Hybrid AI-Powered Call Center Solution Development
Budget: ₹750 – ₹1,250 INR
We are seeking an experienced AI/ML Developer or VoIP Engineer to build a hybrid AI-powered call center solution (on-prem + cloud) that can handle LLM-powered voice interactions.
Project Scope:
• Call Flow Architecture:
1. Customer speaks (Incoming call through SIP/VoIP).
2. Speech-to-Text (STT) with Whisper – Convert speech to text.
3. LLM Processing with Mixtral – Generate AI-based responses, with GPT-4 API fallback for complex queries.
4. Text-to-Speech (TTS) with Coqui – Convert text back to speech.
5. Bot replies to the customer.
• Fallback to human agents if AI cannot resolve, with full call history & transcription.
Key Features:
• Hybrid Implementation (On-Prem + Cloud): Balancing cost, latency & scalability.
• Omnichannel Support: Handling calls via SIP, WebRTC, and cloud platforms.
• Scalable Architecture: Initial capacity of 50 calls per minute, scaling up to 10,000 per minute.
• Live Transcription & Call Logging: For analytics & future reference.
• Fallback Handling: Smooth call transfers to human agents when necessary.
Tech Stack Preferences:
• Speech-to-Text: OpenAI Whisper / NVIDIA Riva
• LLM Processing: Mixtral (Mistral AI) with GPT-4 fallback
• Text-to-Speech: Coqui AI / OpenAI / Google TTS
• VoIP/SIP: Asterisk / FreeSWITCH / Twilio / Exotel
• Backend: Python (FastAPI/Django/Flask) or Node.js
• Database: PostgreSQL / MongoDB / Redis
• Deployment: Hybrid – On-Prem GPU Servers + Cloud (AWS/GCP/Azure)
Ideal Developer Profile:
• Proven experience in AI-powered voice applications.
• Strong knowledge of VoIP, SIP, Asterisk, and FreeSWITCH.
• Experience working with STT, TTS, and LLMs (Mistral, GPT-4, etc.).
• Ability to build scalable, real-time systems.
• Familiarity with both cloud and on-prem deployment.
How to Apply:
✅ Share relevant past projects (especially voice bot/AI call center).
✅ Briefly explain how you’d implement this hybrid model.
✅ Provide a rough cost estimate.
Looking forward to building this AI-powered call center with you! ?
Project Scope:
• Call Flow Architecture:
1. Customer speaks (Incoming call through SIP/VoIP).
2. Speech-to-Text (STT) with Whisper – Convert speech to text.
3. LLM Processing with Mixtral – Generate AI-based responses, with GPT-4 API fallback for complex queries.
4. Text-to-Speech (TTS) with Coqui – Convert text back to speech.
5. Bot replies to the customer.
• Fallback to human agents if AI cannot resolve, with full call history & transcription.
Key Features:
• Hybrid Implementation (On-Prem + Cloud): Balancing cost, latency & scalability.
• Omnichannel Support: Handling calls via SIP, WebRTC, and cloud platforms.
• Scalable Architecture: Initial capacity of 50 calls per minute, scaling up to 10,000 per minute.
• Live Transcription & Call Logging: For analytics & future reference.
• Fallback Handling: Smooth call transfers to human agents when necessary.
Tech Stack Preferences:
• Speech-to-Text: OpenAI Whisper / NVIDIA Riva
• LLM Processing: Mixtral (Mistral AI) with GPT-4 fallback
• Text-to-Speech: Coqui AI / OpenAI / Google TTS
• VoIP/SIP: Asterisk / FreeSWITCH / Twilio / Exotel
• Backend: Python (FastAPI/Django/Flask) or Node.js
• Database: PostgreSQL / MongoDB / Redis
• Deployment: Hybrid – On-Prem GPU Servers + Cloud (AWS/GCP/Azure)
Ideal Developer Profile:
• Proven experience in AI-powered voice applications.
• Strong knowledge of VoIP, SIP, Asterisk, and FreeSWITCH.
• Experience working with STT, TTS, and LLMs (Mistral, GPT-4, etc.).
• Ability to build scalable, real-time systems.
• Familiarity with both cloud and on-prem deployment.
How to Apply:
✅ Share relevant past projects (especially voice bot/AI call center).
✅ Briefly explain how you’d implement this hybrid model.
✅ Provide a rough cost estimate.
Looking forward to building this AI-powered call center with you! ?