Learn how Pydantic AI's agent framework enabled a hallucination-free, multilingual chatbot that seamlessly handles both English and Arabic for journalist training.
From 1% response rate to AI-powered journalism training
ARIJ Network, connecting investigative journalists across 22 countries in the Middle East and North Africa, partnered with Vstorm to build a RAG-based AI chatbot using Pydantic AI. The bilingual system (English/Arabic) transformed their training process from handling just 1% of inquiries to delivering reliable, fact-checked knowledge at scale while creating new revenue streams.
“Using the PydanticAI framework, it was possible to build the agent itself and a set of tools: one to connect with the database, and one to validate the answers and ensure their language correctness. With that, the agent itself could deliver the best response possible.”— Vstorm, ARIJ Network
Products Used:
Related case studies
View all case studiesQualio, a quality and compliance platform, ships AI features into a regulated industry where customers audit every release. The team uses Pydantic AI as its agent framework and Pydantic Evals to test LLM behavior, gating every deploy behind a pass rate threshold across roughly 160 test cases and 300 evaluations. Because the eval criteria are written in plain language, Qualio's customers' compliance teams can read and approve the same evidence during due-diligence reviews.
Schmitt-Thompson Clinical Content (STCC), the source of nurse triage guidelines used by most North American medical call centers, partnered with Vstorm to build a four-stage agentic RAG system on Pydantic AI. By treating the triage decision trees as the only source of truth and tracing every step with Pydantic Logfire, the system reached 0% hallucinations across 329 clinician-validated scenarios.
Explore Logfire
Explore our open source packages