I will a custom ai agent and rag architecture for your data


Level 1
About this gig
Stop using generic AI. Build an Agent that actually knows your business.
Most AI chatbots fail because they lack context or hallucinate. I build Custom AI Agents using RAG (Retrieval-Augmented Generation) architecture, ensuring your AI provides accurate, grounded answers based exclusively on your private company data.
️ Technical Scope:
- Custom RAG Pipelines: Integrating your PDFs, SQL databases, or Notion workspaces.
- Agentic Frameworks: Using LangChain or AutoGPT for complex reasoning.
- Vector Databases: Expert setup of Pinecone, Weaviate, or ChromaDB.
- Full Integration: Deploying your agent into your existing SaaS or FinTech platform.
Get to know Louis L
Web3 and AI DevOps Architect : Smart Contracts, Private Nodes and LLM
Level 1
- FromUnited Kingdom
- Member sinceJan 2019
- Avg. response time1 hour
- Last delivery1 month
Languages
English, French, Spanish
My Portfolio
FAQ
My data is highly sensitive. Is it safe?
Absolutely. As an Infrastructure Architect, I prioritize data sovereignty. I can deploy the RAG system on your private VPC or use local vector databases (ChromaDB/Weaviate) to ensure your data never trains public models.
Which AI models do you use?
I work with OpenAI (GPT-4o), Anthropic (Claude 3.6), and open-source models like Llama 3. For high-privacy Enterprise needs, I recommend local deployments of open-source LLMs.
Can the agent interact with my existing tools (Slack, CRM, SQL)?
Yes. In the Standard and Premium tiers, I build "Agentic" workflows using LangChain, allowing the AI to perform "Tool Calling"—it can query your database, send Slack alerts, or update your CRM based on instructions.
Do you offer maintenance?
AI models and libraries evolve fast. I offer monthly maintenance subscriptions to monitor your vector database health, update model versions, and refine retrieval accuracy (RAG Evaluation).

