You will get Production-Ready AI Chatbot with RAG (Custom LLM App)
Project details
Production-ready Retrieval-Augmented Generation (RAG) system built around your documents or internal knowledge base.
You will receive a working, deployable AI chatbot or API-based system capable of retrieving and generating accurate responses from your data.
Implementation includes:
• Document ingestion (PDF, DOCX, database, API sources)
• Embedding pipeline and vector database configuration
• Optimized Top-K retrieval strategy
• LLM integration (OpenAI or open-source models)
• Basic evaluation and response quality tuning
• Deployment-ready backend configuration
The focus is not on experimentation, but on delivering a reliable system that can operate in real workflows and scale as your needs grow.
Suitable for:
• Internal knowledge assistants
• Customer support automation
• Document Q&A systems
• AI-powered enterprise search
Optional extensions (higher tiers) may include monitoring strategy, performance optimization, and scalability planning.
You will receive a working, deployable AI chatbot or API-based system capable of retrieving and generating accurate responses from your data.
Implementation includes:
• Document ingestion (PDF, DOCX, database, API sources)
• Embedding pipeline and vector database configuration
• Optimized Top-K retrieval strategy
• LLM integration (OpenAI or open-source models)
• Basic evaluation and response quality tuning
• Deployment-ready backend configuration
The focus is not on experimentation, but on delivering a reliable system that can operate in real workflows and scale as your needs grow.
Suitable for:
• Internal knowledge assistants
• Customer support automation
• Document Q&A systems
• AI-powered enterprise search
Optional extensions (higher tiers) may include monitoring strategy, performance optimization, and scalability planning.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI-Enhanced Classification, Conversational AI, Natural Language Generation, Natural Language Understanding, Sentiment Analysis, Sequence Modeling, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging Face, PyTorch, StreamlitAI Models
BERT, ChatGPT, GPT-4, LLaMA, WhisperWhat's included
| Service Tiers |
Starter
$390
|
Standard
$790
|
Advanced
$1,490
|
|---|---|---|---|
| Delivery Time | 7 days | 10 days | 14 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 |
Optional add-ons
You can add these on the next page.
Deployment on client cloud
(+ 3 Days)
+$300
Cost optimization audit
(+ 2 Days)
+$250Frequently asked questions
About Ling
Production AI Systems | LLM & RAG Architecture | PhD
Singapore, Singapore - 10:17 pm local time
I design and implement production-ready machine learning and LLM systems that move beyond experimentation into stable, scalable infrastructure. My work focuses on delivering systems that operate reliably in real workflows — not demos or prototypes.
With 20+ years across academia and industry, including leadership roles in large-scale e-commerce platforms, I have built predictive intelligence and AI infrastructure serving multi-million user environments. My experience spans architecture design, system optimization, and production deployment.
Selected impact:
• Led ML initiatives driving 8–16% revenue uplift in large-scale commerce systems
• Built predictive intelligence pipelines operating at multi-million user scale
• Winner of 10+ international ML competitions
• 80+ peer-reviewed publications in top-tier venues
What I help clients build:
• Production-ready LLM and RAG systems
• AI knowledge assistants and internal document Q&A platforms
• Retrieval pipelines with vector databases and optimized search
• Scalable ML infrastructure aligned with business objectives
• Technical architecture review and ML due diligence
I do not sell isolated models or generic AI prototypes.
I design structured AI systems that are deployable, maintainable, and aligned with measurable outcomes.
If you are building an AI-driven product, integrating LLM into workflows, or transitioning from prototype to production — I can help structure the architecture and execution roadmap.
Steps for completing your project
After purchasing the project, send requirements so Ling can start the project.
Delivery time starts when Ling receives requirements from you.
Ling works on your project following the steps below.
Revisions may occur after the delivery date.
Clarify Use Case & Data
Review your data sources and define system objective.
Build Retrieval Pipeline
Set up document ingestion, embeddings, and vector search.