Use case
Manufacturing & supply chain
The systems that matter are on-premises and are not moving.
The useful data is in an MES, a historian, an ERP instance customised over fifteen years, and a spreadsheet that one planner maintains and everyone depends on. Some of it is behind an air gap for good reasons.
Cloud-first AI tooling assumes none of this. The result is pilots that work in a lab and never reach the plant floor.
01 — Where it stalls
What actually slows this down
Connectivity is genuinely limited
Sites are segmented, bandwidth is constrained, and some networks do not reach the internet at all.
Every site is a variant
The same nominal system is configured differently at each plant, so an integration that works at one is a starting point at the next.
Downtime has a price per minute
Nothing gets tried against production. Anything that touches the line is proven elsewhere first, in full.
02 — What Orfyn takes on
The work that stops being manual
- 01
Run the daemon on a machine inside the plant network, with no dependency on outbound cloud access for the platform itself
- 02
Stand up an isolated environment per site variant, each with its own allocated service ports
- 03
Capture per-site differences as configuration rather than as forked code
- 04
Schedule recurring extraction and reconciliation jobs on cron-style triggers
- 05
Review every change as a diff before it reaches an environment that can stop a line
03 — The deciding constraint
It has to run where the systems are
The daemon runs on your hardware — including a server sitting inside a segmented plant network. Clients connect to it locally or over a relay, so an engineer can supervise from a desk without the systems themselves being exposed.
Walk us through your version of this.
Bring one workflow you are trying to ship. We will go through what it touches, where it stalls today, and what running it on Orfyn would look like.