AI Deployment Pod
Your pilot impressed everyone. It never shipped. We fix that.
The model isn't the problem. The gap is everything around it: your data, your legacy systems, your security reviews, your customers' messy workflows. That gap is engineering work that has to happen on the ground.
An AI Deployment Pod is a Forward Deployed Engineer and a Deployment Strategist embedded in your company. We connect your pilot to your real data and systems, and put it into production with a metric we agree on day one.
Two people, one outcome
The Forward Deployed Engineer
Writes the production code inside your systems: integrations, agents, evals, guardrails. Lives in your Slack and standups.
The Deployment Strategist
Owns the metric, the scope and the people around the deployment: stakeholders, security review, hand-off. So the FDE ships.
What you get
One AI workflow in production in 6–8 weeks.
A defined outcome, not billable hours.
Security, guardrails and human-in-the-loop by design.
Built in your infrastructure, on your stack.
Hand-off with runbooks, or we keep running it.
How it runs
Discover (week 1)
We map the workflow, the data and the systems, and define what "in production" means with a metric you sign off on.
Embed (weeks 1–2)
Your FDE joins your Slack, your standups and your customer calls. Same timezone, same channels as your team.
Ship (weeks 2–8)
Integrations, agents, evals and guardrails, built and deployed in your infrastructure. Human-in-the-loop by design.
Hand off
Documentation, runbooks and training, so your team owns it. Or we keep running it.
Who this is for
US mid-market companies in fintech, healthtech and SaaS with an AI pilot that works in the demo and has not reached production.
Questions we get
- What counts as "in production"?
- Whatever we define together in week 1, with a metric. Not a demo, not a staging environment: the workflow running for real users on your systems.
- Do we need to change our stack?
- No. The pod builds and deploys in your infrastructure. Python, TypeScript and Java, GCP and AWS.
- What happens when the pod leaves?
- Hand off: documentation, runbooks and training so your team owns it. If you prefer, we keep running it.
Have an AI pilot stuck before production? Book a 30-min Deployment Call.