You will get AI Voice Agent That Cut Manual Call Volume by 70%
Rising Talent
Rising Talent
Project details
š¬š¼ššæ š°š®š¹š¹š²šæš š“š²š š¶š»ššš®š»š š®š»ššš²šæš. š¬š¼ššæ šš²š®šŗ šµš®š»š±š¹š²š š¼š»š¹š ššµš² š°š¼š»šš²šæšš®šš¶š¼š»š ššµš®š š®š°ššš®š¹š¹š š»š²š²š± š® šµššŗš®š».
šš©šŖš“ š·š°šŖš¤š¦ š¢šØš¦šÆšµ š¢šÆš“šøš¦š³š“ š¦š·š¦š³šŗ š¤š¢šš šŖšÆ š¶šÆš„š¦š³ 1 š“š¦š¤š°šÆš„, š³š¦š“š°šš·š¦š“ š¤š°š®š®š°šÆ š³š¦š²š¶š¦š“šµš“ (š“š¤š©š¦š„š¶ššŖšÆšØ, š“šµš¢šµš¶š“ š¤š©š¦š¤š¬š“, šššš“), š¢šÆš„ š³š°š¶šµš¦š“ š¤š°š®š±šš¦š¹ š¤š¢š“š¦š“ šµš° šµš©š¦ š³šŖšØš©šµ š±š¦š³š“š°šÆ šøšŖšµš© š§š¶šš š¤š°šÆšµš¦š¹šµ.
I built a production voice agent for a healthcare client that handles š®,š¬š¬š¬+ daily calls at š°š¬š¬šŗš response time. Their manual call volume dropped š³š¬% in 30 days and they saved $šš®š/šŗš¼š»ššµ in staffing costs.
š¬š¼š š“š²š š® š³šš¹š¹š š±š²š½š¹š¼šš²š± ššššš²šŗ - not a demo. Trained on šŗš°š¶š³ scenarios, integrated with šŗš°š¶š³ tools, tested with real calls before go-live.
šš©šŖš“ š·š°šŖš¤š¦ š¢šØš¦šÆšµ š¢šÆš“šøš¦š³š“ š¦š·š¦š³šŗ š¤š¢šš šŖšÆ š¶šÆš„š¦š³ 1 š“š¦š¤š°šÆš„, š³š¦š“š°šš·š¦š“ š¤š°š®š®š°šÆ š³š¦š²š¶š¦š“šµš“ (š“š¤š©š¦š„š¶ššŖšÆšØ, š“šµš¢šµš¶š“ š¤š©š¦š¤š¬š“, šššš“), š¢šÆš„ š³š°š¶šµš¦š“ š¤š°š®š±šš¦š¹ š¤š¢š“š¦š“ šµš° šµš©š¦ š³šŖšØš©šµ š±š¦š³š“š°šÆ šøšŖšµš© š§š¶šš š¤š°šÆšµš¦š¹šµ.
I built a production voice agent for a healthcare client that handles š®,š¬š¬š¬+ daily calls at š°š¬š¬šŗš response time. Their manual call volume dropped š³š¬% in 30 days and they saved $šš®š/šŗš¼š»ššµ in staffing costs.
š¬š¼š š“š²š š® š³šš¹š¹š š±š²š½š¹š¼šš²š± ššššš²šŗ - not a demo. Trained on šŗš°š¶š³ scenarios, integrated with šŗš°š¶š³ tools, tested with real calls before go-live.
AI Algorithms
Large Language Model, Multilayer Perceptron, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Text-to-Speech, Automatic Speech Recognition, Conversational AI, Natural Language Generation, Natural Language Understanding, Sentiment Analysis, Speech SynthesisAI Development Language
PythonAI Tools
Hugging Face, NVIDIA AI Platform, PyTorch, ReplitAI Models
ChatGPT, GPT-4, LLaMA, OpenAI Codex, WhisperWhat's included
| Service Tiers |
Starter
$1,500
|
Standard
$3,500
|
Advanced
$7,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 28 days |
Number of Revisions | 1 | 3 | 7 |
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 |
Optional add-ons
You can add these on the next page.
Extra Language Support
(+ 3 Days)
+$500
Outbound Calling
(+ 5 Days)
+$750
Analytics Dashboard
(+ 4 Days)
+$600Frequently asked questions
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SC
Savior C.
