You will get Custom Retrieval-Augmented Generation (RAG) for Your Internal Documents
Project details
Most organizations already have the knowledge they need —> they just can’t access it efficiently.
A Retrieval-Augmented Generation (RAG) system changes that by combining semantic search with language models, enabling teams to query their internal documents in natural language and receive relevant, cited answers instantly.
This sprint helps you design, prototype, or deploy a fully functional RAG pipeline: from embedding creation and vector database setup to retrieval logic and response generation.
You’ll receive:
• A clear architecture blueprint tailored to your data environment
• Tested pipelines ready for scaling or integration
• Documentation and training materials for your team to extend the system confidently
Built using modern frameworks, each implementation is optimized for speed, accuracy, and maintainability.
With over a decade of experience developing AI-driven knowledge systems across Japan, the US, and the EU, I help companies turn unstructured information into structured intelligence, empowering faster decisions, better collaboration, and measurable productivity gains.
A Retrieval-Augmented Generation (RAG) system changes that by combining semantic search with language models, enabling teams to query their internal documents in natural language and receive relevant, cited answers instantly.
This sprint helps you design, prototype, or deploy a fully functional RAG pipeline: from embedding creation and vector database setup to retrieval logic and response generation.
You’ll receive:
• A clear architecture blueprint tailored to your data environment
• Tested pipelines ready for scaling or integration
• Documentation and training materials for your team to extend the system confidently
Built using modern frameworks, each implementation is optimized for speed, accuracy, and maintainability.
With over a decade of experience developing AI-driven knowledge systems across Japan, the US, and the EU, I help companies turn unstructured information into structured intelligence, empowering faster decisions, better collaboration, and measurable productivity gains.
AI Algorithms
Large Language Model, Multimodal Large Language ModelAI Applications
AI Chatbot, Image Analysis, Image Recognition, Natural Language Generation, Natural Language Understanding, Sentiment Analysis, Text RecognitionAI Development Language
PythonAI Tools
Hugging Face, PyTorch, TensorFlow, Word2vecAI Models
BERT, ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$750
|
Standard
$1,650
|
Advanced
$2,950
|
|---|---|---|---|
| Delivery Time | 3 days | 7 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.
Fast Delivery
+$150 - $975
Additional Revision
+$175Frequently asked questions
About Rodrigo
Senior Data Scientist & AI Founder
Tokyo, Japan - 10:26 am local time
Over the past decade, I’ve co-founded and led AI ventures across Europe, the US, and Japan, building systems that search smarter, predict demand accurately, and personalize user experiences.
My expertise focuses on three applied AI pillars:
1. RAG & LLM Systems – Architecting retrieval-augmented generation pipelines that make enterprise knowledge instantly accessible, reducing manual search time and LLM inference costs.
2. Time-Series Forecasting – Designing demand prediction and stock optimization models for retail, logistics, and energy, driving efficiency and reducing waste.
3. Recommendation Engines – Delivering personalization systems that increase engagement and retention for media, SaaS, and e-commerce platforms.
Deep technical understanding (Python, PyTorch, TensorFlow, Qdrant, ColBERT, AWS/GCP) with business acumen from years of founding and scaling startups, fridging data science and product strategy to turn AI into tangible ROI.
Currently, Co-Founder & CTO of WhiteNarwhal Japan K.K. / Ikkaku AI Lab, an applied-AI company and incubator that helps startups and enterprises deploy production-ready AI systems; and of our first AI Lab spin-off company: Monju AI, a document-centered workspace powered by retrieval and multimodal AI.
Steps for completing your project
After purchasing the project, send requirements so Rodrigo can start the project.
Delivery time starts when Rodrigo receives requirements from you.
Rodrigo works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Context Alignment
Kick-off call or written briefing to understand the project goals, data availability, and existing systems. Clarify desired outcomes and KPIs.
Architecture Design
Create or refine your RAG architecture (retriever, embeddings, vector DB, LLM).