The Vitzi Deployment Method
From AI pilot to production, in four phases.
Every engagement runs the same way, whether it is an AI Deployment Pod, a two-week Deployment Sprint or a white-label FDE at your customers. Four phases, one metric, one owner for the outcome.
The four phases
Discover (week 1)
We map the workflow, the data and the systems. We define what "in production" means with a metric you sign off on. What closes it: the metric and the scope, in writing.
Embed (weeks 1–2)
Your FDE joins your Slack, standups and customer calls, with access to your repo and environments. What closes it: the FDE is shipping in your codebase.
Ship (weeks 2–8)
Integrations, agents, evals and guardrails are built and deployed in your infrastructure, with human-in-the-loop by design. What closes it: the metric, met, on real users.
Hand off
Documentation, runbooks and training, so your team owns it. Or we keep running it. What closes it: your team runs it without us, or a run agreement.
Three rules we don't break
Embedded, not outsourced
Our FDEs live in your Slack, standups and customer calls, on US hours.
Production, not pilots
We answer for the working system, not billable hours.
Builders, not resellers
We run AI agents in production ourselves, every day, on our own Yatendi platform.
Capabilities
What our Forward Deployed Engineers bring into your infrastructure, and what each one means in a real deployment.
LLM integration: your model, your provider, wired into your systems with the right context and the right fallbacks.
RAG: your documents and data reachable by the model, with the retrieval quality measured, not assumed.
Evals: a test set from your real cases, so every change is measured before it ships.
Agent orchestration: multi-step workflows that call your tools and APIs, with limits on what they can do.
Guardrails and human-in-the-loop: the system knows when to stop and hand off to a person.
Python, TypeScript and Java, on GCP and AWS.
Want to see the method on your workflow? Start with a two-week Deployment Sprint.