Saral AI

The multilingual AI receptionist for every MSME

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Inspiration

Every missed phone call can mean a lost customer for a small business.

Across India, millions of MSMEs can't afford dedicated receptionists or customer support staff. Many business owners are busy serving existing customers, leaving incoming calls unanswered. Language barriers further make it difficult to provide quality customer service to people who prefer speaking in their native language.

We built Saral AI to solve this problem by giving every small business an affordable, multilingual AI receptionist that answers calls 24/7, qualifies leads, and ensures no customer is left unheard.

What it does

Saral AI is a multilingual AI voice receptionist built for small and medium businesses.

It can:

  • Answer customer calls instantly
  • Converse naturally in multiple Indian languages
  • Understand customer queries using AI
  • Qualify potential leads
  • Collect customer information
  • Answer frequently asked questions
  • Operate 24/7 without human intervention
  • Help businesses never miss an opportunity because of an unanswered phone call

Saral AI is designed for clinics, salons, restaurants, coaching institutes, retail stores, and other service businesses that rely on phone inquiries.

How we built it

Saral AI uses a real-time AI voice pipeline built with modern AI technologies.

Tech Stack

  • FastAPI
  • Python
  • Railway
  • WebSockets
  • Groq LLM
  • Sarvam AI (Speech-to-Text & Text-to-Speech)

Workflow:

  1. Customer calls the AI receptionist.
  2. Speech is converted into text using Sarvam AI.
  3. The conversation is processed by Groq's LLM.
  4. The AI generates a contextual response.
  5. Sarvam AI converts the response back into natural speech.
  6. The customer experiences a seamless voice conversation in their preferred language.

Challenges we ran into

Building a real-time voice AI system came with several challenges.

One of the biggest challenges was reducing conversation latency. Initially, response times were much higher than expected, making conversations feel unnatural.

We also faced deployment issues while hosting the application. Due to platform limitations and payment verification issues, we migrated our backend from Render to Railway to achieve a more reliable deployment.

Synchronizing real-time audio streaming, speech recognition, LLM responses, and speech synthesis while maintaining a smooth conversation was another major engineering challenge.

Accomplishments that we're proud of

  • Built a working end-to-end multilingual AI receptionist.
  • Successfully integrated speech recognition, LLM reasoning, and voice synthesis into one seamless experience.
  • Deployed the application successfully.
  • Reduced latency significantly compared to our initial implementation.
  • Created a solution capable of helping millions of small businesses improve customer communication.

What we learned

This project taught us that building conversational AI isn't just about connecting APIs.

Creating natural voice interactions requires careful optimization of latency, conversation flow, deployment reliability, and user experience.

We also learned that impactful AI products begin with understanding real human problems rather than simply showcasing the latest technology.

What's next for Saral AI

We're planning to expand Saral AI with several new capabilities:

  • CRM integrations
  • Appointment booking
  • WhatsApp follow-ups
  • Lead analytics dashboard
  • Industry-specific AI agents
  • More Indian language support
  • Business knowledge base integration
  • Call summaries and customer insights

Our long-term vision is to make Saral AI the voice operating system for millions of small businesses, enabling them to deliver professional customer service regardless of their size or budget.

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