Shaping agentic AI
Agentic AI Foundation
Vstorm was the first agentic AI engineering company to join the Agentic AI Foundation and received its 2026 Open Agentic AI Innovation Award.
Read the announcementCode, data and prompts that stay yours
Agents in your sensitive processes without depending on one provider. We agree which processes stay under your control and keep their code, data and prompts with you.
Three-agent product advisor guiding customers through print-order configuration.
Read case studySupply chain intelligence agents reducing manual coordination overhead.
Live AI workloads moved to new hardware for on-prem LLM deployment.
AI agent implementation for a global automotive enterprise.
Text-to-workflow agents building validated node graphs inside the platform.
Read case studyHIPAA-compliant guideline-executing triage — 42% to 98% on the open 50-scenario benchmark.
Read case studyOne agent can call a model, embeddings, a vector store, tracing and a cloud.
Where it stallsPrompts written against one provider's API.
What we do A model layer lets models swap.
Where it stallsData and logs go to unchecked regions.
What we do Sensitive steps stay in your environment.
Where it stallsA vendor or integrator holds the keys.
What we do Keys, accounts and repositories in your name.
Code, data, prompts and keys stay in your name.
We list each model, store, cloud and tool, who holds its account, and which processes must stay yours.
A model layer lets an agent run hosted or open-weight, and an evaluation set judges every replacement.
Every critical dependency gets a tested exit plan, so you can change a provider, or us.
Open standards, open-source tooling and published research, all public, so you can check our engineering before you hire us.
Shaping agentic AI
Vstorm was the first agentic AI engineering company to join the Agentic AI Foundation and received its 2026 Open Agentic AI Innovation Award.
Read the announcementOpen source
Framework contributors
We maintain our own open-source agent framework and contribute fixes to the frameworks our agents run on, through official partnerships with their teams. Breaking changes reach us before they reach your codebase.
Research
Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results.
Track record
Our client agents were in production before most of today's agent frameworks had a stable release. You get a team that already knows what breaks between a promising demo and a system people rely on every day.
Each path starts with a free call with a founder or senior AI engineer. A build goes ahead only after its Proof of Value meets the agreed threshold.
We map one workflow with your experts and agree a success threshold before any build.
Best whenYou want the success test agreed before you commit to a build.
An evaluation decides when a replacement passes.
Bring one agent or planned build; we show which dependencies need an exit first.
Why mid-market firms move from frontier LLMs to small language models: lock-in, cost and control, plus a local Gemma-4 benchmark for a client.
Why bother with small models when capable LLMs are available? Because businesses may face stricter requirements than inidividual users
Agents that issue refunds need identity, authorisation, approval and audit before the call lands. What an agentic AI control plane is, and when you need one.