You will get RAG Pipeline for Documents & Knowledge Bases
Top Rated
Project details
You get a RAG system that lets your team search, ask questions, and get answers from your own documents. I can build document ingestion, embeddings, vector search, reranking, retrieval logic, and backend APIs. This is useful for companies with PDFs, policies, support docs, internal knowledge bases, tickets, or technical documents.
AI Algorithms
Autoencoder, Convolutional Neural Network, CycleGAN, Generative Adversarial Network, Large Language Model, Long Short-Term Memory Network, Radial Basis Function Network, Restricted Boltzmann Machine, StyleGAN, YOLOAI Applications
AI Chatbot, AI Content Creation, AI Mobile App Development, AI Text-to-Image, AI Text-to-Speech, AI-Generated Code, Conversational AI, Image Analysis, Image-to-Image Translation, Natural Language Generation, Neural Machine Translation, Sentiment AnalysisAI Development Language
PythonAI Tools
Azure OpenAI, Bing AI, GitHub Copilot, Gradio, Hugging Face, Jasper AI, NVIDIA AI Platform, PyTorch, Streamlit, TensorFlowAI Models
BERT, BLOOM, ChatGPT, GPT-4, LLaMA, OpenAI Codex, WhisperWhat's included $3,500
These options are included with the project scope.
$3,500
- Delivery Time 5 days
Frequently asked questions
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DS
David S.
Jul 24, 2026
Multi-agent platform
Ignas was a pleasure to work with. He quickly got to the important questions, and always provided advice with good logic. We look forward to working with him again.
KG
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Philip N.
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30 minute consultation
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Hila F.
May 1, 2026
AI Automation Engineer Needed for Claude Code Integration
Thank you, Ignas!
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Gabriella H.
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API Service with FastAPI
About Ignas
AI Agent Development Expert | RAG, Python, LangChain, LLM AI Engineer
100%
Job Success
Rio de Janeiro, Brazil - 3:20 pm local time
I help companies design, build, and deploy AI Agent Development, RAG pipelines, LLM apps, AI automation systems, internal AI tools, and production-ready AI software using Python, LangGraph, LangChain, LlamaIndex, FastAPI, Rust, TypeScript, OpenAI, Claude, Gemini, vector databases, and cloud infrastructure.
What I do best:
1. AI Agent Development
2. RAG pipelines and retrieval systems
3. LLM app development
4. AI internal tools
5. AI document processing
6. Knowledge base and chatbot systems
7. API development and AI integrations
8. Vector database setup and optimization
9. AI workflow automation
10. AI audits, scoping, and technical planning
My strongest work is around AI agents and RAG.
For AI agents, I build systems that can reason, use tools, call APIs, work with memory, and complete multi-step tasks without needing constant human input.
For RAG, I build retrieval systems that go beyond basic vector search. This includes document ingestion, hybrid search, reranking, embeddings, layout-aware parsing, evaluation, and grounded answers from your own company data.
I am an expert at:
Python, Rust, TypeScript, FastAPI, React, Next.js, LangGraph, LangChain, LlamaIndex, CrewAI, OpenAI, Claude, Gemini, Hugging Face, Milvus, Pinecone, PGVector, Chroma, Weaviate, PostgreSQL, Docker, Azure, AWS, RunPod, Vast AI, Linux, N8N, and CI/CD pipelines.
The clients I work best with are B2B companies, enterprise teams, software companies, AI startups, and businesses with complex documents, workflows, internal data, tickets, knowledge bases, or operations that need to become easier to search, automate, and manage.
If you need AI Agent Development, RAG, LLM app development, Python backend engineering, or production AI infrastructure, send me a message.
I wanna hear your idea now, how can I help?
Steps for completing your project
After purchasing the project, send requirements so Ignas can start the project.
Delivery time starts when Ignas receives requirements from you.
Ignas works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Document Scoping
We start with a short call to understand your documents, use cases, and query patterns. I map out document types, accuracy needs, and success metrics, then send a written scope before any build starts.
Pipeline Build and Integration
I build the RAG pipeline based on the agreed scope: ingestion, chunking, embeddings, vector storage, retrieval, reranking, and backend APIs. You get progress updates and a working version to test on your real documents.