Scheduling AI is an optimisation engine that produces staff schedules tailored to actual demand while respecting employee constraints (such as availability, contracts and skills) and your business rules (coverage requirements, policies, break rules, etc.).
When embedded into your platform, this engine handles the complex optimisation logic so your solution can serve managers with schedules that are fair, compliant, and cost-effective — all without you building and maintaining your own scheduling algorithm stack.
Direct Solutions this engine powers
AI Staff Scheduling
AI Staff Scheduling generates fully optimised rosters that align labour supply with required demand curves. Your platform can use this capability to give customers accurate, compliant shift plans that help them hit service and productivity goals every day.
why partners need it
Respects real business rules — contracts, skills, breaks and policies — without custom code.
Delivers shift assignments alongside compliance and coverage visibility.
Integrates via API so your platform owns the UI and workflow experience.
Reduces reliance on manual scheduling or spreadsheet-based planning.
Typical Inputs
Demand curves or requirement curves from AI Demand Forecasting
The engine uses predicted demand and requirement signals (e.g., hours required by period) as the baseline for building schedules.
Employee availability, contracts, break rules
You supply the engine with your customers’ existing workforce constraints — who can work when, minimum/maximum hours, mandatory breaks, contract rules and skill requirements, so schedules respect real workplace rules.
Site rules, labour policies, constraints
Business-specific rules such as coverage minimums, union policies, labour cost limits, and local restrictions are provided so the engine honours the real operational framework your customers follow.
Typical Outputs
Optimised schedules per site per period
The engine returns schedules that match demand at the site level across defined time periods (e.g., hourly, daily). Your platform can present these as ready-to-publish rosters or as proposals that managers can review and adjust before publishing.
Shift assignments per employee
Scheduling AI outputs clear shift assignments for each employee based on availability, skills and policy constraints. Your UI can use this to show managers who is working when — reducing manual assignment effort and improving accuracy.
Coverage metrics and exceptions
Alongside schedules, the engine produces coverage summaries and exception reports that highlight where planned coverage meets or falls short of demand, as well as where policies were adjusted. Your platform can surface these metrics to help managers validate schedules and identify any outstanding issues.
What partners need to implement
Your WFM platform sends the scheduling engine a payload representing the current business context of the rota, including demand requirements, employee contracts, availability, roles, skills, and existing shifts. This JSON payload reflects the operational logic already held in your scheduling model and acts as the context for the AI. The engine combines this context with the optimisation configuration to generate compliant, optimised schedules, which are returned via API and ingested back into your platform.
This lets your platform send necessary context to the engine and receive fully computed schedules that can be surfaced in your scheduling workflows.
Partners implement a configuration UI that allows each tenant to define how the scheduling engine should optimise their rotas. This interface captures the rules, priorities, and constraints that guide the AI — such as demand coverage, fairness, shift lengths, break policies, and compliance requirements. These settings are stored per tenant and sent to the engine as a configuration payload, effectively acting as the ‘prompt’ that tells the AI how to balance competing objectives when generating schedules.
This covers the screens and interactions your users will use to generate schedules and interact with the engine outputs while keeping your familiar interface and control layer intact.
Expose a small set of confidence and outcome metrics so managers can validate schedules (e.g., demand vs planned coverage; budget vs planned cost; actual vs planned; cost-to-sales ratio where relevant).
Your platform visualises these metrics, helping customers trust and tune the results generated by Scheduling AI.
Inventory optimisation sets stock and consumable levels based on expected demand and uncertainty. It identifies risk early, allowing teams to adjust before shortages or excess occur.
In leisure, this supports better planning for retail stock, food and beverage, consumables, and operational supplies by site or facility, reducing waste while maintaining availability during busy periods.
Task scheduling plans what work should be done, when, and by whom, based on expected demand and available capacity. Tasks are prioritised and sequenced to reflect how work is actually completed on site.
For leisure teams, this improves execution across cleaning routines, safety checks, facility preparation, changeovers, and compliance tasks, ensuring critical work is completed before and during peak usage.
Resource allocation supports decisions on where to deploy limited resources such as space, equipment, capital, and budget. It evaluates options using demand forecasts and expected return.
Leisure leaders use this to decide which facilities to prioritise, where to invest, and where to scale back, improving utilisation and returns from existing assets rather than expanding capacity unnecessarily.
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.
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.
Yes. Tasks such as cleaning, replenishment, prep or maintenance can be scheduled alongside or within shifts, ensuring the right people are assigned at the right times.
The system can model breaks as part of the optimisation, including lunch‑break rules (length, position, coverage requirements) and shorter rest breaks.
Forecast accuracy in Smart Scheduling is comparable to standalone Forecasting.AI. We typically benchmark against your existing forecast or manual planning approach and expect a meaningful reduction in error, particularly around peaks and troughs that most manual methods miss.
Use the Engine name for technical scope, integration work, payloads, and platform architecture (because Engines are the modular units you embed). Use the Solution name only when you want a customer-facing description of the use case. Many partners also choose their own UI label (e.g., “Schedule Assist”) while the underlying capability remains “Scheduling AI”.
We assume partners already provide:
Authentication
Their own data ingestion pipelines
A core UI framework (admin screens + rota/schedule views)
The toolkit accelerates the AI-specific UI and workflow layer, rather than replacing a partner’s platform foundations.Note: we also understand that some of these configurations will not be implemented as stand alone UIs but integrated into rules engines and rota management screens. We have chosen to publish these stand alone UIs as we believe they will speed the adaptation of these tools which will be unique per partner.
Yes. The availability reference UI covers capturing manager-approved unavailability as a hard constraint that should not be overwritten by scheduling runs. It also supports the concept of hard vs soft treatment (depending on partner workflow preference).
Bulk apply + “exceptions” management (select all then override a subset)
Targeted exceptions via a rule-set builder (apply by location/region/employee filter)
Explainability panels per control (what it influences, symptoms, recommended starting values)
TL;DR
AI Scheduling turns demand into compliant, optimised staff schedules. It ingests demand curves, employee availability and business rules, then produces cost-effective schedules that meet coverage requirements while respecting contracts and constraints. Partners integrate via API, embed a human-in-the-loop scheduling workflow in their UI, and expose simple outcome metrics so managers can review, trust and publish schedules—while SolvedBy.AI powers the optimisation engine.
