Production deployments
Live agent systems in the client's own environment, handling real workloads.
Sell what today only your
best people can do.
Agentic systems can carry the judgement your specialists apply by hand, at a volume and a price your customers can buy. That moves your offering, your cost to serve, and the accounts you can win.
Three-agent product advisor guiding customers through print-order configuration.
Read case studySupply chain intelligence agents reducing manual coordination overhead.
Production 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 — 44% to 98% on the open 50-scenario benchmark.
Read case studyAn internal automation that gets something wrong costs you an hour of rework. The same mistake inside an offering reaches the person who paid for it, and it comes back as a refund, a churn event and a support queue. That is a higher engineering bar than a pilot has to clear, and clearing it is the whole job.
Applied means it holds up in front of the people paying you.
Live agent systems in the client's own environment, handling real workloads.
PhDs, published researchers and open-source maintainers, working only on agentic systems.
Production work on applied AI that started well before agents had a category name.
Business-model change takes three shapes in the mid-market. The first workshop works out which one is available to you, with an ROI case on each and a prioritised roadmap behind it.
What your specialists deliver by hand today becomes something a customer configures and runs. Delivery cost stops scaling with headcount, and the margin structure moves with it.
Accounts too small to serve profitably by hand come into reach once the judgement sits in the system. The same offering, sold into a market that was closed to you.
When a system you own executes the work, you can price the result it produces. The customer buys an outcome and the efficiency stays on your side of the deal.
One call on whether the judgement inside that work can be carried by a system your customers would pay for.
Once your name is on the offering, the engineering underneath stops being an internal matter. The tooling our engineers maintain on github.com/vstorm-co — guardrails, sandboxes, orchestration and context tooling — came out of systems that had to hold up in front of paying customers.
Thirty minutes with the engineers who would build it. You leave with a feasibility read and a shape for the work, whether or not you hire us.