A buyer that tells you what's running low — and where to buy it
Companies lose hours hunting the internet for what to order and who to order it from — then reorder by gut and find out too late that a supplier was unreliable. That costs real money: stock-outs that lose sales, dead stock that freezes cash, and margin left on the table for want of a price check. It concentrates in three concrete problems. A buyer changes that — it learns what you regularly buy, watches your stock, tells you what's running low and where to buy it best. It never places the order itself: it proposes, you decide, and your feedback makes it sharper.
The problem we see
Buying in a smaller company is rarely a real process. It's someone remembering, someone googling, and someone hoping the usual supplier still has it at a decent price — with the scattered long tail of small purchases nobody controls.
That long tail is bigger than it looks: by some estimates, small unmanaged purchases are only about 20% of spend but touch ~80% of suppliers — a tail of buying that quietly eats time and margin. And stock-outs aren't free: the best-run operations lose around 2% of sales to them, while strugglers lose 11–16%.
Three real problems this solves — and who has them
1. Reordering by gut, noticing too late — the stock-out
Who has it: a 30-person plastics-component manufacturer where the shift lead reorders regranulate and a few critical additives by eyeballing the racks — and twice this quarter a line idled half a day waiting on a resin that "looked like there was plenty".
Retail out-of-stock sits stubbornly around 8% (8.6% in Europe) and costs roughly 4% of sales, and when an item is out about 31% of shoppers go to another store (Corsten & Gruen). The SMB's own ERP often only carries a static reorder point someone typed in once and never revised — so it either nags constantly or stays silent while real consumption has drifted.
How we solve it: the buyer learns this company's real consumption pace from past orders and invoices, projects when each item crosses its lead-time horizon, and flags what's running low with a suggested quantity before the stock-out — a moving trigger driven by your usage, not a number from 2022. The human still presses buy.
2. Dead stock and over-ordering quietly freezing cash
Who has it: a family electrical wholesaler with €4M of stock, roughly a third of which never turns — and the owner can't get a loan released because the cash is sitting on shelves as fittings nobody's bought in 14 months.
Overcorrecting for stock-outs, buyers over-order "to be safe", and capital freezes in slow movers. Carrying cost runs 20–30% of inventory value a year (NetSuite), and cutting inventory just 10–15% on a €10M book frees €1–1.5M in working capital.
How we solve it: because the buyer scores items by actual consumption, it suggests right-sized quantities instead of padded safety buffers and surfaces the slow and dead movers it would stop reordering — shrinking the excess tier directly, with the human keeping the call on liquidation.
3. Hours googling suppliers, buying from whoever's easiest, reliability stuck in one head
Who has it: a 25-person electrical contractor whose stores person spends a morning a week tracking down a non-stock breaker and a cable run — calling three wholesalers, and still occasionally ordering the wrong rating.
37% of procurement teams want to spend less time sourcing simple purchases, and the long tail is where margin leaks: the ~20% of spend spread across ~80% of suppliers gets no price scrutiny, yet actively managing it yields around 7% savings (Hackett Group). Worse, supplier reliability — who's on time, who slips, who sent the wrong item last spring — lives only in the buyer's head and walks out when they're sick or leave.
How we solve it: for each item the buyer searches and scores suppliers on price, availability and reliability — from your own order history — and proposes a "what to reorder and from whom" shortlist in seconds. Every outcome you report (late delivery, wrong item, good price) feeds a reliability score, turning tribal knowledge into durable data. It proposes; you buy.
These are widely-cited industry benchmarks, linked to their sources above. The real numbers for your stock and orders come out of the free diagnostic.
The idea: a buyer that proposes — you decide
All three problems share one root: nobody is watching your real consumption and supplier history, so buying happens from memory and a search engine. The buyer does that watching for you.
The goal isn't to hand purchasing over to a bot. It's a controlled loop where a buyer does the watching and the legwork, and a human stays firmly in control of every order.
It does not buy anything on its own. It prepares the decision — what's missing and where to get it — and the responsible person places the order.
