Stock records are accurate on the day somebody counts them, and they drift every day after that. Buying then happens on memory, and the cost lands twice: capital stuck in lines nobody wants, and sales lost on the ones that moved.
Retail AI inventory automation means the stock record updates itself from what actually arrives and what actually sells. A vision model reads a delivery note or a box rather than somebody keying it in. Forecasting then runs against your own sales history, which is what turns a reorder point into a date somebody can act on.
Retail runs on a number that was true once. Between counts the system and the shelf move apart, and the ordering happens on whatever the buyer remembers. Both cost money. Only one is visible.
A cycle count is accurate on the day. Every day after, the record and the shelf drift apart.
Whoever knows which lines move is going on experience. That knowledge walks out at five o'clock.
Capital sitting in slow lines is invisible until a valuation. By then the money went months ago.
A delivery arrives and somebody photographs it. Codes, quantities and serial strings come off it as rows.
Selling rate is watched against supplier lead time. When those cross, the gap is coming.
Holding cost and falling value are tracked per line. Slow lines stop being a year end surprise.
Four steps. A person still sits at exactly one of them, and it is the step that decides whether the other three are worth having.
Somebody snaps the note or the box at goods inwards.
A vision model lifts the codes, counts and serials.
Lines are checked against the order that is pending.
A person looks at the shortfalls and nothing else.
Human checkpointThe same reading and forecasting turns up in jobs that look unrelated until you notice they are all somebody retyping or somebody guessing.
Stock records carry your margins and your supplier pricing, which makes where they go a question worth settling before anything is built.
Integrations run against business tier endpoints with retention turned off, so what you send never enters a training set. Somebody has to switch that on, and we check it.
The pipeline queries your stock system through its API. Nothing is migrated to make room.
A line in the system points back to its photograph. A disputed figure gets looked at rather than argued about, which is a different conversation.
These are the systems stockrooms bring us, and your own does not have to be on the list. Nothing here is a requirement.
Not to start. A phone camera and a vision model read codes off a box well enough to prove whether the approach works on your stock. Dedicated scanners are faster at volume, and that is a decision worth making once you have seen it run.
If it has an API, the pipeline reads and writes straight through it without any export step. Where there is none, we look at what it exports. Replacing a stock system to get better forecasting is a large project solving the wrong problem.
As far ahead as your supplier lead time, which is the number that actually matters. A warning after the reorder window closes is not one. What it needs is sales history and a lead time per supplier rather than one figure for everything.
Better than people expect. Lead time matters more than volume. What forecasting needs is history rather than scale, and a few years of consistent sales on a few hundred lines is usually enough.
Printed codes, quantities and serial strings, lifted off a delivery note or straight off the packaging. What it is poor at is anything it has no examples of, and that gets flagged for a person rather than guessed.
A reorder point is a number somebody set once and it stays where it was put. Forecasting recalculates as the selling rate changes, so a line that suddenly moves faster is caught before the shelf empties.
Data entry, answering the same tickets, chasing numbers between systems. We automate the parts that repeat. Your team keeps the parts that need judgement.
