The order does not stall on the floor. It stalls between systems that are each sure it already moved.
The status that lives in three systems and agrees in none of them. The certificate re-attached by hand for the fifth time. The change request rebuilt from an email chain. The weekly pull across plants into one deck. Below is what that work looks like today, what it looks like once Silow has mapped it, and what it gives back.
How the work looks today. How it looks after.
Seven processes, in the order we would take them. The left-hand column is not a caricature of a plant office — it is the shape of work two recorded teams were observed doing. Where a row is tagged Measured, the number came out of that recorded work, not out of a benchmark.
Sales, planning and the floor each hold a piece of the truth about one order, and none of the three systems agrees with the other two. Work that needs a second person or desk to move it on — a confirmation, a release, a re-plan — waited a median of about seven days in the teams we recorded. Work one person could finish alone waited about one. The order is not stuck on the floor. It is stuck on the desk between two systems that each think it already moved.
Every crossing gets a shape: what the last system said, what "done" means, who is waiting and since when. One status, reconciled from the systems you already run, not a new one to enter by hand.
Who owes what on this order. Who is blocked on which release. What has gone quiet since the last shift change. The chase is performed by phone, radio, messenger and walking over, by someone holding several orders in their head at once, and none of it leaves a trace anywhere.
The chase becomes a queue with a shape: what is waiting, on whom, since when, and what would unblock it. Standard follow-ups draft themselves; a human confirms anything that reaches another desk or another shift.
In a recorded team, one document was manually located and re-attached across two messengers on five separate occasions in three weeks, because no shared link for it existed. In a plant office that document is a cert of conformance, a material spec, an inspection report, a supplier COA — and the request for it is retyped from scratch every time by someone who knows exactly what it will say.
Standard requests draft themselves from the language your team already uses, with the right attachment already attached. The human reviews and sends. The file has one retrievable place, so it is never hunted for again.
A change request arrives as an email, a redlined drawing, or a phone call, and someone rebuilds the packet from scratch: what changed, who signed off, which orders it touches, what needs re-quoting. The packet that made the last change possible exists somewhere, as an attachment in a thread nobody can find again.
The packet assembles from what already exists — the prior version, the affected orders, the sign-offs on record — with the gaps listed. What reaches the engineer is a filled draft with the open items flagged, not a blank page and a memory of the last one.
OTIF, order status, backlog, scrap and rework — pulled from the ERP, the MES and a shift report into one sheet into one deck, on a cadence, by hand, by someone senior enough to know which number looks wrong.
The recurring pull-assemble-format cycle is drafted from the systems that already hold the numbers. The judgement about what the numbers mean, and what to do about them, stays where it was.
A task is moving about 3% of the time it exists in the teams we recorded — the other ~97% it sits in someone's queue. In a plant office, that queue is a release, a deviation sign-off or a credit hold, and it is where your ship date actually lives, invisible to every system you own.
The queue becomes visible: what is waiting, on whom, since when, and what would unblock it. You are not making anyone faster. You are attacking the ~97%.
Counting AI by application name undercounted every person in a team we recorded, and one of them by around seven times — their assistant ran inside another tool, so the window reported the host's name. If you bought licences and your dashboard says nobody uses them, the dashboard is the thing most likely to be wrong.
Adoption measured from the work itself rather than from seats and logins — and, more usefully, the reason. An assistant nobody opens is almost never a model problem. It is that nobody mapped it onto a procedure anyone actually performs.
The teams behind these rows: a distributed team of under ten people over five weeks in document-heavy client operations, and an 8-person cross-border operations team. Both anonymized. Rows tagged Measured carry a number computed from that real recorded work; rows tagged Observed in the trace come from the same captures without a stand-alone figure attached. Every figure was computed before a model was allowed to write a sentence about it, then each sentence was checked back against the figure.
What an agent may touch, and what it may not.
A plant office runs on decisions with a shipment, a quality result or a production plan on the other side of them. So we draw this line before anything is built.
- Assembling order-to-ship status from the systems that already hold it, reconciled into one view.
- Drafting the document or certification request sent for the fortieth time this month.
- Watching the queue: which order, release or deviation has been waiting, on whom, for how long.
- Pre-filling the change-request packet from the prior version and the orders it touches.
- The reporting ritual: the recurring pull from the ERP, the MES and a shift report into one deck.
- Producing the record of what moved, when and in what order — as a by-product, not a task.
- Every production plan and schedule decision. Silow never re-plans or re-sequences the floor.
- Every shipment release. Silow never releases an order; it drafts the packet that supports the release.
- Every quality decision. Silow never decides pass or fail, disposition, or a deviation sign-off.
- The exception nobody has seen before — which is most of what you hired a coordinator for.
- Every draft an agent produces is reviewed before it is sent. Nothing auto-sends to a customer or a supplier.
- Any decision about an employee. No ranking, no scoring, no automated judgement about a person.
Silow never changes a production plan, never releases a shipment, and never decides a quality pass or fail. It assembles status and drafts the chase around those decisions and leaves the decision — and the accountable human who makes it — exactly where it was.
Time, money, and the people you already have.
The rule for this section: not one invented percentage. Every figure is either measured in a recording or supplied by you — hours multiplied by your own loaded cost. A saving you cannot defend to a plant manager is not a saving, it is a slide.
No industry benchmark. Every candidate above is scored in hours against your recording and ranked by payback, so the first thing you build is the one that gives back fastest rather than the one that demos best. The anchors we can already point at, from real captures: a task moving about 3% of the time it exists, a median wait of around seven days each time work crosses a desk, and one document re-found and re-attached five times in three weeks.
