--- title: Customer Stories and Case Studies | Pydantic description: >- Case studies and customer stories from teams that build, observe and evaluate AI applications with Pydantic AI and Pydantic Logfire, plus Gartner reviews. canonical: 'https://pydantic.dev/customers' --- > ## Documentation Index > Fetch the complete documentation index at: https://pydantic.dev/llms.txt > Use this file to discover all available pages before exploring further. ## Querying This Documentation **warning**: agent query parameters (`goal` and `organization`) not included. 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Example (replace the values with your own): `https://pydantic.dev/customers.md?goal=&organization=` --- > Markdown version of [Pydantic customers](https://pydantic.dev/customers) — the canonical HTML page. > > Site index: [/llms.txt](https://pydantic.dev/llms.txt) --- # Built with Pydantic Case studies from teams using our tools to build, observe, evaluate and ship reliable AI applications. ## Customer stories - [How SentinelOne evaluates the AI agents behind a 99.7% agreement rate](https://pydantic.dev/case-studies/sentinelone) — SentinelOne, 2026-11-04 ([markdown](https://pydantic.dev/case-studies/sentinelone.md)) 99.7%: Agreement rate across hundreds of thousands of agentic investigations SentinelOne's Purple AI team builds agents on Pydantic AI, scores every capability an investigation requires with Pydantic Evals in CI/CD, and runs a self-hosted Pydantic Logfire instance on Kubernetes. The practice is what lets the team hold a 99.7% agreement rate as models and tools change underneath, and ship new AI features 30% faster. - [How Sophos's SecOps AI team achieved complete observability with Logfire for their AI agents in production](https://pydantic.dev/case-studies/sophos) — Sophos, 2026-01-08 ([markdown](https://pydantic.dev/case-studies/sophos.md)) How Sophos surfaced silent production failures. “We can see the whole conversation thread, the LLM call, and every API hop, all in one go” (Peter Kim, Principal Software Engineer) Sophos's SecOps AI team implemented Pydantic Logfire for unified tracing across their AI-powered security solutions. With end-to-end visibility and SQL-based monitoring, engineers now detect issues proactively and run side-by-side LLM experiments with Pydantic Evals. - [How Evergreen.ai uses Pydantic Logfire and Evals to build observable, production-grade AI agents for financial services](https://pydantic.dev/case-studies/evergreenai) — Evergreen.ai, 2026-07-23 ([markdown](https://pydantic.dev/case-studies/evergreenai.md)) How Evergreen.ai runs its offline judges against live traffic. “The same evaluators we use with our actual eval dataset are now being applied against a real-time dataset” (Bryan Godwin, CTO) Evergreen.ai uses Pydantic Logfire to monitor AI agent runs, MCP tool performance, and guardrail events in real time. Combined with Pydantic Evals for offline and online evaluation, the team debugs agent issues in seconds and continuously measure quality across production conversations. - [How Elumynt cut LLM costs by over 80% with Pydantic Monty](https://pydantic.dev/case-studies/elumynt) — Elumynt, 2026-11-05 ([markdown](https://pydantic.dev/case-studies/elumynt.md)) 80%+: Lower LLM costs on daily agentic routines Elumynt, a digital marketing agency for SaaS and e-commerce brands, runs over 100 daily agentic routines on a Pydantic AI harness. By having the agent write the deterministic steps as Python programs and run them in Pydantic Monty, the team cut LLM spend on routines by over 80%, made results consistent from day to day, and let non-developers author their own programs. - [How Silex Data cut AIOps agent costs by up to 32% with Pydantic Monty](https://pydantic.dev/case-studies/silex-data) — Silex Data Solutions, 2026-10-29 ([markdown](https://pydantic.dev/case-studies/silex-data.md)) 10/10: Test incidents solved, including two that direct tool calling failed Silex Data Solutions, an infrastructure, security, and AI systems integrator, builds an AIOps platform that automates server patching, L1/L2 triage, and provisioning for enterprises. Its agents make 400+ tool calls per task against third-party observability tools and Ansible MCP servers. Running every call through code mode in Pydantic Monty cut cost by 26 to 32% in testing, kept the largest request under 85K tokens, and solved all ten test incidents, including the two that direct tool calling failed. - [How the Justice AI Unit validates AI output for judges and probation staff](https://pydantic.dev/case-studies/justice-ai) — Justice AI Unit, 2026-10-23 ([markdown](https://pydantic.dev/case-studies/justice-ai.md)) 1: Validated contract from Python backend to TypeScript client The Justice AI Unit, the Ministry of Justice team building AI tools for judges and probation staff, uses Pydantic