00 RetailFlow

Margin is lost when the store stops telling the truth.

RetailFlow is designed to rebuild store inventory truth from existing store data and verified counts — without requiring new cameras, RFID or shelf sensors — and to run a closed agentic loop aimed at margin lost to shelf out-of-stocks and store labor.

Request a proof-of-value briefing See the returns

Wondering about sensors, shrink, short deliveries or your existing systems? Read the short answers

This animation explains RetailFlow’s intended capability. It does not show a live store deployment or measured store results.

Scroll to move through the store

01 The deficit

The store cannot act on inventory it cannot locate.

Two leaks account for much of what is missing, and both begin with the same cause: the store's record of itself is wrong.

Out-of-stocksPerpetual inventory counts totals, not locations. Stock sits in reserve while the facing runs empty and the sale walks out.

LaborTeams walk aisles to verify what a system already claims to know, then search a backroom that was never mapped.

02 The operating loop

Four components. One continuous cycle.

RetailFlow is designed as a loop, not a dashboard bolted onto the store: locations are addressed, signals are read, risk is predicted, work is executed — and every execution writes corrected truth back into the model it came from.

StockPulseTruthAn addressable store

RetailSyncSignalLive state, no hardware

ShelfPulseForesightGaps anticipated, not found

LaborSenseMotionWork sequenced, not searched

03 StockPulse

Every unit gets an address.

StockPulse separates consumer units on the sales floor from base supply units in backstock reserve, maintaining real-time count accuracy through a permanent location-centric link between shelf facings and the reserve inventory that covers demand until the next delivery. Its Store-Based Ordering layer predicts forward demand precisely, balancing supply with demand across delivery cycles and moving the supply chain toward just-in-time response to store demand.

Return to the loop — inventory truth

04 RetailSync

Signals only. Nothing bolted to the shelf.

RetailSync is designed to integrate with legacy applications rather than replacing them, ingesting transactions, master data, planograms, and demand-chain delivery events. For legacy POS, it parses two years of item-level history to calibrate product sell-through velocities before day one. Those velocities avoid the capital expenditure for in-aisle cameras, smart shelves, RFID tagging, and robotic aisle scanning: the calibration already exists in store data.

Return to the loop — live state

05 ShelfPulse

The gap is predicted, never discovered.

ShelfPulse is the agentic in-store layer that calculates each product facing’s time to shelf out-of-stock from measured sell-through velocity. When reserve inventory exists in backstock, it is designed to have replenishment finish before the event, using intelligent labor-task batching to minimize labor time and backstock visits. This is intended to maintain on-shelf availability and to make POS data a more accurate guide to demand by time cycle for StockPulse’s Store-Based Ordering capability.

Return to the loop — out-of-stocks prevented

06 LaborSense

Foresight becomes a sequenced task.

LaborSense uses ShelfPulse inventory-event detections to convert predicted shelf-out-of-stock risk into batched, handheld-guided workforce management: replenishing from the correct backstock reserve location and using intelligent batching to minimize labor utilization. Visual gap-checking and backroom searching are designed to disappear. Together with batched picks, the target is a 40% reduction in store labor costs from RetailFlow operations, to be proven in a proof of value.

Return to the loop — motion removed

07 The loop closes

Each turn leaves the store more accurate than the last.

Completed work is itself a signal. Every pick, replenishment and correction is designed to be written back into StockPulse, so the model sharpens with use and the next prediction starts from better ground. Four components, four different returns.

Availability+Shelf held to calculated velocity

Labor−Checking and searching removed

Fresh−Waste cut by forward precision

Demand+Clean signals sent upstream

08 The economics

Opportunity begins with a truer operating picture.

RetailFlow helps retailers examine the opportunity in availability, labor, fresh, shrink, and fulfillment without requiring physical changes to the store. Financial impact is established against each retailer’s own baseline.

About 15%Reference assumptionStore labor cost as a share of sales. Kroger reported 14.7%, rounded to 15%. Replace with your own baseline.

An opportunity story, evaluated with each retailer’s own baseline

09 Proof of value

A defensible number from one store.

Data access, baseline, measurement and milestones are agreed before a proof starts — real-time telemetry replacing backward-looking spreadsheets, on one store, in one category, against a baseline you set.

Start a 60-day proof of value

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10 Questions

What we get asked first.

Straight answers on sensors, shrink and theft, short deliveries, your existing systems, in-day response and whole positions.

Data trust

Can demand be trusted without shelf sensors?

Existing sales activity informs demand. Observed counts and verified work correct the inventory record.

The design uses data the store already produces: point-of-sale transactions, inventory records, planograms, digital orders and delivery plans. RetailSync is designed to mark every event with its source, the time it happened and how current it is.

A predicted shelf count is an estimate, not a sensor reading. It is corrected by physical observation: when an associate completes and verifies a replenishment, or corrects a count, the shelf and backstock records update from what was actually seen. The design uses existing store data plus verified inventory counts, without requiring new shelf sensors or cameras. Optional feeds can supplement it.

Perpetual inventory records a store total, not what is on the shelf, so a product can be in stock but not on sale. RetailFlow is designed to hold salesfloor and backstock quantities separately, so both are under system control.