The first is hours × your loaded cost — your rate, not a benchmark we invented. The second is the one nobody counts: the AI you already pay for and nobody opens. In a team we recorded, app-name telemetry undercounted AI use for every single person, so the licence decision was being made against a number that was simply wrong.
No new system to adopt, no data to move, no process change while we look — Silow runs on the ERP, MES and messengers the plant office already uses, inside the perimeter you choose. The capacity comes back into the same team, doing the same job with the assembly work removed.
A task is moving about 3% of the time it exists. Making the coordinator faster optimises that 3%. Your ship date lives in the other 97%.
From a status nobody trusts to a layer you own.
The agents are the visible part. The thing that makes them possible — and that keeps paying after they ship — is the record of how the plant office actually works.
It is written down somewhere, in three places, and they disagree. The real procedure — the one performed at the shift-change meeting when the order is late — has never been observed, which is why every automation attempt starts with a workshop and a guess.
The real steps, from the recording, with the evidence attached. In one of our own recordings all thirty supporting quotes were checked back against the raw scenes they came from, not against a summary of them. Ranked by impact, effort, risk and payback.
Order-to-ship status, the document chase, the change-request draft. Each arrives as a specification an engineer can build from: the observed steps, the evidence, and the lines it must not cross. Human review stays in the loop by design.
The agents are consumers of something more valuable: a private, structured record of how your plant office actually works, living inside your perimeter. Onboarding, search, handover, and whatever you build next, on data only you own.
The office work nobody has a line item for.
These six categories are not our taxonomy — they came out of the report on an operations team we recorded, ranked from what that team was observed doing all day. They are not manufacturing specific, which is rather the point: this is where the day goes in any coordination business.
The recurring pull-assemble-format cycle across the ERP, the MES and a sheet.
The chase: who owes what, who is blocked, what has gone quiet since the last shift.
Order, change-request and supplier cases assembled into one reviewable object.
The pre-read and the post-meeting actions, drafted from what was actually said.
The certification and change-request packs, rebuilt from scratch for every new order.
Onboarding, access requests, and the tool blockers that stall a whole shift.
Where AI actually works in manufacturing operations — and where it stalls.
Everyone selling AI into manufacturing has a case study with a number in it. We are not going to quote you someone else's, because we cannot verify it. Here are the patterns instead, including the failures.
The part of the plant that already runs on models and optimisers, and has for years. It is also the part your engineers own, it is measured, and it is not where your coordinators spend their day. The money left on the table is upstream of it.
The most valuable repeated object in the plant office and the least industrialised. Every change is treated as new because the last one was never written down in a form anything could reuse. Retrieval, not generation, is the differentiator.
The same order crosses a plant, a shift and sometimes a site, and each boundary is a fresh status conversation. It stalls when the automation is built against one system instead of against the desk where the handoff actually happens.
High volume, low judgement, document-shaped — which is exactly the profile that pays back in the same quarter. It stalls when the request is retyped from scratch instead of drafted from what your team already sends.
Sales asking planning, planning asking the floor, the floor asking sales again — coordination performed by phone and by walking over, with no trace left behind for the next person.
The highest-frequency, lowest-judgement work in a plant office, and the one most companies believe they solved with a brittle sheet. It looks solved. It is a senior person's recurring half-day, every week, on every line.
Pilots do not die because the models are bad. They die because nobody could say which of forty candidate processes to do first, or what "correct" looked like when it was done. That ranking is what Silow produces; the agents are what you build on top of it.
Nobody automates the waiting. It is not a tool category, so no vendor sells it — yet on the teams we recorded, roughly two thirds of the elapsed time sat in a queue between two people, and a task was moving about 3% of the time it existed. It is the largest number on this page and the cheapest one to move.
The part other vendors leave out.
Two of the things we most wanted to be true were killed by our own data. The vendor who tells you what their product cannot do is the one worth the second meeting.
The recording sees activity, never outcome. Whether the order shipped on time, whether the change closed — that is not in the data. Join one column of your outcomes to our trace and it becomes provable. Until then, any ROI figure quoted at you by anyone is a guess.
A duplicate-work detector: about 44 recoverable minutes across nine people over five weeks, so there is no product. A meeting-ROI report: three quarters of what the metric called "meeting time" had no room attached. Both were cut before they ever reached a customer.
Numbers are computed before the model is allowed to write a sentence about them, so no figure gets invented. But a true number can still be attached to the wrong claim. We have caught exactly that twice — both times with human eyes, not with a check.
Weeks, not quarters.
Before any recording, we agree where it runs — including fully air-gapped, on-prem or inside your own VPC. No keystroke logging, no microphone, no message content, no scoring of anyone. Those are product constraints, not policy promises.
Silow runs on the plant office's existing machines and tools, and on the floor terminals alongside them. No migration, no new system, no process change while we look — the point is to see the work as it actually is.
Scenes become tasks; tasks become the real procedure. Every opportunity scored by impact, effort, risk and payback against your own hours — so the first thing you build is the one that gives back fastest rather than the one that demos best.
A specification, not a suggestion: the observed procedure, the evidence, the scenes behind it, and the boundaries it must not cross.
What you do with the capacity is yours.
If the honest answer for your plant turns out to be "there is not enough here to be worth it", we would rather find that in a two-week recording than sell you a year of it. Book a call and we will show you what the map looks like on your own systems, and what it would rank first.