44% → 98%
Clinical triage — Schmitt-Thompson
Guideline-executing triage lifting a raw LLM from 44% to 98% on the open 50-scenario benchmark — a domain where a wrong answer is a patient-safety event.
Stalled AI pilot recovery
Your AI pilot works in the demo. We get it to production.
We audit the pilot you already have, fix what blocks production and hand it back to your team — whether it was vibe-coded, built by another vendor or locked into a platform.
Trusted by teams in production
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+11.76% orders from day 1 of the Australian launchThree-agent product advisor guiding customers through print-order configuration.
Read case study -
2 hrs → 3 min to generate a workflowText-to-workflow agents building validated node graphs inside the platform.
Read case study -
44% → 98% raw LLM vs guideline-executing accuracy on the open 50-scenario benchmarkHIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark.
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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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6 agents orchestrated by one supervisorMulti-agent financial coach: pattern extraction, gamified habit-building and two coaching personas with opposite attitudes.
Why pilots stall
Most pilots stall on engineering, not on the idea
A pilot proves that an agent can do the task once. Production needs it to do the task on real data, at volume, at a cost you can carry, and in code your team can change. Those are engineering problems, and they can be measured and fixed without starting over.
Every figure here is a published client outcome tied to the evaluation set it was measured against — the standard we suggest you hold any vendor to.
95.4%
Workflow success rate — Mixam
The client aimed for 80% accuracy to be usable. The delivered multi-agent product advisor exceeded it in production, with an 11.76% order increase on day one of the Australian launch.
2 hrs → 3 min
Workflow generation — Synera
Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass.
What you get
What we fix
Four ways a pilot gets stuck, and the work that moves it forward.
01
Vibe coding hardening
A prototype built fast with AI coding tools, cleaned up for production: tests, error handling, secrets out of the code, logging, and a structure your team can maintain.
02
Agentic performance optimization
Accuracy, latency and token cost measured on your own cases, then fixed where they break: retrieval, prompts, tool design, model choice and caching.
03
Agentic system audit & due diligence
An engineering review of an agent system before you scale it, buy it or invest in it: architecture, evaluation, security, data handling and what it will cost to run.
04
Migration off a platform
Agents moved from a closed or no-code platform to code and infrastructure you own, with the same behaviour checked against your existing cases before the switch.
How we work
From stalled pilot to a system your team runs
An audit first, then a fix plan with a go / no-go decision, then the work itself — measured against the same baseline from start to finish.
Audit
We read the code, run it against your real cases and write down what blocks production.
- Findings report
- Evaluation baseline on your data
- Ranked list of blockers
Fix plan
Each blocker gets a fix, an owner and an estimate, including the option to rebuild a part instead of patching it.
- Fix plan with estimates
- Go / no-go recommendation
- Fixed-price quote
Harden and hand over
We make the changes, prove them against the baseline and hand the system to your team with runbooks.
- Production-ready system
- Evaluation suite
- Runbooks and knowledge transfer
Proof in practice
Agents running in production, with the numbers behind them
Published outcomes, each linked to the full case study.
“My wish was to come to at least an 80% success rate in the workflow results, and by the time we finished the project, the success rate is, I believe, over 95.4% — so it definitely exceeded expectations.”
95.4%
Success rate in workflow results
Schmitt-Thompson · Clinical triage
Nurse-triage guidance where a wrong recommendation is a patient-safety event — staged retrieval and validation rather than one model answering in a single pass.
44% → 98%
raw LLM vs guideline-executing accuracy on the open 50-scenario benchmark
Why Vstorm
Credentials you can verify
Not adjectives — memberships, partnerships and open-source work anyone can check.
01
Agentic AI Foundation
The first AI consultancy in the foundation — contributing to the standards for how production agents get built.
02
Official Pydantic partner
Official Pydantic implementation partner, working with the framework since its beta versions.
03
Open source in production
Our agentic AI libraries are used by developers worldwide; we contribute to the tooling we deliver on, rather than only consuming it.
FAQ
Stalled pilot recovery, answered
What counts as a stalled AI pilot?
A pilot that works in a demo but has not reached production: answers that are right in testing and wrong on real data, costs or response times that do not hold at volume, code nobody on the team wants to own, or a platform contract that blocks the next step.
Do you take over code written by another vendor or with AI coding tools?
Yes. The audit starts from the code and the system as they are, whoever wrote them. Vibe-coded prototypes are a common starting point: the logic is often sound, while tests, error handling and structure are missing.
Will you recommend rebuilding instead of fixing?
When it is cheaper. The fix plan compares patching each part with rebuilding it, and the go / no-go recommendation can also be to stop the project if the use case does not justify the cost.
How do you check that the system works better after the changes?
The audit builds an evaluation baseline on your own cases before anything changes. Every fix is measured against that baseline, so the before and after are the same test.
Can you move our agents off a no-code or closed platform?
Yes. We rebuild the agents in code on infrastructure you control, run them side by side with the old setup on the same cases, and switch when the results match.
Who owns the system afterwards?
Your team. The code, data and infrastructure stay yours, and the hand-over includes runbooks and knowledge transfer so you can operate it without us.
Work with us
Have a pilot that will not reach production?
Book a session. We look at what you have and tell you what blocks it and whether it is worth fixing.
