The drop is not late because of the factory. It is late because it crossed a desk.
The listing typed out attribute by attribute, per market. The creative rebuilt at eleven sizes. The Monday pull from the ad platforms into the sheet. The supplier thread nobody outside it can read. Below is what that work looks like today, what it looks like once Silow has mapped it, and what it gives back — in hours, in money, and in capacity you already pay for.
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 brand — 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.
A new SKU becomes an afternoon: the title, the bullets, the long description, the attribute table, the size chart, the images cropped for each surface — then the whole thing again for the second market, and the third, with the units and the compliance line changed by hand. Someone who knows the product spends their day retyping the product.
The listing assembles from what already exists: the spec sheet, the supplier data, the copy pattern your best-converting pages already use, the crops your team actually makes. Per-market variants generate from the master. A human approves the batch instead of building each one.
One approved key visual becomes dozens of near-identical children by hand — every placement, every aspect ratio, every locale, every hook line, every marketplace’s banner spec. The fiftieth export gets the same care as the first, which is to say less, and it lands on the day the campaign was supposed to go live.
The variant matrix is generated from the approved master and your brand rules, with the steps your designer actually performs — which crop, which safe area, which copy slot — read out of the recording and turned into the agent’s specification. It looks like your work, not like a template.
Monday: pull from the ad platforms, pull from the store analytics, paste into the sheet, fix the sheet, rebuild the deck, notice the ROAS line that looks wrong, chase it. Fixed sources, fixed shape, fixed output — done by hand, every week, by whoever is senior enough to know what the numbers mean.
The pull, the assembly and the format run themselves. What lands on the human’s desk is the part that needs judgement: what moved, what broke, what is worth spending on. This is the most repeated ritual we see in a commercial team and the cleanest agent specification on this page.
In the cross-border operations team we recorded, nearly a quarter of the team’s screen time was a messenger — more than every internal system combined. In the go-to-market team we recorded it reached 66%. The purchase order, the revised ship date, the customs exception, the promise about the pallet: all of it lives in threads, and none of it lives anywhere auditable, searchable or reusable by the next person.
What was promised, by whom, when, and what has gone quiet becomes a by-product of the conversation rather than a thing someone has to remember to log. The thread stays where your supplier wants it. The record exists anyway.
Every drop rebuilds the same pack from an empty page: the landing copy, the email, the paid set, the marketplace assets, the retail one-pager, the discount matrix. The last launch that worked is somewhere in the drive, and it is faster to start again than to find it.
The pack starts pre-filled from the launch that actually performed, with assets attached rather than hunted for. Launch stops being a build and starts being a review — which is the only way a brand shipping a drop a month gets its calendar back.
Where is my order. I want to return this. It arrived damaged. Each one is a person opening the order in one window, the carrier in another, the 3PL thread in a third, then writing a reply they have written four hundred times — and the answer, and the goodwill credit, exists only in that thread.
The context assembles before the human arrives: the order, the tracking state, the prior contact, the policy that applies, and a draft in the language your team already uses. The human decides and sends. Nothing auto-sends to a customer.
The listing waits on the photography. The photography waits on the sample. The paid set waits on a brand review. In the go-to-market team we recorded, work that needs a second human waits a median of 7 days; work one person can finish alone waits 1.1. Two thirds of all waiting sits on the far side of a handoff — and it is not time zones, because the least-overlapping pair still shares a quarter of the working day. The drop that slips is usually waiting on a person, not on a task.
Every handoff gets a shape: what is attached, what "done" means, who is waiting and since when. Nothing sits silently. This is the largest single block of recoverable elapsed time we have found anywhere, and the cheapest one to change.
The teams behind these rows: a 9-person go-to-market team, 5 weeks, ~325 hours recorded, 748 tasks, ~88,000 scenes; and an 8-person cross-border operations team, 8 days, ~95 hours, 461 tasks across 44 work directions. 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.
Time, money, and the people you already have.
The rule for this section: not one invented percentage. Every figure below is either measured in a recording or supplied by you — hours × your loaded cost. We will not quote you a conversion lift or a competitor’s case study, because we cannot verify either.
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 median flow efficiency of 3.4% across 105 reconstructed tasks. A median seven-day wait every time work crosses a desk. One file re-found and re-attached five times in three weeks. 190 minutes over three weeks spent retyping live threads into a system of record, by one person, on one ritual.
