You will get Production AI features with LLM integration and infrastructure included
Project details
You get production AI features (summarization, classification, extraction, etc.) built and integrated into your app with proper error handling and monitoring.
So you can automate manual work and ship AI capabilities your competitors don't have.
Instead of building from scratch, debugging prompt issues, or shipping unreliable AI that breaks in production.
Not sure which tier? Starter for 1 feature MVP, Standard for 3 features with monitoring, Advanced to showcase enterprise-grade AI architecture.
Need something custom? Message me - I built 5 AI features for a Dubai-based startup (NDA) in production at $400 per feature.
So you can automate manual work and ship AI capabilities your competitors don't have.
Instead of building from scratch, debugging prompt issues, or shipping unreliable AI that breaks in production.
Not sure which tier? Starter for 1 feature MVP, Standard for 3 features with monitoring, Advanced to showcase enterprise-grade AI architecture.
Need something custom? Message me - I built 5 AI features for a Dubai-based startup (NDA) in production at $400 per feature.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI Text-to-Speech, AI-Enhanced Classification, Conversational AI, Sentiment Analysis, Synthetic Data GenerationAI Development Language
PythonAI Models
BERT, ChatGPT, GPT-3, GPT-4, LLaMA, WhisperWhat's included
| Service Tiers |
Starter
$600
|
Standard
$1,800
|
Advanced
$6,500
|
|---|---|---|---|
| Delivery Time | 5 days | 14 days | 21 days |
Number of Revisions | 1 | 3 | 5 |
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.
Additional Revision
+$200
Additional Feature
(+ 3 Days)
+$400
RAG Integration
(+ 7 Days)
+$1,200
Fine-Tuning
(+ 10 Days)
+$2,500Frequently asked questions
About Mustafa
Software Engineer | AI Voice Agents & Automation | LangChain, Python
Kocaeli, Turkey - 5:03 pm local time
What I've shipped recently:
Transgate.ai (My SaaS)
Multi-provider speech platform: Deepgram, Soniox, RunPod integration. Async webhooks, provider failover, voice pipelines. 63+ production features shipped. This is why voice AI and telephony integrations are second nature.
Enterprise Customer Chat (NDA - Major European Corporation)
Built production customer chat service with LangChain, LangGraph, and Langfuse. SLA monitoring, robust error handling. Also designed multi-tenant MLOps infrastructure: Azure, Kubernetes, ArgoCD, Terraform.
Corporate Training AI (NDA - Dubai Startup)
AI-powered course recommendation engine. Users describe training needs, LLM suggests relevant courses with relevance scores. FastAPI backend, DeepSeek integration, 77% test coverage.
KenzNote.ai (AI Meeting Notes)
Chat with AI about your meetings. Ask questions, get answers with citations from your transcript. Trust-aware citation system.
I handle the parts that break most projects: multi-provider integration, webhook architecture, error recovery, production monitoring.
Stack: Python, FastAPI, LangChain/LangGraph, VAPI, Retell, Twilio, n8n, React, PostgreSQL, Docker, Supabase
Steps for completing your project
After purchasing the project, send requirements so Mustafa can start the project.
Delivery time starts when Mustafa receives requirements from you.
Mustafa works on your project following the steps below.
Revisions may occur after the delivery date.
Define & Design
Review your examples, define input/output formats, plan feature architecture and LLM integration strategy.
Build Features + Integration
Implement AI features with proper prompts, LLM connection, retry logic, error handling, and testing.