You will get RAG Architecture Framework Implementation
Top Rated
Project details
Retrieval-Augmented Generation (RAG) is a cutting-edge AI architecture that combines the power of large language models with external knowledge retrieval. This project will deliver a custom RAG framework designed to:
Improve accuracy and relevance of AI-generated responses
Integrate domain-specific knowledge seamlessly
Enhance contextual understanding in AI interactions
Reduce hallucinations and false information in AI outputs
Optimize for scalability and performance
Our implementation will be tailored to your specific needs, whether it's for customer support, content creation, data analysis, or any other AI-driven application.
Key Features:
Custom knowledge base integration
Efficient vector storage and retrieval system
Advanced query processing and reformulation
Dynamic context window management
Flexible output generation controls
Scalable architecture for growing datasets
Why Choose This Service:
Expertise in state-of-the-art AI architectures
Custom-tailored solution for your specific use case
Emphasis on scalability and future-proofing
Comprehensive documentation and knowledge transfer
Ongoing support and consultation available
Improve accuracy and relevance of AI-generated responses
Integrate domain-specific knowledge seamlessly
Enhance contextual understanding in AI interactions
Reduce hallucinations and false information in AI outputs
Optimize for scalability and performance
Our implementation will be tailored to your specific needs, whether it's for customer support, content creation, data analysis, or any other AI-driven application.
Key Features:
Custom knowledge base integration
Efficient vector storage and retrieval system
Advanced query processing and reformulation
Dynamic context window management
Flexible output generation controls
Scalable architecture for growing datasets
Why Choose This Service:
Expertise in state-of-the-art AI architectures
Custom-tailored solution for your specific use case
Emphasis on scalability and future-proofing
Comprehensive documentation and knowledge transfer
Ongoing support and consultation available
AI Algorithms
Large Language Model, Long Short-Term Memory Network, Multimodal Large Language ModelAI Applications
AI Chatbot, AI Content Creation, AI Mobile App Development, AI Text-to-Image, AI Text-to-Speech, AI-Enhanced Classification, AIOps, Conversational AI, Natural Language Understanding, Text Recognition, Time Series AnalysisAI Development Language
PythonAI Models
ChatGPT, GPT-3, GPT-4, LLaMA, OpenAI Codex, WhisperWhat's included
| Service Tiers |
Starter
$2,000
|
Standard
$3,000
|
Advanced
$5,000
|
|---|---|---|---|
| Delivery Time | 14 days | 28 days | 42 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
+$500 - $1,000
Additional Revision
+$200
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DW
Des W.
Aug 6, 2025
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Simha D.
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Very professional, understood my needs and executed as needed
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Farid B.
Apr 23, 2024
Use BuildShip for an epub project
Solid developer
About Ugo
Senior AI Engineer | RAG, LLM Agents & Fintech | Python & FastAPI
100%
Job Success
Dubai, United Arab Emirates - 9:06 am local time
🔥 𝐖𝐡𝐚𝐭 𝐈 𝐁𝐮𝐢𝐥𝐝
🧠 GraphRAG & Evidence-Grounded RAG, Hybrid retrieval + knowledge-graph traversal that keeps exact metrics deterministic and every claim tagged stated vs. inferred, with separate response contracts per audience so internal diagnostics never leak to clients.
🎬 Multi-Agent Creative & Video Pipelines Orchestrated agent systems (strategy → copy → layout, or storyboard → scene → assembly) where nothing advances without human approval, every correction creates an immutable child version, and "packaging" is deliberately kept separate from "publishing."
📊 ML Evaluation Infrastructure, Built the measurement harness before the models. Result: 21% performance lift across three architectures, catching 84/98 fraud cases in a 284K-transaction dataset at a 0.02% false-positive rate.
💳 Fintech & Payment Automation, Daily multi-processor reconciliation (Stripe, PayPal, Square, ACH) with fee-aware matching and an auditable exception queue 40+ hours/month saved, ~90% fewer reconciliation errors.
🛠 𝐂𝐨𝐫𝐞 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤
Python · FastAPI · PostgreSQL · React · TypeScript · n8n · scikit-learn · XGBoost · Claude API · OpenAI · Pinecone · Docker · AWS · GitHub Actions
💡 𝐇𝐨𝐰 𝐈 𝐖𝐨𝐫𝐤
✅ Architecture before code, evaluation before deployment, every model and pipeline gets tested before it ships, not after it breaks in front of a client.
✅ Failure is a product state, not a bug recoverable, visible, and logged, never silently retried.
✅ Full ownership, start to finish I've been the only engineer on these systems from architecture through production rollout.
📩 𝐋𝐞𝐭'𝐬 𝐓𝐚𝐥𝐤
If you need AI that actually works in production not a demo that breaks the first time it sees real data, tell me what you're building, and I'll map the fastest path to get it there.
Steps for completing your project
After purchasing the project, send requirements so Ugo can start the project.
Delivery time starts when Ugo receives requirements from you.
Ugo works on your project following the steps below.
Revisions may occur after the delivery date.
We have a meeting to determine nature of project
Meet in person and breakdown on each steps