You will get a RAG pipeline on your private documents using LangChain or LlamaIndex
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Project details
Your business has valuable knowledge locked inside PDFs, Word docs, internal wikis, or databases. I build RAG (Retrieval-Augmented Generation) pipelines that let you or your customers ask questions and get accurate, sourced answers from that private data.
No hallucinations. No generic AI answers. Only answers grounded in your actual documents.
What I build:
• Document ingestion pipeline (PDF, DOCX, TXT, HTML, Notion, Confluence)
• Intelligent chunking and embedding (OpenAI, Cohere, or local models)
• Vector database setup (Pinecone, ChromaDB, Weaviate, FAISS)
• LangChain or LlamaIndex orchestration layer
• Conversational memory with source citation
• REST API backend (FastAPI or Node.js) for your frontend to connect to
• Clean chat UI or integration into your existing app
• Evaluation and accuracy testing before delivery
Use cases I've delivered: legal document Q&A, internal company knowledge base, product manual assistant, contract review tool, customer support bot trained on your docs.
Tell me your document type and use case I'll tell you the right stack and realistic accuracy expectations before you order.
No hallucinations. No generic AI answers. Only answers grounded in your actual documents.
What I build:
• Document ingestion pipeline (PDF, DOCX, TXT, HTML, Notion, Confluence)
• Intelligent chunking and embedding (OpenAI, Cohere, or local models)
• Vector database setup (Pinecone, ChromaDB, Weaviate, FAISS)
• LangChain or LlamaIndex orchestration layer
• Conversational memory with source citation
• REST API backend (FastAPI or Node.js) for your frontend to connect to
• Clean chat UI or integration into your existing app
• Evaluation and accuracy testing before delivery
Use cases I've delivered: legal document Q&A, internal company knowledge base, product manual assistant, contract review tool, customer support bot trained on your docs.
Tell me your document type and use case I'll tell you the right stack and realistic accuracy expectations before you order.
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation System, Software MaintenanceAI Tools
Amazon SageMaker, Apache MXNet, BigDL, Chainer, Deeplearning4j, Google AutoML, Keras, MLflow, NVIDIA AI Platform, PyBrainAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$300
|
Standard
$700
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 18 days |
Number of Revisions | 3 | 5 | 7 |
AI Model Integration | - | - | - |
Detailed Code Comments | - | - | - |
Knowledge Graph | - | - | - |
Model Documentation | - | - | - |
Ontology | - | - | - |
Source Code | - | - | - |
Taxonomy | - | - | - |
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About iheb
AI Mobile & XR Developer | Flutter | Unity | LLM Integration
93%
Job Success
Hammam-Lif, Tunisia - 2:57 am local time
What I build:
— AI-powered mobile apps (Flutter + OpenAI / Claude / Gemini)
Chat assistants, AI coaching apps, LLM-integrated workflows, real-time AI features inside existing apps
— Enterprise XR & Unity simulations
Training simulations, serious games, AR/VR prototypes for industrial and medical clients, Photon multiplayer, cross-platform deployment
— Flutter apps from MVP to App Store
Full-cycle development: architecture, Firebase backend, payment integration, App Store + Play Store submission
Why clients work with me:
→ I've maintained a $10K+ long-term engagement for a single client — I'm not a one-project developer
→ Production-ready code with clean architecture, not copy-paste tutorials
→ I flag problems before they become blockers, not after
→ Fast response, honest timelines, zero scope surprises
Tech stack:
Flutter · Dart · Unity · C# · Firebase · Node.js · OpenAI API · Claude API · Gemini · REST APIs · Photon · AR Foundation · XR Toolkit · .NET · Angular · React · Figma
Flutter developer | Flutter app developer | AI mobile app developer | AI app development | Flutter Firebase | Flutter OpenAI integration | Flutter Claude AI | Flutter Gemini | Unity developer | Unity XR developer | Unity AR developer | Enterprise XR simulation | Unity training simulation | Serious game developer | Flutter iOS Android | Mobile app developer | Flutter MVP developer | LLM integration developer | AI chatbot mobile app | Flutter Node.js | Flutter full stack | Unity C# developer | Photon Unity multiplayer | Flutter in-app purchases | App Store submission Flutter | Flutter bug fix | Unity bug fix | Flutter web app | Flutter desktop | AR Foundation Unity | XR Toolkit Unity | Firebase Flutter | Flutter REST API | AI agent mobile app | Flutter Riverpod | Flutter BLoC | Flutter clean architecture
Steps for completing your project
After purchasing the project, send requirements so iheb can start the project.
Delivery time starts when iheb receives requirements from you.
iheb works on your project following the steps below.
Revisions may occur after the delivery date.
Agil
Complete the project using Agil method