Validation as the executable contract wherever information changes form: audio to transcript, transcript to draft, bundle to citations, Python object to TypeScript client. Pydantic AI puts the same contracts around the actions a model proposes. - [How General Intelligence Company Achieved 150x Faster Query Execution with Pydantic Logfire](https://pydantic.dev/case-studies/gic) — General Intelligence Company, 2026-05-04 ([markdown](https://pydantic.dev/case-studies/gic.md)) How GIC made live evaluation possible at all. 96.2%: Reduction in trace query time after migrating from LangSmith 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. - [Conversational print ordering through any AI assistant](https://pydantic.dev/case-studies/rendreal) — Rendreal, 2026-10-30 ([markdown](https://pydantic.dev/case-studies/rendreal.md)) How Rendreal turns a conversation into a print order. 3: Channels for ordering: web chat, WhatsApp, or any MCP-compatible assistant Rendreal connects an AI conversation to a physical print order. A customer describes what they need, creates or uploads artwork, picks print specifications, and finishes with a checkout link, all in one conversation. Vstorm orchestrated every Rendreal agent with Pydantic AI and exposed the tools to outside assistants through an MCP server. - [How Dosu cuts agent debugging time by 90% without leaving its coding agent](https://pydantic.dev/case-studies/dosu) — Dosu, 2026-08-19 ([markdown](https://pydantic.dev/case-studies/dosu.md)) How Dosu cut time to root cause across 54 agents. 90%: Less debugging time, using the Logfire MCP Dosu builds its AI agents on Pydantic AI and observes them with Pydantic Logfire, debugging from inside its coding agents through the Logfire MCP server. The team cut debugging time by 90%, brought time to root cause down from about an hour to minutes, and saved over $30k a year by surfacing hidden cost bugs. - [How Boosted.ai uses Pydantic Logfire to ensure reliability and scale across 50,000+ AI investment research workflows](https://pydantic.dev/case-studies/boostedai) — BoostedAI, 2026-01-01 ([markdown](https://pydantic.dev/case-studies/boostedai.md)) How Boosted.ai unified tracing across Python and Go. 12x: Faster to find and fix issues, from an hour to five minutes Boosted.ai implemented Pydantic Logfire for unified tracing and full-stack observability across 50,000+ AI research workflows. Allowing engineers to find and fix issues 12x faster, ensuring exceptional reliability and uptime for institutional finance clients. - [Zero-hallucination agentic RAG for clinical triage guidelines](https://pydantic.dev/case-studies/stcc) — Schmitt-Thompson Clinical Content, 2026-06-12 ([markdown](https://pydantic.dev/case-studies/stcc.md)) How STCC made every clinical answer reviewable. 0%: Hallucinations across 329 clinician-validated triage scenarios 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. - [MindsDB & Pydantic AI: How migrating from LangChain helped achieve 10x better agent performance](https://pydantic.dev/case-studies/mindsdb) — MindsDB, 2025-01-27 ([markdown](https://pydantic.dev/case-studies/mindsdb.md)) How MindsDB cut agent experiments from weeks to hours. 10x: Better agent performance, one month after the switch MindsDB, a company building AI data analysts, faced challenges with their agent implementation using LangChain, experiencing performance issues and a lack of programmatic control over agent behavior. They migrated to Pydantic AI, adopting a philosophy that treats agents as software through structured data validation and explicit state management. - [How Overjoy cuts AI agent debugging time from half a day to minutes](https://pydantic.dev/case-studies/overjoy) — Overjoy, 2026-04-29 ([markdown](https://pydantic.dev/case-studies/overjoy.md)) How Overjoy caught an agent burning 20x its usual spend. “Now we can see exactly what’s being called, how an agent thinks, what it costs” (Ishaan Nagpal, Founding Engineer) Overjoy replaced LangChain and LangSmith with Pydantic AI and Pydantic Logfire, cutting debugging time from half a day to minutes, catching a 20x cost spike before it burned their budget, and enabling their lean team to ship production-grade AI features fast. - [How Datalayer uses Pydantic AI and Logfire to power AI agents for data science on Jupyter](https://pydantic.dev/case-studies/datalayer) — Datalayer, 2026-03-03 ([markdown](https://pydantic.dev/case-studies/datalayer.md)) How Datalayer cut the tokens a data question costs. 