How it works
- Learn what you buy — the buyer picks up your recurring purchases from past orders, invoices and your product or material list.
- Watch the stock — connected to your inventory, it tracks levels and the pace you consume things, so it knows when something is about to run out.
- Flag what's running low — instead of a gut check, you get a clear "about to run out" list with suggested quantities, before it becomes a stock-out.
- Find & score suppliers — for each item it searches options, compares price and availability, and rates suppliers on reliability — delivery time, price stability, past experience.
- You decide & buy — you review the shortlist, pick, and place the order. Nothing is ordered without you.
- Feedback — you tell it how it went (supplier delivered late, wrong item, great price). That feedback tunes the next recommendation.
Where exactly AI helps — and where it stops
AI is useful here because buying is messy and repetitive at the same time. It helps with:
- recognising your recurring purchases and predicting when you'll need them again,
- spotting what's about to run out before a person would,
- searching and comparing suppliers across the web and your own history,
- scoring supplier reliability from delivery, price and past orders,
- drafting the "what to reorder and from whom" list.
But the line is deliberate: AI proposes, the human buys. It doesn't spend your money, sign off on a supplier or commit an order. That stays a human decision — which also means no runaway bot ordering the wrong thing.
Two jobs, one loop
The same buyer covers two needs, depending on the company:
| If you hold stock | If you buy to order |
|---|---|
| It watches inventory and tells you what's running low and when to reorder, so you avoid stock-outs and dead capital. | It tells you where to buy it best — finding and rating reliable suppliers for what you need, instead of hours of googling. |
Most companies want a bit of both — and the loop handles both from the same picture of what you buy.
How it gets sharper
Every order teaches it something, because you close the loop with feedback. Over time:
- reorder timing fits your real consumption, not a flat rule,
- supplier scores reflect who actually delivered well for you,
- the long tail of scattered buying gets visible and controllable,
- recommendations stop being generic and start matching how you really buy.
What we measure
A buyer loop should be measured from the first version.
| What we measure | Why it matters |
|---|---|
| Time spent sourcing | Hours of searching replaced by a ready shortlist |
| Stock-outs avoided | The core proof — fewer "we ran out" moments |
| Supplier reliability | Late deliveries and wrong items trending down |
| Reorder accuracy | Suggested quantities matching real need |
| Scattered (maverick) spend | The long tail becoming visible and controlled |
| Price captured | Buying from the best option, not the easiest |
Why it works better than googling and gut reorders
A search engine can find a supplier. It can't watch your stock, remember who let you down, or learn what you reorder. A controlled buyer loop gives you:
- one "what's running low" list instead of noticing too late,
- rated suppliers instead of whoever came up first,
- buying decisions backed by your own history,
- the scattered long tail under control,
- a human firmly in charge of every order.
You don't need a procurement department or an enterprise system to start. One category of what you buy, your stock data and your order history is enough to show whether the loop is worth scaling.
Who it makes sense for
This makes sense for companies that buy the same kinds of things again and again, especially with a warehouse. Particularly if:
- you hold stock and stock-outs (or dead stock) hurt,
- people spend real time hunting the internet for suppliers,
- reordering happens by gut and gets noticed too late,
- supplier reliability isn't tracked anywhere,
- lots of small purchases happen with no real overview.
The point isn't to take buying out of human hands. It's to do the watching and the legwork — what's running low, where to buy it, who's reliable — so the person who decides can decide fast and well.
The payback is concrete: fewer stock-outs that cost you sales, less cash frozen in dead stock, and the scattered long tail bought from the best option instead of the easiest — with a human firmly in charge of every order. One category of what you buy is enough to prove it.
The buyer is one shape of an AI agent among several — see AI agents for business: what they actually do and where to start, and our AI solutions overview shows where it fits.
Want to see it on your own stock and orders? Get a free diagnostic — we map one category of what you buy and show you exactly what we'd build, the impact and the cost. No obligation.