AI for retail that puts the right stock in the right store
Custom forecasting, replenishment, pricing, and store-ops software built on your POS and ERP data. Fixed project cost, 4 to 12 weeks, and the code is yours.
How it works
Retail margin is decided long before a customer reaches the register: in what got ordered, how it was allocated, when it was marked down, and how much walked out the door. Most chains run those decisions on a merchandising system that reports history and a set of planner spreadsheets that guess at the future. We build custom AI software that sits on your POS, ERP, and supply chain data and turns those four decisions into forecasts, ranked actions, and exception queues your buyers and store leads can work in a morning.
Demand forecasts your planners will actually trust
Forecasts are produced at the SKU-store-day level and scored against the baseline your team uses today, so adoption is a measurement question rather than a leap of faith.
- SKU-store-day forecasting with promo, weather, and local event signals
- Cold-start handling for new items, new stores, and remodels
- Accuracy tracked per category against your current planning method
- Planner overrides captured and fed back into the next model run
Replenishment, allocation, and markdown in one loop
The same demand signal drives what gets ordered, where it goes, and when the price comes down, so the three decisions stop contradicting each other.
- Safety stock set by service-level target, not a flat weeks-of-supply rule
- Order rounding for case packs, pallets, and truck capacity
- Store-to-store transfers proposed before a purchase order is raised
- Markdown timing and depth modeled against the sell-through curve
Shrink and store-floor signals surfaced daily
Transaction-level anomaly detection and shelf imagery give loss prevention and store ops a short, ranked list instead of a monthly report nobody acts on.
- POS anomaly detection across voids, refunds, discounts, and no-sales
- Self-checkout loss patterns flagged by lane, hour, and operator
- Planogram and on-shelf availability checks from shelf photos
- Labor schedules built against forecast traffic and task load
Where teams use it
Built for real revenue work.
Replenishment for a multi-format grocery chain
Perishable ordering was set by store managers using last week's sales and instinct, producing waste in some stores and gaps in others. A forecasting and ordering engine now proposes daily quantities per store with shelf-life constraints built in, and managers approve or adjust rather than start from a blank sheet.
Markdown timing for seasonal apparel
Instead of a fixed calendar of discounts, the model watches sell-through by size curve and region and recommends when each style should break, and by how much. Buyers review a ranked list weekly and see the projected margin impact of taking the cut early versus holding.
Shrink detection across self-checkout
Scan-avoidance and produce-code substitution were suspected but never quantified. Transaction pattern models now score every basket, route the outliers to loss prevention with the receipt and lane context attached, and track recovery by store.
“Our planners were spending three days a week rebuilding the same spreadsheets. Now they spend that time on the exceptions the system flags, and our in-stock position on the top thousand SKUs is the best it has been in four years.”
FAQ
Questions, answered.
How is AI actually used in retail operations today?
The four places it pays off fastest are demand forecasting, replenishment and allocation, markdown timing, and shrink detection. All four share the same requirement: a clean, current view of sales, inventory, and receipts at store level. That is why we start with a data readiness check rather than a model, and why our first release usually covers one category or region before it goes chain-wide.
Do we have to replace our merchandising or ERP system?
No. We build alongside what you run, reading from your POS, ERP, and warehouse systems and writing recommendations back as orders, transfers, or price changes in the tools your teams already use. Integrations for SAP, Oracle Retail, NetSuite, Microsoft Dynamics, and most POS platforms are standard scope. If a system has no usable API, we work from scheduled extracts instead.
What does an AI project for retail cost and how long does it take?
A single-domain build such as forecasting plus replenishment for one category typically lands in the $60,000 to $120,000 range and ships in 6 to 8 weeks. Chain-wide programs that add pricing and shrink work run longer, usually 10 to 12 weeks. Every project is quoted as a fixed cost with a delivery date before work starts, and the code, models, and infrastructure are transferred to you at handover.
How is this different from the retail CRM you offer?
The CRM side is about the customer: loyalty, clienteling, campaigns, and lifecycle marketing. This is about the operation behind the shelf, meaning what to buy, where to send it, what to charge, and where product is being lost. Plenty of our clients run both, and we connect them so demand signals from marketing calendars feed the forecast.
Keep exploring
More from the Leadify platform.
Custom AI Software
The build model behind these systems: fixed scope, a dedicated team, and full code ownership.
AI Automation
Vendor packets, invoices, and store reporting processed straight through without re-keying.
Retail CRM
The customer side of retail: loyalty, clienteling, and lifecycle marketing on the Leadify platform.
Put a number on your stockouts and overstock
Send us a sample of your sales and inventory data and we'll come back with a scoped build, a fixed price, and a delivery date.