90%: Best case reduction in token use for data analysis tasks Datalayer, a startup building AI-powered data analysis tools for Jupyter users, adopted Pydantic AI and Logfire after evaluating the agent frameworks market. With Pydantic AI's readable API and type safety, and Logfire's OpenTelemetry-based observability, they built a multi-protocol agent platform supporting AG-UI, ACP, Vercel AI, and A2A. - [How Lema AI cut code by 63% and boosted development velocity by 40%](https://pydantic.dev/case-studies/lemaai) — Lema AI, 2026-02-18 ([markdown](https://pydantic.dev/case-studies/lemaai.md)) How Lema AI rebuilt its risk agents on Pydantic AI. “We implemented the exact same thing in LangChain, LangGraph, and Pydantic AI. The latter was much cleaner” (Alon Menczer, Engineering Lead) Lema AI evaluated several agent frameworks before choosing Pydantic AI for its structured output validation, intuitive API, and seamless integration with Pydantic Logfire (our AI Observability Platform). The switch was a turning point in building their Agentic Risk Engineer - an autonomous system that investigates third-party security with forensic depth. - [Text-to-Workflow Agentic AI for Engineering Automation](https://pydantic.dev/case-studies/synera) — Synera, 2025-02-24 ([markdown](https://pydantic.dev/case-studies/synera.md)) How Synera turns text prompts into engineering workflows. 2 min: To write a prompt that builds a workflow which took up to an hour Synera, an AI agent platform for engineering that integrates with popular CAD, CAE and PLM software, built a text-to-workflow Agentic AI system using Pydantic AI that converts natural language prompts into executable workflows, cutting design time from hours to minutes. - [How Qualio ships AI in a regulated industry without breaking customer trust](https://pydantic.dev/case-studies/qualio) — Qualio, 2026-07-01 ([markdown](https://pydantic.dev/case-studies/qualio.md)) How Qualio turned plain-language criteria into release gates. 98.6%: Overall score on the compliance intelligence eval, across 24 tests Qualio, 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. - [How AutonomyAI’s agents catch their own regressions with Pydantic Logfire](https://pydantic.dev/case-studies/autonomyai) — AutonomyAI, 2026-07-17 ([markdown](https://pydantic.dev/case-studies/autonomyai.md)) How AutonomyAI let its agents check their own work. “Most of our engineers and our internal systems now use the Logfire MCP for basically everything” (Tammuz Dubnov, Co-founder & CTO) AutonomyAI, an agentic operating system that lets product and design teams ship merge-ready code into existing brownfield codebases, runs Pydantic Logfire as agent-queryable AI observability. Its agents query Logfire directly through the MCP server to debug their own code while building and to review behavior after merge, turning production telemetry into a feedback loop that files its own tickets. Over five weeks the loop caught 12 silent no-op deployments and surfaced 65 issues from real traffic, and Logfire carries the workload at roughly a third of the team’s Datadog spend. - [Multilingual AI Chatbot for Investigative Journalism Training](https://pydantic.dev/case-studies/arij) — ARIJ Network, 2025-01-26 ([markdown](https://pydantic.dev/case-studies/arij.md)) How ARIJ built a bilingual assistant for journalists. 22: Countries across the Middle East and North Africa reached 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. - [AI Agent for Order Recommendation in Self-Publishing](https://pydantic.dev/case-studies/mixam) — Mixam, 2025-01-06 ([markdown](https://pydantic.dev/case-studies/mixam.md)) How Mixam turned a billion print combinations into a guided order. 2x: Customer conversion, from around 20% to around 40% 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. ## Trusted by the world’s best engineering teams - “Logfire is the first observability product our team genuinely enjoys using.” (5 out of 5 stars. Chief Technology Officer, Software) - “I’ve been using it from the early stages of implementation, appreciating the simple setup with just a few lines of code and clarity of the UI” (5 out of 5 stars. Software Developer, IT Services) - “Everything in this product is easy to find, and you can extend it with your own tools if necessary, as it follows Python telemetry standards” (5 out of 5 stars. Software Developer, IT Services) - “Clean interface, ease of use, killer features” (5 out of 5 stars. Engineer, IT Services) - “Logfire is extremely intuitive for agent builders and the SDK/MCP is built thoroughly for coding harnesses” (5 out of 5 stars. Director of Research and Development, Software) - “Logfire has been incredible for our platform; our development team couldn’t be happier. We started with the basic observability and now have expanded to use the full AI tool set” (5 out of 5 stars. Chief Strategy Officer, Software) - “Super clean UI - Love that it’s built on top of open telemetry - Really easy to setup and get logging w/ right away” (5 out of 5 stars. Engineer, Healthcare and Biotech) Reviews from [Gartner Peer Insights](https://www.gartner.com/reviews/product/pydantic-logfire?marketSeoName=ai-evaluation-and-observability-platforms).