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 announcementLLMOps services
LLM systems you can measure, afford and change
Change prompts and models without surprises in quality or cost. Every change is tested before release, rolled out in stages and costed per request.
50+ workflows agentified since 2017
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~20% → ~40% conversion to a final saleThree-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 GaudiLive 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 create a workflowText-to-workflow agents building validated node graphs inside the platform.
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42% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmarkHIPAA-compliant guideline-executing triage — 42% to 98% on the open 50-scenario benchmark.
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Why LLM operations stall
Uptime graphs do not show when the answers get worse.
A prompt edit can pass every health check and still weaken answers.
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Where it stallsPrompt edits ship after spot checks.
What we do Evaluations run on every change first.
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Where it stallsNobody can compare live with previous.
What we do Every release measured against the last.
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Where it stallsToken costs surface on next month's invoice.
What we do Cost per request tracked per workflow.
How we work
Each change tested, released in stages and costed
From how changes reach users today to a release path your team runs.
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Your release path, mapped
We trace how a change reaches users today: who edits prompts, what gets tested, what it costs.
- Workflows, owners and costs
- Release gates defined
- Cost baseline per workflow
On the open 50-scenario benchmark, a raw LLM scored 42%; the guideline-executing system scored 98%. - 0203
Releases gated on evaluations
Each release passes an evaluation gate, ships to a slice of traffic first and is traced.
- CI/CD in your cloud
- Tracing and alerts
- Drift checks on answers
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Costs and models under your control
Every version is logged with scores and cost per request; cheaper models ship only when they pass.
- Version history with costs
- Model swaps without rebuilds
- Runbooks and training
Engineering in the open
We help build what agentic AI runs on
Open standards, open-source tooling and published research, all public, so you can check our engineering before you hire us.
Open source
We build open-source agent tooling that other engineering teams install
10M+ PyPI downloads
4,000+ GitHub stars
33 public repositories
Framework contributors
When the framework breaks, we fix it at the source
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
Our engineers and PhDs publish peer-reviewed research
Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results.
Track record
Frontier AI teams now build on what we shipped first
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.
Case studies
Systems our clients run and measure
Engineering workflows built in minutes, validated before they run
The agent drafts each workflow and checks it in several steps before it runs, so engineers skip the manual setup.
2 hrsto prepare a workflow by hand
3 minto create one with the agent
A print-order advisor that lifted conversion to a final sale
Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations.
~20% → ~40%conversion to a final sale
96%+web-to-print orders handled end to end
Clinical triage that executes the guideline, step by step
The model answers the assessment questions. The guideline decides the disposition, the same way every time.
42% → 98%accuracy, raw LLM vs guideline-executing
96%weighted F1 across 566 scenarios
Engagement models
Ways to start
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.
Start with one workflow
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.
FAQ
Questions about LLMOps services
What is LLMOps?
LLMOps is the practice of releasing and running systems built on large language models: versioning prompts and models, gating each change on evaluations, rolling it out in stages, tracing every call and tracking cost per request. It extends MLOps to systems whose behavior changes when a prompt or a model changes.
How is LLMOps different from MLOps?
MLOps manages models you train: data pipelines, training runs and model registries. LLMOps manages systems built on large language models you mostly do not train, where prompts, model versions, retrieval settings and tool calls change behavior, so each release is gated on evaluations and its cost is tracked per request.
What does Vstorm's LLMOps service include?
A map of how a change reaches your users today, then a release path where quality and cost checks run before any release: evaluation gates in CI/CD, a slice of traffic first, tracing and alerts on every call, drift checks on inputs and answers, and a log of each version with its evaluation results and cost per request. Tracing goes to Logfire or the tools you already run, such as Grafana or Datadog.
How do you control LLM costs in production?
Each version is logged with its cost per request next to its evaluation results. A cheaper model or a shorter prompt ships only when the evaluations still pass, so cost goes down without a silent drop in quality.
Can LLMOps work with open-weight models?
Yes. The model sits behind one interface, so you can move to a cheaper hosted model or a self-hosted open-weight model once the evaluations allow.
Who runs the system after the engagement?
Your team. The pipelines, evaluation sets, dashboards and runbooks live in your accounts and repositories, with training for the people who will run releases, and there is no license or platform fee to Vstorm.
LLMOps consultation
Know what each release does and costs
Bring one LLM system; we map how its changes reach users.
From the blog
Reading on LLMOps
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