Nobody is replacing your agents. The queue is not waiting on them.
The same answer written for the fortieth time, from memory. The case history reassembled across four windows before a reply can be typed. The escalation that sits for a week because it crossed a desk. Below is what that work looks like today, what it looks like once Silow has mapped it, and — because this is support — exactly where an agent stops and a human takes over.
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 support desk — it is what two recorded teams were observed doing. Where a row carries a number we measured in real recorded work, the tag says “Measured”. Where it does not, the tag says that too.
The answer exists. It has been written forty times this month, correctly, by people who know exactly what it will say — and it is retyped from memory each time, because finding the last good version costs more than writing a new one. The fortieth reply gets the same care as the first, which is to say, less.
The draft arrives pre-written in the language your best agents already use, pulled from threads they actually sent — not invented by a model. A human reads it, edits it, and presses send. Nothing reaches a customer that a person has not signed off on.
Before a single word of the reply gets typed, someone opens the ticket, the messenger thread, the order record, the shared drive and the mail client, and reconstructs what has already happened to this customer. Pure assembly work, performed identically every time, by the person you hired for their judgement.
The case arrives assembled: what the customer said, where, when, what was promised, what was attached, what is still missing. The agent starts at the judgement instead of at the archaeology.
One PDF was manually located in Finder and re-attached across two messengers on five separate occasions in three weeks. No shared link for it existed. The support version of that is the answer you send constantly and have never once saved: the same paragraph, the same attachment, rebuilt by hand.
The recording surfaces the reply you keep re-writing and the file you keep re-finding, and turns them into the macro that should already have existed — drafted from the version your team actually sends, not from a template someone wrote in 2023 and nobody updated.
The escalation queue is where the SLA dies. Work that needs a second human waits a median of 7 days; work one person can finish alone waits 1.1 — 6.4 times longer. Two thirds of all waiting sits on the far side of a handoff, and it is not time zones: the least-overlapping pair still shares a quarter of the working day. The handoff is simply not defined anywhere.
Every escalation gets a shape: what is attached, what “done” means, who is waiting and since when. The queue becomes visible, which is most of the fix. This is the single largest block of recoverable elapsed time on the page and the cheapest one to change.
A task is moving 3.4% of the time it exists. The other 96% it is sitting in someone’s queue. Your handle time optimises the 3.4%. Your customer is experiencing the 96% — and so is your backlog.
The waiting becomes the thing you manage: what is queued, on whom, since when, and what would unblock it. We are not trying to make the agent type faster. We are attacking the part of the clock nobody has ever been able to see.
In the operations team we recorded the messenger was ~24% of screen time — and up to 66% in the go-to-market team we recorded. More than every internal system combined. The chase, the exception, the promise to the customer: all of it happens in threads, and none of it lands anywhere a manager or an auditor can follow.
What was said, when, in what order and on whose desk becomes a by-product of the work rather than a task someone has to remember at the end of the day. The record exists because the work happened, not because someone wrote it down afterwards.
One operator asked an AI assistant for a licensing framework’s name at 08:56, read the answer at 08:57, typed it into a messenger at 08:58 and sent it to the client at 09:01. Unreviewed, unlogged, straight to a customer. This is already happening in your support team. Nobody has ever seen it, because nobody was looking at the work — only at the licence seats. Measured by app telemetry the team we recorded used AI 11.7% of the time; measured from the actual work, 14.9%.
Adoption measured from the work itself, and — more usefully — the shadow path made visible: where a model’s output is reaching a customer without passing a review. You cannot govern what you cannot see, and today you cannot see it.
Every figure on this page comes from anonymized recorded work. The two teams behind the numbers are a 9-person go-to-market team — 5 weeks, ~325 hours, 748 tasks, ~88,000 scenes — and an 8-person cross-border operations team that handles partner, driver and courier support as part of its job — 8 days, ~95 hours, 461 tasks, 44 work directions. Rows tagged “Measured” carry numbers from that recorded work; rows tagged “Observed in the trace” describe shapes we saw without inventing a count. Every figure was computed before a model was allowed to write a sentence about it.
What an agent may touch, and what it may not.
In support this line is the product. “The AI auto-replied to a customer” is the nightmare, and it is a real one — so we draw the line explicitly, before anything is built, rather than discovering it in an incident review.
- Drafting the reply from what your best agents already answer — captured from real threads, never auto-sent.
- Assembling the case history: what was said, where, when, what was promised, what is missing.
- Surfacing the macro that should exist — the answer written forty times and saved zero.
- Routing: sending the case to whoever has actually solved this one before, not to whoever is free.
- Spotting the recurring question before it is asked, so the fix lands upstream of the queue.
- Producing the audit trail — what moved, when, on whose desk — as a by-product, not a task.
- Pressing send. Every draft is reviewed by a person; nothing an agent writes auto-sends to a customer.
- The angry customer, and the call where somebody has to be a human being about it.
- Refunds, goodwill, credits, and every commercial exception.
- The edge case nobody has seen before — which is precisely the one a model will answer confidently.
- Any decision about an employee. Silow maps work, not workers, and no report ranks a person.
Silow does not decide anything for a customer, and it does not decide anything about a person on your team. It removes the assembly work in front of the reply and leaves the reply — and the named human who sends it — exactly where your customer expects to find them.
