You will get Citation based RAG system for your internal knowledge
Project details
I build grounded AI systems that answer from your documents instead of making generic chatbot guesses.
This service is ideal for companies that want a private knowledge assistant for PDFs, SOPs, manuals, policies, technical documentation, research files, or internal knowledge bases.
Your system can include document ingestion, semantic retrieval, vector database setup, citation-based answers, prompt guardrails, and a simple API or chat interface.
The goal is simple: meaningful answers, traceable sources, and a system that stays within the boundaries of your documentation.
This is not a generic chatbot build. I create grounded RAG systems that understand the question, retrieve the right context, and stay within your documents.
If needed, I can also connect additional sources such as websites, databases, or structured internal content and shape the solution around your workflow.
This service is ideal for companies that want a private knowledge assistant for PDFs, SOPs, manuals, policies, technical documentation, research files, or internal knowledge bases.
Your system can include document ingestion, semantic retrieval, vector database setup, citation-based answers, prompt guardrails, and a simple API or chat interface.
The goal is simple: meaningful answers, traceable sources, and a system that stays within the boundaries of your documentation.
This is not a generic chatbot build. I create grounded RAG systems that understand the question, retrieve the right context, and stay within your documents.
If needed, I can also connect additional sources such as websites, databases, or structured internal content and shape the solution around your workflow.
AI Algorithms
Large Language ModelAI Applications
AI Chatbot, AI Content Creation, Conversational AI, Natural Language UnderstandingAI Models
ChatGPT, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$299
|
Standard
$1,200
|
Advanced
$3,500
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 21 days |
Number of Revisions | 1 | ||
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 Data Source
(+ 1 Day)
+$150
Evaluation / answer quality review
(+ 2 Days)
+$250
Cloud deployment
(+ 2 Days)
+$500Frequently asked questions
About Kadir
Software Eng. Lead | .NET + Python | RAG & Semantic Search
Denizli, Turkey - 2:47 am local time
I help teams ship reliable, scalable products—from LLM/RAG-powered semantic search to enterprise-grade .NET platforms—with strong CI/CD and QA maturity.
What I can do for you:
Build/optimize RAG pipelines (vector DB, hybrid search, reranking, evals)
Architect microservices and cloud-native backends (Docker/K8s)
Set up or harden CI/CD + QA (Azure DevOps/Jenkins, automated testing, release hygiene)
Moodle: plugin development, SSO (Keycloak/OAuth/SAML), integrations, performance & ops
WordPress: custom plugins, REST integrations, performance/security hardening
Tech: .NET / ASP.NET Core, Python (FastAPI), PostgreSQL, Redis, Docker, Kubernetes, AWS/Azure, observability, clean architecture.
If you need someone who can own the technical direction and also deliver hands-on, let’s talk.
Steps for completing your project
After purchasing the project, send requirements so Kadir can start the project.
Delivery time starts when Kadir receives requirements from you.
Kadir works on your project following the steps below.
Revisions may occur after the delivery date.
Review goals and data sources
I review your use case, documents, and data sources, then define the best RAG approach for accurate, grounded answers.
Design retrieval and document flow
I structure the document pipeline, retrieval logic, and answer boundaries so the system stays focused on your data and use case.