Learn how Pydantic AI's focus on type-safety made it the right choice for the implementation of this powerful RAG vector store.
Turning printing complexity into conversational simplicity
Mixam, a global self-publishing company, partnered with Vstorm to build an AI agent using Pydantic AI that helps customers navigate complex printing specifications, reducing support burden while improving customer experience for all users who need ordering guidance.
“Our project required heavy experimentation with different models and approaches. PydanticAI proved to be flexible enough to make these experiments possible, without losing the robustness required for the production environment. That's why we decided to use it in our project.”— Lucian Puca, Digital Product Manager, Mixam
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Related case studies
View all case studiesSchmitt-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.
General Intelligence Company (GIC) migrated to Logfire, Pydantic’s AI Observability Platform and Pydantic AI to build a live evaluation system for their autonomous agents. The results? Query performance improved 150x, eliminating rate limits and enabling real-time deviation detection and agent self-correction that was impossible before.
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