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, your rate. A saving you cannot defend in a board meeting is not a saving, it is a slide.
We do not quote you an industry percentage, and we will not tell you what support desks “typically” save. Every candidate above is scored in hours against your recording, then ranked by payback — so the first thing you build is the one that gives back fastest, not the one that demos best. The anchors we can point at come from the teams we recorded: a task moving 3.4% of the time it exists, a median seven-day wait each time work crosses a desk, a document re-found and re-attached five times in three weeks.
The first is hours × your loaded cost — your rate, not a benchmark we made up. The second is the one nobody counts: the AI you already pay for and nobody opens. In the operations team we recorded, the company had built its own internal assistant — able to draft, query data and handle tickets end to end — and recommended it to the team. Recorded usage across eight people: seven at zero percent, one at one. That licence line is being paid every month right now.
No new helpdesk to adopt, no data to move, no process change while we look — Silow runs on the tools the team already uses. The capacity comes back inside the same team, doing the same job with the assembly work removed. We are not selling you a smaller support team. What you do with the released hours — depth, coverage, the backlog, the queue nobody gets to — is a decision for the people running the business.
A ticket is moving 3.4% of the time it exists. Handle time optimises that 3.4%. Your customer is waiting through the other 96%.
From a queue you cannot see 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 your support actually works.
You have handle time, CSAT and a backlog that will not go down. The waiting is invisible, the real answers live in your best agents’ sent folders, and the AI licence is reported from seats.
The real work, reconstructed from the recording: where the hours go, where the escalation queue sits, which questions recur, which answers already exist — ranked by impact, effort, risk and payback against your own numbers.
Draft replies, assembled case history, the handoff contract. Each arrives as a specification drawn from observed steps, not from a workshop guess — and each stops at the review, by design.
The agents are consumers of something more valuable: a private, structured record of how your support actually works. Onboarding that teaches a new agent from real cases. Search that answers from what was really said. Whatever you build next, on data only you own.
Where AI actually works in support — and where it blows up.
Everyone selling AI to support teams has a case study with a deflection rate 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 durable win in the market and the least exciting one. Suggested replies a human approves survive contact with reality. Fully autonomous replies survive until the first screenshot of a confidently wrong answer reaches social media.
Every deflection number in every vendor deck rests on a knowledge base that is current. Most are not. Where the answer is genuinely written down, models are very good. Where it lives in one senior agent’s head, generation produces fluent, confident landfill.
The highest-frequency, lowest-judgement work in the function. Teams believe they solved it with a macro list from two years ago that nobody maintains — so the good answer is retyped from memory instead.
Sending a case to the person who has actually solved that case before, rather than to whoever is next in the round-robin. Boring, mechanical, and one of the few places where the payback shows up in the same quarter.
Tier-1 to tier-2, support to engineering, agent to manager. Every one of them is the same object: work crossing a desk with nothing defining what “done” means. It is the least instrumented step in the whole function.
QA reads a handful of tickets a week and generalises from them. Reviewing more is a real use, and it is the one place where the ethics get sharp fast — read the boundary section above, because we will not score your agents.
Nobody automates the waiting. It is not a tool category, so no vendor sells it — yet two thirds of the elapsed time in our recordings sat in a queue between two people. It is the largest number on this page and the cheapest one to move.
Not 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.
The part other vendors leave out.
The most important line on this page is the first one, and it costs us the sale more often than anything else we write. You should hear all four from us here rather than discover them in month three.
The recording sees activity, never outcome. Whether the ticket actually resolved the customer’s problem is not in the data. Join one column of your outcomes to our trace and it becomes provable. Until then, anyone quoting you a deflection rate or a CSAT lift is guessing.
A duplicate-work detector: 44 recoverable minutes across nine people over five weeks — 0.22% of working time, median duplicate fifteen seconds. There is no product there, so we do not sell one. And a meeting-ROI report: three quarters of what the metric called “meeting time” had no room attached. Both were cut before they reached a customer.
Of the operations team we recorded about two hours per person per working day. The report says so on its own coverage page rather than quietly extrapolating to a full week. If a number rests on thin data, you will see that in the report itself — and a human reads every report, because a true number can still be attached to the wrong claim. We have caught exactly that twice, both times with eyes.
Weeks, not quarters.
Silow runs on the team’s existing machines and tools. No new helpdesk, no migration, no process change while we look. No keystrokes, no microphone, no scoring of anyone — and you choose the deployment mode, including fully air-gapped.
Scenes become tasks, tasks become the real procedure. Numbers are computed before a model writes a sentence about them, and every quote is checked back against the raw scene it came from — in one recording, all thirty of them.
Every candidate scored by impact, effort, risk and payback against your real hours — so the first thing you build is the one that pays back fastest, not the one that demos best.
Each opportunity arrives as a specification an engineer can build from: the observed procedure, the evidence, the scenes it came from, and the line it must not cross. Drafts go to a human. Nothing auto-sends to a customer.
We are not selling you a smaller support team.
Support is the function most often threatened with “AI will replace the agents”, and that is not what this is. Silow maps work, not workers: no productivity ranking, no performance evaluation, no automated decision about anyone — 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.