Shrink and theft

What about shrink and theft?

Expected-versus-observed counts can expose discrepancies for investigation.

Each verified count is compared with what the record expected. A variance is not proof of theft: it can come from a receiving error, a misplaced case, damage or a sale recorded late. Sensor-free operation does not mean every loss is detected immediately, and some loss may not be visible until a later count.

RetailFlow is not a theft-prevention system, and its labor case counts no theft benefit. Inventory distortion, fresh waste and loss are assessed separately against the retailer’s own baseline.

Supply

What if 100 are expected and only four arrive?

Each delivery is checked against the committed delivery plan. A shortfall is detected immediately, and store management and supply chain are alerted so it can be corrected.

RetailFlow has access to the forward delivery plan the distribution center or vendor is committing. If the delivery count differs from that plan, the variance is detected as part of delivery restocking, immediately, and store management and supply chain alerts are raised for correction. The shelf’s cover is recalculated from the stock that actually arrived.

Adoption

Why change from the systems we already run?

Designed to work alongside existing retailer systems without requiring their replacement. Data access, mapping, permissions and integration needs are agreed for each proof.

RetailFlow depends on data points the existing systems already expose, and works alongside those applications while returning more precise intelligence that they can use. Its use of existing system feeds is designed to need no pre-processing of data by the retailer’s internal IT teams. Near real-time visibility is why it interacts with them directly. Adoption starts with a conversation about fit and, if it fits, a bounded store proof with agreed categories, baseline, measures and milestones.

A proof still takes work on both sides: data access, product and location mapping, store processes and associate training.

In-day response

Does it respond during the day, or only overnight?

Designed for both. Overnight alignment sets the start-of-day work; during the day, sales and digital picks update the shelf picture and lead to verified work.

Overnight, counts and deliveries are reconciled and a start-of-day pick and replenishment list is prepared. During the day, sales and digital picks deplete the shelf record. For example, if shoppers take 25 of 30 units on a shelf in the morning, the record falls with each sale. When ShelfPulse predicts the facing will run out before the next delivery while backstock is available, LaborSense is designed to create a task that says what movement is needed, when it should be done and how long it should take, and that is assigned, acknowledged, completed and verified.

Picklists can be batched, digital picks are batched, and deliveries into backstock are batched by delivery as the distribution center or vendor dictates. Each batch is accounted for as it is picked; the later point-of-sale record for that order is handled separately by policy, so one purchase is not counted twice.

Labor

Does it remove a whole position?

Usable freed hours are summed weekly. At a 40-hour weekly working basis, each 40 hours of usable capacity released is equivalent to one full-time weekly position. Payroll savings depend on scheduling and an actual staffing decision.

A few minutes saved on each task does not pay for itself until those minutes add up to time a store can actually reschedule. Fragmented time may improve coverage without removing a position. Released capacity, whole 40-hour blocks, actual people and realized cash are four different things, and each needs its own evidence or decision.

11 What the loop returns

The headline is margin. These are the mechanisms.

Every return below comes from the same source: a store that finally knows where its inventory is, corrected continuously from signals it already produces.

Availability

Fewer shelf out-of-stocks

If the unit exists in the building, RetailFlow is designed to predict the gap before it opens and move stock from reserve to facing in time to hold the sale.

Labor

Lower labor intensity

Visual gap-checking and backroom searching are replaced by sequenced, batched tasks — aimed at a 40% reduction in labor costs across RetailFlow operations, to be proven in a proof of value.

Margin

More retained margin per unit

Leakage is designed to be corrected before it becomes a markdown, a substitution, a missed sale, or an hour of avoidable work.

Fresh

Stronger fresh performance

Forward-demand precision is intended to improve replenishment timing, markdown control and FIFO discipline, so perishables sell through with less waste.

Fulfillment

Profitable digital fulfillment

Pick activity is guided against live location truth, which is intended to stop online demand cannibalizing shelf availability or overloading store teams.

Demand

Cleaner signals upstream

Accurate shelves make POS and fulfillment data a truthful guide to real demand for replenishment, forecasting and planning.

12 Team

Built by people who understand the aisle, the backroom, and the architecture.

RetailFlow combines grocery retail systems expertise, agentic AI architecture, cloud data engineering, integration capability and store-execution discipline — built for the messy middle of real grocery operations, without asking retailers to rebuild the physical estate first.

Les McNeill

Retail Operations

Deep knowledge of how grocery stores actually operate and how enterprise retail systems fail at store level.

Grant Bussinger

Agentic AI & Data

Agentic AI architecture, data engineering, and enterprise systems integration behind the operating loop.

Aalok Muley

Strategic Advisor

Strategic guidance and advisory support for RetailFlow's growth and execution.

13 Call to action

Prove the margin in one store. Scale the model across the network.

Start with one store. Establish the baseline. Expose the leakage. Run the RetailFlow loop. Measure the margin recovered. Then scale what works.

Baseline Expose leakage Run the loop Measure margin Scale

Bring a store, a category, and a current performance baseline. We will show where the margin is leaking — and how RetailFlow is designed to capture it.