Healthcare hallucination rate
65.9%
Hallucination rates of "raw" LLMs in healthcare-related environments
Pydantic Development Services
Ship Python services and agents with Pydantic data contracts.
Vstorm, an official Pydantic implementation partner, builds trustworthy and reliable AI agents with the Pydantic AI toolset, bringing stability and flexibility to real-life agentic AI solutions.
Trusted by operators
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+11.76% orders from day 1Three-agent product advisor guiding customers through print-order configuration.
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Supply chain intelligence agents reducing manual coordination overhead.
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37 kernels ported from CUDA to Intel GaudiProduction AI workloads moved to new hardware for on-prem LLM deployment.
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AI agent implementation for a global automotive enterprise.
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2 hrs → 3 min to generate a workflowText-to-workflow agents building validated node graphs inside the platform.
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0 hallucinations across 329+ validation scenariosHIPAA-compliant agentic RAG over proprietary clinical triage guidelines.
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The Pydantic stack
The trusted partner for Pydantic in production
Vstorm is an official Pydantic implementation partner. Our engineers are highly knowledgeable about the tools used to build reliable and flexible solutions to real-life business problems.
We contribute to pydantic-ai on GitHub — filing issues, contributing fixes, and working with the Pydantic core team. Building agents rather than validating data? See our Pydantic AI development services.
"Vstorm haven't just adopted Pydantic AI, they've helped shape it — contributing extensions, pushing on the rough edges, and showing us where real-world agent systems break. That kind of collaboration is gold for an open source project."
Samuel Colvin, Founder & CEO, Pydantic
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Controlled outputs
Outputs are validated against typed schemas, so malformed or hallucinated values are caught before they reach production.
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Settings management
Configuration and settings management.
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Pipeline validation
Data in the pipeline needs to be validated and checked before processing.
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Quality extraction
The extracted data must meet a defined quality bar, validated at the boundary.
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Typed contracts
Typed contracts at service boundaries.
The Risk Without Validation
Why this distinction matters in production
These numbers reflect what happens when LLM outputs are not validated against typed schemas.
Both products address this at different layers. Pydantic catches malformed data entering and leaving your Python services. Pydantic AI catches malformed or schema-violating LLM outputs at the agent boundary, before they reach the rest of your system.
Multi-agent chain reliability
20%
End-to-end success in a 10-step workflow when each step is ~85% reliable — compound failure across the chain, not a single bad model.
Case Studies
Production systems with validated outputs
Next step
Put typed contracts around your LLM outputs
A 30-minute call with an engineer — we review where unvalidated outputs put your pipeline at risk and what a validation layer would take.
Our process
From use case to production — the TriStorm methodology
We do not start by building. We start by finding the right thing to build. TriStorm is our three-phase framework — from Pydantic feasibility to a validated production system your team owns.
Strategic alignment and planning
Consulting-led planning before any code. Deep interviews and scoping workshops to frame the problem, map where Pydantic and Pydantic AI create the highest operational leverage, and produce a prioritized roadmap with an ROI model per use case.
- Deep interviews and scoping workshops with your operations and technical leadership
- Where Pydantic and Pydantic AI create the highest leverage across your Python and AI stack
- Prioritized roadmap with an ROI model per use case
Proof of Value
We assess where Pydantic and Pydantic AI fit in your Python and AI architecture, build a proof of value in context, and surface potential problems or improvements before full build.
- Assessment of where Pydantic and Pydantic AI fit in your Python and AI architecture
- Proof of value that shows the solution in context
- Team finds potential problems or improvements before full build
Process augmentation
We embed with your team to ship the production-ready solution on the Pydantic stack — data and output validation at every boundary, pipeline stability verified under production-like conditions — and transfer ownership so your engineers run and extend it without us. No vendor lock-in.
- Production-ready solution on the Pydantic stack, validated at every boundary
- Embedded engineering and structured knowledge transfer — runbooks, not a doc drop
- Ownership transferred to your team; no vendor lock-in
Why Vstorm
Three reasons mid-market and enterprise teams work with us on Pydantic
We have been building production Python systems since 2017 and contributing to the Pydantic AI framework since beta — across the validation library and the agent framework.
01
Experience — 30+ production deployments on the Pydantic-powered stack
Deep expertise across Pydantic, Pydantic AI, FastAPI, LangGraph, CrewAI, and LlamaIndex. Our 25 AI specialists deliver type-safe solutions tailored to your existing Python services — whether the problem is at the data layer or the agent runtime layer.
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Stack — Specialized, production-ready tooling
We combine Pydantic (schema enforcement) and Pydantic AI (agent orchestration) with Logfire for observability and OpenTelemetry for tracing. Every project is accurate, debuggable, and cost-controlled.
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Support — End-to-end ownership
Full support from consultation through deployment, monitoring, and ongoing optimization. That includes upgrades to new Pydantic AI releases and Pydantic v2 schema changes as the ecosystem evolves.
FAQ
Frequently asked questions
What is Pydantic?
Pydantic is the most widely used data validation library in the Python ecosystem. It uses type hints to define schemas and validates data against them at runtime. Malformed inputs surface as structured errors — not silent failures. Its Rust core makes it one of the fastest validation libraries available. It is the validation layer underneath the OpenAI SDK, Anthropic SDK, FastAPI, LangChain, LlamaIndex, and CrewAI.
What is Pydantic AI and how is it different from Pydantic?
They are distinct products. Pydantic enforces data schemas at runtime across any Python application. Pydantic AI is a separate agent framework — built on top of Pydantic by the same team. It adds type-safe agents, validated tool calls, dependency injection, streaming validation, automatic retries on malformed LLM outputs, and Logfire observability. Use Pydantic for data contracts. Use Pydantic AI when you need a full agent runtime around an LLM.
Do I need Pydantic, Pydantic AI, or both?
It depends on where your problem lives. For typed schemas in FastAPI, validated config settings, or ETL pipelines — use Pydantic. For LLM agents that must return structured, validated outputs — use Pydantic AI. Many production systems use both: Pydantic at the data layer, Pydantic AI at the agent layer.
What are the main use cases for Pydantic (without the AI framework)?
Enforcing data contracts in FastAPI or Django Ninja APIs, managing application configuration with type safety, validating inputs and outputs in ETL pipelines, serializing and deserializing nested data structures, and generating JSON schemas from Python models. None of these require Pydantic AI.
Does Pydantic AI work with any LLM provider?
Yes. Pydantic AI supports OpenAI, Anthropic, Google, Groq, Mistral, Cohere, AWS Bedrock, and any OpenAI-compatible API, plus locally hosted models. You can swap providers without rewriting agent logic. The framework supports fallback chains to route to a cheaper or faster model when the primary one is unavailable.
What is Pydantic Logfire?
Logfire is the Pydantic team's OpenTelemetry-based observability platform for Python and AI workloads. It provides real-time LLM call traces, cost tracking, eval-based monitoring, and tight Pydantic AI integration. It exports OTel, so you can send the same data to Datadog, Grafana, or Honeycomb instead of running Logfire as a standalone backend.
Get started
Ready to build your Pydantic solution?
Whether you are enforcing data contracts in an existing Python service, retrofitting validation onto an LLM pipeline, or building a Pydantic AI agent from scratch — our team can scope the problem, design the right stack, and take you from concept to production.