The first is hours × your loaded cost — your rate, not a number we invented. The second is the one nobody puts on a slide: the AI you already pay for and nobody opens. In the operations team we recorded, the company had built its own internal assistant and recommended it to the team. Recorded usage across eight people: seven at zero percent, one at one. That licence is being paid this month.
No new system to adopt, no catalogue to migrate, no process change while we look — Silow runs on the tools the team already uses. The capacity comes back into the same team, doing the same job with the assembly work removed: more SKUs live, more markets open, more drops shipped on the date they were promised. What you do with it is a decision for the people running the brand.
A task is moving 3.4% of the time it exists. The other 96% it waits. Making the merchandiser faster optimises the 3.4%; the slipped drop lives in the rest.
From a listing calendar 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 operation actually works.
There is a launch checklist somewhere and it is out of date. The real procedure — the one performed at 09:00 the week before a drop — has never been observed, which is why every automation attempt starts with a workshop and a guess.
The actual steps, from the recording, with the evidence attached. In one team we recorded, all thirty supporting quotes were verified against the raw scenes they came from, not against a summary — the procedure comes from observation, not from a model’s imagination.
The top of the ranking ships: the listing assembly, the variant matrix, the weekly pull, the handoff contract. Each arrives as a specification an engineer can build from — the observed steps, the evidence, and the lines it must not cross.
The agents are consumers of something more valuable: a private, structured record of how your brand actually operates. Onboarding that teaches from it, search that answers from it, and whatever you build next — on data only you own.
Where AI actually works in retail operations — and where it stalls.
Everyone selling AI to e-commerce has a case study with a conversion 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 most reliable payback in the category and the least glamorous. Descriptions, attributes, crops, per-market variants — high volume, low judgement, verifiable output. It pays where a structured source exists to generate from, and produces landfill where it does not.
One SKU into six markets and four surfaces, each with its own spec, unit and compliance line. Mechanical, repetitive, high-volume — which is exactly why it is one of the few places the payback shows up in the same quarter you started.
The highest-frequency, lowest-judgement work in the commercial team, and the one most brands believe they already solved with a brittle sheet. It looks solved. It is a senior person’s recurring half-day, every week, forever.
Where it works: sequences whose content is derived from what already converts for you. Where it stalls: sequences generated from nothing, which read as generated and get unsubscribed from as generated. The asset is your team’s real language, and almost nobody has captured it.
The least automated part of the whole operation, because it does not live in a system — it lives in threads. That is a data problem long before it is a model problem, and no tool category exists to sell you the fix.
Deflection is the pitch; assembly is the win. Pulling the order, the tracking state, the prior contact and the applicable policy into one place before the human replies is dull, safe, and worth more than a bot that answers wrongly at speed.
Pilots do not die because the models are bad. They die because nobody could say which of forty candidate processes to start with, or what "correct" looked like when it was done. That ranking is the thing 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 two thirds of all waiting in the work we recorded sat in a queue between two people, and a task was moving 3.4% of the time it existed. It is the largest number we have 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. You should hear that from us here rather than discover it in month three.
The recording sees activity, never outcome. Whether the drop sold, whether the campaign converted — that is not in the data. Join one column of your outcomes to our trace and it becomes provable. Until then, anyone quoting you an ROI figure is guessing, including us.
The pitch was "N people doing the same thing, here is the saving." The real answer across nine people over five weeks was 44 recoverable minutes — 0.22% of working time, with a median duplicate of fifteen seconds. There is no product there, so we do not sell one. The meeting-ROI report went the same way: three quarters of what the metric called "meeting time" had no room attached.
In the operations team we recorded, capture ran about two hours per person per working day. The report says so on its own coverage page instead of quietly extrapolating to a full week. If a number rests on thin data, you will see that in the report itself.
Weeks, not quarters.
Silow runs on the team’s existing machines and tools. No migration, no new system, no catalogue to move, 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. Numbers are computed before a model is allowed to write a sentence about them, and every quote is checked back against the raw scene it came from.
Every candidate scored by impact, effort, risk and payback against your real hours — including your AI-adoption number measured from the work rather than from licence seats, which measure the shape of your tools and not the shape of your work.
A specification, not a suggestion: the observed procedure, the evidence, the scenes behind it, and the boundaries it must not cross. Nothing auto-sends to a customer or a supplier.
What you do with the capacity is yours.
Silow maps work, not workers. No productivity ranking, no performance evaluation, no automated decision about anyone’s job — not as a policy written afterwards, but because the unit of analysis is the process. Two weeks of recording, and the first ranked roadmap lands with the hours attached.