Case study · AI Operations
VstreamX Platform
Studio Operations Platform + AI Agent HQ
The brief
The VstreamX platform is our own operating system — and the clearest proof of how we build. One codebase serves this marketing site, a client studio portal, and an admin console covering the full back-office: sales pipeline, projects with risk registers and ROI forecasting, finance, operations, messaging, documents, and support. On top sits Agent HQ: a supervised team of AI agents (customer service, billing, sales, onboarding) that drafts work product, retrieves knowledge through an agentic retrieval loop over a live Google Drive corpus, and connects to our product portfolio through scoped per-app connectors. Every deliverable an agent produces is archived in a work library, retrievable per client and project, and clients see the same project ROI and risk analysis our staff do — transparency as a feature. Every consequential action waits for a human click, AI spend is capped by fail-closed budget ceilings, and every outbound action lands in an audit log.
The problem
Most "AI agents" for the back office are autonomous by default — which means they can send the wrong email, quote the wrong number, or spend without a ceiling, and you find out afterward.
Our approach
We run our own operations on the opposite model: supervised agents that draft sales, billing, onboarding, and support work, gate every outbound action behind a human click, meter spend against a fail-closed daily ceiling, and write every action to an immutable audit log — with a one-click kill switch over the whole fleet.
The outcome
VstreamX is the proof: the same platform we sell runs our sales-to-delivery-to-support loop, with grounded agent drafting, per-app connectors, and human-gated approvals — so the AI-agents story on this site is a system we operate, not a demo.
What we built
Tech stack
Key metrics
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