AI Engines for Point of Sale Systems

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We work with Point of Sale (POS) platforms looking to turn transactional data into actionable forecasting and operational decisions.

If you’re looking to move beyond reporting and dashboards, and introduce forward-looking demand and stock control capabilities without building them internally, SolvedBy.Ai engines provide that layer directly within your product.

Our engines use transactional data alongside external demand drivers to generate demand forecasts at product, location and time level, feeding into replenishment, allocation and stock positioning decisions across store networks.

This enables your customers to react faster to demand shifts, maintain availability on high-turn products and reduce excess stock, while allowing you to deliver planning capability as part of your platform.

What solutions we power in POS platforms

AI Forecasting

For demand drivers like sales, transactions, footfall, basket size.

Enables your platform to forecast demand at product and location level using both transactional data and external drivers. This gives your customers a forward-looking view of sales patterns, helping them plan ahead with greater confidence and reduce reliance on reactive decision-making.

AI Stock Control

For replenishment recommendations and ordering logic.

Generates data-driven replenishment and ordering decisions based on predicted demand, stock levels and operational constraints. This helps your customers maintain availability, reduce overstock and improve margins and efficiency.

AI Demand Forecasting

Where staffing requirements are part of the platform.

Translates demand forecasts into expected workload and staffing requirements by location and time. This allows your customers to align labour with demand, improving service levels while controlling labour costs.

AI Staff Scheduling

Where staffing is part of store operations within the platform.

Generates staff schedules aligned to predicted demand at store and day-part level, using forecasts of transactions, footfall and workload. This allows your customers to align staffing with expected in-store activity, improving service levels during peak periods while maintaining control of labour costs.

Case Study

How it works

1

Forecast the future

Our engines ingest transactional data from your platform via API alongside external demand drivers. Forecasts are generated across products, locations and time horizons, with support for both scheduled batch runs and near real-time updates where required.
2

Decide the outcome

Forecast outputs are processed into decision layers through optimisation engines, generating reorder quantities, allocation recommendations or staffing curves. These services account for constraints such as stock positions, lead times, capacity and operational rules defined within your platform.
3

Execute in your platform

Decision outputs are exposed via API endpoints and returned to your POS platform, where they are embedded into existing workflows, UI components or automation layers. Execution remains fully controlled within your application, whether decisions are user-approved or system-triggered.
4

Improve

Actuals, execution outcomes and performance metrics are fed back into the models via continuous data pipelines. This supports retraining, recalibration and model versioning, ensuring forecasts and decision outputs adapt over time as demand patterns and behaviours change.

AI Engines that can be used in POS Systems

Powers the AI Staff Scheduling solution by converting demand forecasts into optimised store-level schedules, taking into account staffing requirements, availability and operational constraints.
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Aggregates and structures external demand drivers such as weather, events and seasonality signals for use within forecasting models.
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Powers the AI Staff Scheduling solution by converting demand forecasts into optimised store-level schedules, taking into account staffing requirements, availability and operational constraints.
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Powers AI Inventory Optimisation and AI Stock Control solutions by processing demand forecasts and stock data to calculate inventory targets, replenishment quantities and stock positioning decisions across stores.
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The addition of third part data sets enhances the allocation of labour and assets based on forecast demand and operational constraints.
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....
Price optimisation supports pricing and promotion decisions based on expected demand and utilisation impact. Prices adjust in response to changing conditions rather than reacting late through blanket discounts.

In leisure, this helps optimise membership pricing, day passes, peak and off-peak rates, and promotional offers, protecting revenue while improving utilisation across quieter periods.
Opening hours optimisation evaluates when sites or facilities should operate based on expected demand and financial impact. It identifies which hours contribute value and which create unnecessary cost.

This allows leisure operators to adjust opening times by site or facility, maintaining access when demand exists while reducing exposure during low-usage periods.
Budget forecasting builds budgets based on expected demand rather than fixed assumptions. Forecasts adjust as conditions change, giving finance and operations teams clearer visibility of risk and cost pressure.

For leisure organisations, this improves control, reduces variance, and limits late-year corrective action when demand shifts.
(Best suited to asset-heavy leisure operations)

Predictive maintenance identifies early failure risk in critical assets that affect safety, availability, or customer experience.

In leisure, this applies to equipment such as pool systems, HVAC, rides, lifts, and facility-critical infrastructure, reducing unplanned downtime and protecting service and safety.

Ready to explore a partnership with SolvedBy.Ai?

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FAQ

POS and retail platforms use Forecasting to predict sales, transactions and footfall by site, channel, till and time interval. These forecasts can feed into their own labour planning modules, stock reorder logic or promotional planning tools. We provide the forecasting and optimisation layer; you surface the insights where your users already work.

We process only the minimum operational data needed to generate forecasts and decision outputs—typically time-series data such as sales volumes, transactions, demand drivers, staffing levels, inventory positions, or asset/telemetry signals (depending on the AI being used). We do not need customer names, store names, staff names, or any identifying labels; we work using system IDs only. This means your platform can retain all sensitive naming and context while still benefiting from our AI layer.

Scheduling.AI takes staffing or capacity demand and produces optimised schedules that respect contracts, labour laws, preferences, budgets and operational rules. It can return shift proposals for managers to review or fully allocated schedules ready to publish.

Most partners provide a simple configuration experience: “What are we forecasting?” and “What drivers should be considered?” plus a way to add datasets (upload/sync) and map them to sites/SKUs/assets. The key is to make exogenous intelligence feel like a trusted, configurable feature, not a hidden technical detail.

If demand cannot be fully covered, the optimiser will produce the best possible schedule given constraints and can flag under‑coverage. We can optionally generate “virtual” shifts to show where additional headcount would be needed.

Yes — we can expose model selection metadata (model family, validation performance, key drivers used) so your team can explain results internally and build trust with customers. If you prefer not to expose model names in the UI, we can still provide the metadata to your technical team for audit and support.

For each individual forecast series, we typically train and evaluate a shortlist of 10–20 candidate models as part of an automated data science framework. This matters because different series behave differently: one store may be stable and seasonal, another may be volatile and promotion-driven, and another may be trending due to growth or local change. Training multiple candidates ensures the forecast is fit-for-purpose rather than “one model fits all.”
TL;DR for AI Forecasting

TL;DR

SolvedBy.AI enables POS and retail platforms to embed AI-driven demand forecasting, replenishment and (optionally) staffing decisions via API. We forecast sales and footfall, turn predictions into reorder or staffing recommendations, and support a closed loop of forecast → decide → execute → improve—while you retain the retail workflows, UI and customer relationship.
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