Pablo G.

Most RAG applications never go beyond an MVP or a prototype. There's a reason for that: they are not architected or built with production-grade capabilities in mind. As a result, these MVPs: • Produce inaccurate results, such as hallucinations and biases • Are insecure, vulnerable to DDoS attacks, prompt injection, cascading failures, and other risks • Are highly expensive in terms of token usage If your next goal is to build a MVP, build it with production capabilities in mind from the start. The transition from MVP to full production will then be low-friction and straightforward. If you're ready to build a production-ready application right away, make sure it includes all the necessary capabilities to avoid unnecessary money spent and wasted effort. I am a software engineer with more than 10 years of experience developing production-grade data pipelines, search applications, and RAG systems. I have successfully delivered numerous RAG and Agentic RAG projects with true production-grade capabilities . What I Provide: Production-grade architecture and rapid development of RAG applications, including Agentic RAG systems, that are able to: • Accurately parse, chunk and ingest millions of documents • Handle thousands of requests per second • Remain highly secure against DDoS attacks, prompt injection, and other threats • Feature highly efficient data ingestion pipelines (e.g., accurately parsing thousands of PDF files, including tables spread across multiple pages and unknown layouts) • Deliver highly accurate RAG and Agentic RAG performance through data-driven error analysis AI evaluators and systematic improvement of prompt engineering, retrieval (like vector search), and LLM capabilities. If you want to start building serious, production-grade RAG applications instead of toy prototypes, contact me for a free assessment of your idea's viability and technical feasibility.