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RauPro/README.md

Hi there, I'm Raúl Guillén 👋

I am the Founder & Lead Engineer of Converza AI, architecting the first ConTech and Spanish-first, production-grade AI agent workspaces for heavy enterprise industries (AEC). My expertise lies at the intersection of high-performance distributed backends, custom multi-agent routing systems, and advanced hybrid retrieval infrastructure.

With a deep background in competitive programming (ICPC Finalist) and extensive software engineering experience delivering generative AI frameworks for firms like Bain & Company, I focus on building resilient, deterministic LLM systems that run at scale.

Converza AILinkedInEmail


🛠️ Core Stack & Focus Areas

  • Agentic Frameworks & Routing: Production Pydantic AI systems, multi-agent orchestrations, tool/function calling logic.
  • Data & Vector Infrastructure: Hybrid Postgres RAG (pgvector + full-text), RRF fusion, Cohere reranking, multi-format parsing pipelines.
  • Backend & Systems: Asynchronous Python (FastAPI, Asyncio, Pydantic), C++, WebSockets/SSE streaming, distributed systems.
  • MLOps & Observability: Enterprise logging and tracing via Logfire, Sentry, PostHog, hyperparameter optimization.

🚀 Key Architectures

🌌 Converza AI — Core Engine & Agent Workspace

  • Architecture: Enterprise-grade monorepo combining a high-concurrency FastAPI streaming backend with a Next.js 14 App Router UI.
  • Core Mechanisms: Engineered a production Pydantic AI agent loop that dynamically calls system-level tools (search_kb, read_kb_pdf, edit_document) based on explicit, structured input validation.
  • Retrieval Pipeline: Implemented an state-of-the-art hybrid search index in pgvector utilizing full-text keyword fusion, Cohere rerank API calls, and vision-based PyMuPDF rasterization to embed scanned blueprints and graphical drawing layouts.
  • Stack: FastAPI, Pydantic AI, Supabase/Postgres, pgvector, LiteLLM, Tiptap, Logfire.

⛓️ Async MEV Sandbox Bot

  • Architecture: High-throughput, asynchronous blockchain monitoring tool designed to capture real-time market discrepancies.
  • Core Mechanisms: Utilizes asyncio loops and web3.py to stream the Sepolia testnet mempool, predicting real-time transaction profitability and programmatic liquidity interactions.
  • Stack: Python, Web3, Asyncio, Uniswap V2, QuickNode.

📊 System Activity

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  1. Algorithmic-Toolbox Algorithmic-Toolbox Public

    My personal repository for problems of Coursera course Algorithmic Toolbox By University Of California San Diego

    C++ 5

  2. GiphySearch GiphySearch Public

    App to search gifs, using Giphy Api

    JavaScript 4

  3. Pokemons-manager Pokemons-manager Public

    JavaScript 4

  4. Competitive-Programming Competitive-Programming Public

    Python 3 1

  5. My-blog My-blog Public

    JavaScript 3