You will get a real-time voice AI assistant that talks with your users
Project details
A voice assistant lives or dies on latency. If it pauses two seconds before answering, people talk over it and hang up. I build streaming pipelines — audio in and audio out over a live socket — so the reply starts while the sentence is still being processed.
I've built real-time AI Q&A systems with sub-second response over WebSocket streaming, and I'll build yours the same way: speech recognition, an LLM that can look things up mid-conversation, and low-latency speech coming back.
You get an assistant you can actually talk to, the source code in your own repository, and a written handover. Start with a browser demo on a single topic, or go straight to a production agent on your phone line or inside your app.
I've built real-time AI Q&A systems with sub-second response over WebSocket streaming, and I'll build yours the same way: speech recognition, an LLM that can look things up mid-conversation, and low-latency speech coming back.
You get an assistant you can actually talk to, the source code in your own repository, and a written handover. Start with a browser demo on a single topic, or go straight to a production agent on your phone line or inside your app.
AI Algorithms
Transformer ModelAI Applications
AI Chatbot, AI Text-to-Speech, Automatic Speech Recognition, Conversational AI, Natural Language Understanding, Speech SynthesisAI Development Language
PythonAI Models
GPT-4, WhisperWhat's included
| Service Tiers |
Starter
$75
|
Standard
$250
|
Advanced
$600
|
|---|---|---|---|
| Delivery Time | 5 days | 12 days | 25 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | ||
Detailed Code Comments | - | ||
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | ||
Model Documentation | - | ||
Model Monitoring | - | ||
Model Testing & Optimization | - | - | - |
Model Tuning | - | - | - |
Natural Language Processing | |||
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | ||
Source Code |
Frequently asked questions
About Prerit
AI Application Engineer | RAG Systems | LangChain | Full Stack
Noida, India - 4:42 pm local time
In my current engineering role I've built:
→ RAG pipelines using Azure OpenAI GPT-4 that improved data retrieval by 35%
→ GenAI APIs that reduced manual data extraction errors by 90%
→ AI data systems processing 500GB+ monthly with 40% faster query response
→ Real-time AI Q&A systems with sub-second response via WebSocket streaming
What I can build for YOU:
✅ AI Chatbot for your website or app
✅ RAG system — upload docs, ask questions, get AI answers
✅ OpenAI / Claude / Gemini integration into your existing app
✅ Voice AI assistant
✅ AI automation workflows using LangChain/LangGraph
My stack:
LangChain · LangGraph · FastAPI · OpenAI · React · Node.js ·
Python · Vector DBs · AWS · Azure · PostgreSQL · Docker
I've delivered enterprise-level AI systems. Now I bring
the same quality to businesses of all sizes.
Let's talk about what you need to build. 🚀
Steps for completing your project
After purchasing the project, send requirements so Prerit can start the project.
Delivery time starts when Prerit receives requirements from you.
Prerit works on your project following the steps below.
Revisions may occur after the delivery date.
Conversation design
We agree what the assistant handles, what it hands to a human, and how it should sound to your callers.
Streaming pipeline
Speech recognition, the LLM layer and speech synthesis wired together over a live socket, tuned for low latency.