You will get an AI RAG Assistant answers from your documents
Top Rated
Project details
You’ll get a fully functional RAG (Retrieval-Augmented Generation) chatbot that answers questions directly from your documents with high accuracy, zero hallucinations, and a clean user experience. This system transforms static PDFs, manuals, textbooks, medical notes, or internal knowledge bases into an intelligent assistant that works 24/7.
What sets this project apart is the architecture behind it: a carefully engineered pipeline using modern vector databases, advanced chunking strategies, and optimized prompt flows. You’re not getting a template—you’re getting a purpose-built, production-grade AI system structured exactly around your data and your workflow.
What makes this service different:
Tailored retrieval pipeline for your exact documents
Accurate, citation-based answers powered by LLMs
Multi-file ingestion with clean data normalization
A lightweight UI or API endpoint depending on your needs
Elastic, Chroma, or Redis-backed vector search for speed and relevance
Tech that drives the impact:
FastAPI, LangChain/LangGraph, OpenAI models, vector databases, Docker deployment, and optional RAG optimizer modules for boosting precision.
What sets this project apart is the architecture behind it: a carefully engineered pipeline using modern vector databases, advanced chunking strategies, and optimized prompt flows. You’re not getting a template—you’re getting a purpose-built, production-grade AI system structured exactly around your data and your workflow.
What makes this service different:
Tailored retrieval pipeline for your exact documents
Accurate, citation-based answers powered by LLMs
Multi-file ingestion with clean data normalization
A lightweight UI or API endpoint depending on your needs
Elastic, Chroma, or Redis-backed vector search for speed and relevance
Tech that drives the impact:
FastAPI, LangChain/LangGraph, OpenAI models, vector databases, Docker deployment, and optional RAG optimizer modules for boosting precision.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Mobile App Development, Conversational AIAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, Word2vecAI Models
ChatGPT, GPT-4, LLaMA, OpenAI Codex, WhisperWhat's included
| Service Tiers |
Starter
$99
|
Standard
$175
|
Advanced
$300
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 7 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.
Additional Revision
+$100
Deploy to your server (AWS/Azure/GCP))
(+ 2 Days)
+$100
Agentic tool
(+ 2 Days)
+$100Frequently asked questions
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FA
Fahad A.
Jun 15, 2026
Local LLM Consultation for 25-30 users
YC
Yasemin C.
Dec 6, 2025
We are looking for AI professionals for a usability test!
JS
James S.
Oct 12, 2025
Senior AI Architect: Constitutional Cost Control & Multi-Agent Orchestration
He seems very knowledgeable, competent and honest. A rare find.
About Vahit
AI Architect | AI Engineer | Consultant | Product Owner
100%
Job Success
Rotterdam, Netherlands - 9:46 pm local time
💡 Notable Projects
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Built a two-sided clinical AI platform from scratch — a patient nutrition coaching app and a clinic assistant automating 100% of nutritionist workflows. LangGraph-orchestrated multi-agent architecture, RAG pipelines for clinical knowledge retrieval, MCP tool integration, Django + MongoDB + Redis.
LMXAI Benchmark (Context Engineering)
Achieved top-5 global ranking in LLM education benchmarks, raising accuracy from 68% → 85% through advanced context engineering, prompt optimization, and agentic reasoning within a custom evaluation framework.
🎯 How I Work
I build systems that are modular, observable, and production-ready — not prototypes. Focus is always on reliability, latency, and measurable business impact.
🧰 Core Stack
LangGraph · LangChain · FastAPI · Python (async) · RAG · vLLM · SGLang · LoRA/QLoRA · Redis · Elasticsearch · PostgreSQL · MongoDB · Docker · Kubernetes · OpenAI / Claude / Open-Source Models · MCP · Context Engineering · GPU Inference · AWQ/FP8 Quantization
Steps for completing your project
After purchasing the project, send requirements so Vahit can start the project.
Delivery time starts when Vahit receives requirements from you.
Vahit works on your project following the steps below.
Revisions may occur after the delivery date.
Understand your use case & data
I review your goals, target users, and documents to clearly define what the RAG chatbot should do and which data sources it will rely on.
Design the RAG architecture
I design the retrieval pipeline (chunking, embeddings, vector DB) and how the chatbot will combine user questions with your data to give grounded answers.