Jan 28, 2026
Full-Stack Developer (Vimeo Custom + LiveKit + WebSocket) ā Fix & Stabilize
About Muhammad
Senior AI/ML Engineer | AI Agents & Voice AI | RAG & LLM Pipelines
Lahore, PakistanĀ - 10:16 am local time
Your support team stops drowning in repetitive calls. Your team finds answers in šš²š°š¼š»š±š instead of digging through documents for hours. Your manual workflows run on autopilot while your people focus on work that actually needs a human brain.
š„š²ššš¹šš š šµš®šš² š±š²š¹š¶šš²šæš²š±:
ā¢ š³š¬% drop in manual call volume for a healthcare client (AI voice agent, LiveKit)
ā¢ Sub-š®-šš²š°š¼š»š± document retrieval across šš¬š+ files (RAG system, NHS England)
ā¢ šµš³% extraction accuracy on structured document processing (OpenAI consultation)
ā¢ šØš¦š š£š®šš²š»š šš¼š¹š±š²šæ in applied AI systems
I am a ššš¹š¹-š¦šš®š°šø + šš/š š šš»š“š¶š»š²š²šæ. You get š¼š»š² š½š²šæšš¼š» who designs the architecture, builds the product, and deploys it. No handoffs between "model people" and "app people."
šš³ šš¼š š®šæš² šµš²šæš² š³š¼šæ š©š¼š¶š°š² šš / šŖš²šÆš„š§š
Your callers get instant responses. Your team handles only the conversations that need a human.
ā¢ šš¶šš²šš¶š, š©š®š½š¶, š§šš¶š¹š¶š¼, šš“š¼šæš®, šš®š»šš
ā¢ NAT traversal, SFU/MCU, jitter, echo, low-latency tuning, ASR/TTS pipelines
šš³ šš¼š š®šæš² šµš²šæš² š³š¼šæ šš / š š (ššš š, š„šš, šš“š²š»šš)
Your docs become searchable in seconds. Your workflows run themselves. Your AI gives grounded answers with sources, not hallucinations.
ā¢ š„šš with š£š¶š»š²š°š¼š»š² / šššš¦š¦ / šŖš²š®šš¶š®šš² / š½š“šš²š°šš¼šæ - hybrid search, re-ranking, citations
ā¢ šš šš“š²š»šš with šš®š»š“ššµš®š¶š», šš®š»š“ššæš®š½šµ, ššæš²ššš, šš¹š®šŗš®šš»š±š²š
ā¢ šš¹š®šš±š² šš£š, š šš£ šš²šæšš²šæš, šš¼š¼š¹ ššš², šš¹š®šš±š² šš¼š±š² integrations
ā¢ Models: šš£š§-š°š¼, šš¹š®šš±š², šš²šŗš¶š»š¶, š š¶šššæš®š¹, šš¹š®šŗš®, ššš“š“š¶š»š“šš®š°š²
šš³ šš¼š š®šæš² šµš²šæš² š³š¼šæ š„š¼šÆš¼šš¶š°š šš / š¢š½š²š»šš¹š®š
Your robot learns in simulation and works on real hardware.
ā¢ š¦š¶šŗ-šš¼-šæš²š®š¹ transfer, reinforcement learning, imitation learning
ā¢ ššš®š®š° š¦š¶šŗ, š ššš¼šš¼, š£šššš¹š¹š²š, š„š¢š¦/š„š¢š¦š®, vision-language models for manipulation
šš³ šš¼š š»š²š²š± ššµš² ššµš¼š¹š² š½šæš¼š±šš°š (ššš¹š¹-š¦šš®š°šø)
You get production code, not demo glue:
ā¢ Frontend: š„š²š®š°š / š”š²š š.š·š (dashboards, admin panels, real-time UIs)
ā¢ Backend: šš®šššš£š / š”š¼š±š².š·š (REST, WebSockets, auth, payments, integrations)
ā¢ Infra: šš¼š°šøš²šæ, ššŖš¦/ššš£/ššššæš², CI-friendly deploy
š š "š»š¼-šššæš½šæš¶šš²š" š±š²š¹š¶šš²šæš šššš¹š²
ā¢ Clear milestones (what ships in week 1 vs week 3)
ā¢ A testable slice early so you see progress fast
ā¢ Clean handoff: documented setup + deploy notes
Tell me what "š±š¼š»š²" looks like for your project and I will respond with an execution plan.
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery and call flow mapping
I analyze your call types, map decision trees for each scenario, define escalation rules, and confirm integration points with your CRM/tools.
Voice agent build and intent training
Build the ASR/TTS pipeline, train intents for your scenarios, connect to your phone system and CRM, implement fallback and escalation logic.