You will get full stack voice agent with proper tool calling with best latency.
Project details
I build AI voice agents that answer your calls 24/7, screen out spam, and turn real callers into qualified leads—automatically. While most "voice bots" sound robotic and miss the point, mine run on a production-grade backend with real-time speech, intelligent reasoning, and tool calling that actually books jobs, captures details, and follows up.
With 4 years building production backends—106+ REST APIs, multi-tenant systems, and live voice pipelines (Twilio, Deepgram, Cartesia)—I deliver systems that don't break under real call volume. Every missed call is a lost job; this system makes sure that never happens.
With 4 years building production backends—106+ REST APIs, multi-tenant systems, and live voice pipelines (Twilio, Deepgram, Cartesia)—I deliver systems that don't break under real call volume. Every missed call is a lost job; this system makes sure that never happens.
Programming Languages
PythonCoding Expertise
Cross Browser & Device Compatibility, Performance Optimization, DesignWhat's included
| Service Tiers |
Starter
$3,000
|
Standard
$4,500
|
Advanced
$10,000
|
|---|---|---|---|
| Delivery Time | 40 days | 60 days | 70 days |
Number of Revisions | 1 | 2 | 5 |
Design Customization | |||
Content Upload | |||
Responsive Design | - | ||
Source Code |
Frequently asked questions
About Arbaz
Ai Engineer
Lahore, Pakistan - 1:05 am local time
Real talk, when your AI is wrong 3 times out of 10, it's not the prompt. It's the data underneath it. Nobody wants to say that out loud because fixing data is boring and prompts are easy.
So I opened their dataset first. Dates saved as text. Same customer sitting in there four times, spelled four different ways. One column 40% empty that everyone had been quietly averaging around for months. Cleaned the whole thing in pandas. Then fine-tuned the model in PyTorch on their real data.
71 to 94. Same model. Better foundation. That's it.
Most people can't do this because they can't get under the hood. They call an API, wrap it in a Django view, and when accuracy tanks their only move is "let me try a bigger model." Six weeks later you're at 73% and broke.
WHAT I DO
• Fix your data in pandas, the boring part everyone skips and then blames the model for
• Train real prediction models in PyTorch when off-the-shelf isn't cutting it
• Fine-tune LLMs with LoRA and QLoRA on your data, not the internet's
• Build LangGraph agents that recover from weird inputs instead of dying on them
• Build MCP servers with custom skills, the protocol Anthropic's own tools run on. Go search this platform for someone else who's shipped one. I'll wait.
RECEIPTS
• Trained the Cursor code-generation model. Prompt engineering, dataset validation, evaluation workflows at volume
• Built a real estate SaaS platform end to end. Multi-tenant, ETL pipelines pulling from 30+ data sources
• Built a UK lettings backend. 106 production APIs, PostgreSQL sharding, 6 CRM integrations, SSO, Stripe billing
• Voice agents answering live calls under 200ms, running right now for customers paying every month
• 4 years shipping production systems
STACK
Python, PyTorch, pandas, scikit-learn, LangGraph, MCP, Django/DRF, PostgreSQL, Redis, Celery, Docker, Railway, AWS/GCP
Send me what you're stuck on. I'll tell you in one message whether it's your data or your model. Free, before you hire anybody, me included.
Steps for completing your project
After purchasing the project, send requirements so Arbaz can start the project.
Delivery time starts when Arbaz receives requirements from you.
Arbaz works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & requirements
You share your call-handling needs, business details, and the outcomes you want. I map the workflow and confirm scope.
Build & integrate
I set up the backend, voice pipeline, and AI call screening, then connect your dashboard, SMS/email/WhatsApp, and scheduling.