder meetings, difficult conversations, customer interactions, and workplace scenarios with intelligent AI characters that respond naturally in real time.

Instead of watching videos or reading theory, users learn by doing, building the confidence and communication skills needed to succeed in real-world situations.

What it does

Squinia allows organizations and individuals to create realistic AI simulations for communication training.

A user can:

  • Practice technical interviews
  • Handle difficult stakeholders
  • Prepare for behavioral interviews
  • Rehearse customer conversations
  • Train leadership communication
  • Receive instant AI feedback
  • Review transcripts and evaluations
  • Improve over time through repeated simulations

Every interaction is stored so users can track improvement and organizations can measure training outcomes.

How we built it

Our goal was to prove that production-ready AI applications don't need to sacrifice speed.

We used v0 to rapidly build a polished Next.js interface while AWS databases provide the production-grade backend that powers persistent conversations, scenario management, analytics, and evaluation history.

Our architecture separates simulation, evaluation, authentication, and data storage so the application can evolve from a prototype into a scalable SaaS platform without changing its foundation.

The application combines:

  • AI-powered conversation generation
  • Persistent conversation history
  • Structured evaluation storage
  • Real-time interaction
  • Modern full-stack architecture
  • Cloud-native deployment

Challenges we ran into

One of our biggest challenges was making AI voice conversations feel natural.

Our initial implementation used WebSockets for real-time communication. While it worked well for streaming audio, it made natural conversation difficult because users couldn't easily interrupt the AI while it was speaking, resulting in interactions that felt rigid and less human.

To solve this, we redesigned our real-time communication layer using WebRTC. This gave us lower latency and enabled more natural turn-taking, allowing users to interrupt, respond, and converse with the AI much like they would with another person.

This experience reinforced an important lesson: building great voice AI isn't just about choosing the right language model, it's about selecting the right real-time communication architecture to deliver a seamless conversational experience.

Accomplishments that we're proud of

  • Built a production-ready AI communication platform instead of a simple chatbot demo.
  • Created realistic AI simulations that help users practice interviews, stakeholder meetings, and workplace conversations in a safe environment.
  • Successfully transitioned our real-time voice infrastructure from WebSockets to WebRTC, enabling natural conversation flow with low latency and interruption support.
  • Designed the platform to be scalable from day one using AWS Databases and Vercel, allowing the same infrastructure to support both prototypes and production workloads.
  • Developed an AI evaluation system that provides structured, actionable feedback after every simulation, helping users continuously improve their communication skills.

What we learned

We learned that shipping production-ready AI products isn't just about choosing the best model.

It's about building reliable infrastructure that supports authentication, conversation history, analytics, evaluations, and future growth from day one.

Modern developer tools like v0 and AWS databases dramatically reduce the time required to move from an idea to a production-ready application.

What's next for Squinia

This hackathon version is only the beginning.

We're expanding Squinia into a complete AI communication platform for:

  • Technical interview preparation
  • Corporate communication training
  • Customer support simulations
  • Leadership coaching
  • Sales enablement
  • Workplace learning

Our long-term vision is to become the infrastructure powering communication training for organizations around the world.

Built With

Share this project:

Updates