AI Engines for Enterprise resource planning platforms

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We work with Enterprise Resource Planning platforms that want to introduce AI-driven planning capabilities into their products, including forecasting, scheduling, inventory optimisation and procurement decision support.

Our AI engines integrate via API into your ERP planning architecture, allowing your platform to generate forecasts, translate them into operational requirements, and produce recommended actions such as schedules, allocations or replenishment decisions. You retain ownership of the ERP workflows, user experience and customer relationships, while our engines provide the optimisation and forecasting intelligence behind those decisions.

What solutions we power in ERP platforms

AI Demand Forecasting

To improve forecasts for demand, sales, workloads or volumes across sites, products or departments.
These forecasts provide the planning signals used by ERP modules to anticipate operational demand, supporting downstream decisions across production, procurement, staffing and inventory planning.

AI Labour Demand Forecasting

To translate forecasts into requirement curves (labour, capacity or resource demand).
These requirement curves define how much labour, capacity or operational resource is needed across time periods, providing structured inputs for scheduling, allocation and operational planning modules within the ERP platform.

AI Staff Scheduling / AI Resource Allocation

Where ERP includes workforce or work-order scheduling.
The engine determines how work, resources or staff should be scheduled or allocated based on predicted demand, operational rules and capacity constraints defined within the ERP environment.

AI Stock Control

To power replenishment decisions and stock ordering logic inside ERP planning.
The engine determines when stock should be reordered and in what quantities based on demand forecasts, current stock positions and supplier constraints.

AI Inventory Optimisation

To improve inventory targets and ordering decisions based on constraints and service level goals.
The engine calculates optimal inventory targets, buffers and replenishment policies that balance availability, working capital and operational constraints.

AI Task Scheduler

Where your ERP includes task lists and needs to allocate work across teams or individuals.
The engine assigns tasks to available resources based on priorities, timing and operational constraints, ensuring work is distributed efficiently within the ERP task management workflow.

Case Study

How it works

1

Forecast the future

Forecast demand or workload drivers using AI Forecasting.
ERP platforms use these forecasts to anticipate operational demand across products, locations, production lines or service activities, creating the planning signals required for downstream operational decisions.
2

Decide the outcome

Convert those forecasts into recommended actions, schedules, allocations, replenishment orders or inventory targets.
The engines translate forecast outputs into operational recommendations that ERP planning modules can use to determine how resources, inventory or production should be planned.
3

Execute in ERP

Users approve or execute decisions inside their existing workflows.
Recommended decisions are surfaced within the ERP workflows where planners or operational teams review, approve or execute actions using the platform’s existing approval and planning processes.
4

Improve

Learn from outcomes and feedback loops via analytics (forecast vs actual, decisions vs results).
Analytics compare planned decisions against real outcomes, allowing ERP systems to refine forecasts, planning assumptions and operational decisions over time.

Relevant AI Engines

Generates demand forecasts across products, sites or operational units using internal demand signals and deep exogenous data intelligence to improve forecast accuracy.
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Introduces external demand drivers such as weather, holidays, events or economic signals that influence demand patterns and improve forecasting accuracy.
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Translates demand forecasts into requirement curves that define labour, capacity or resource demand across planning periods.
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Generates operational schedules based on requirement curves, resource availability and planning constraints defined within your ERP system.
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Assigns tasks across teams or individuals based on priorities, timing and operational constraints within your ERP task management workflows.
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Optimises inventory targets, buffers and replenishment decisions using demand forecasts, stock positions and service level objectives.
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Allocates available resources such as staff, equipment or capacity to tasks or work orders based on demand signals and operational constraints.
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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

Yes. For ERP/SCM partners, Inventory AI typically sits behind existing purchasing, replenishment, and planning screens. It can output suggested purchase orders, transfer orders, stocking targets, and exception lists that map to your objects and approval flows.

For ERP vendors we typically power forecasting for revenue, volumes, workload and stock, plus downstream decision engines. This can include labour demand planning for service teams, stock and inventory optimisation modules, and resource scheduling for internal teams or assets. ERP partners often start with Forecasting and Stock/Inventory use‑cases, then add labour or resource scheduling later.

By embedding advanced forecasting, your platform delivers measurable financial value – not just reporting. This helps you differentiate, increase ARR through premium modules, and improve retention as customers rely on your system for critical operational decisions.

Yes. We are comfortable supporting very large enterprise customers that you onboard to your platform. In those cases, we typically provide enhanced technical support, bespoke AI work and may agree additional service levels and costs, but the commercial and contractual relationship remains between you and your customer.

We work with any platform that uses forecasts to drive decisions. Today our focus is: ERP platforms, Workforce Management (WFM) systems, POS and retail platforms, Restaurant Management Systems, Inventory Management systems and Supply Chain Management suites. If your product would benefit from using world class AI derived forecasts to drive staffing, stock, production or resource scheduling, we are likely a good fit.

Yes. Many ERP, inventory and supply chain partners embed Forecasting.AI only, using its outputs to drive their own planning or optimisation modules. You can start with Forecasting and add other AIs later when it makes product and commercial sense.

We run a fully automated, multivariate forecasting pipeline that trains and evaluates multiple candidate models for every forecast series (e.g., each store × metric). For each series, the platform automatically cleans and prepares the data, tests relevant exogenous drivers, and then trains a shortlist of models (typically 10–20) with hyperparameter optimisation and backtesting. We select the best performer using validation metrics, then generate the forecast using that model.

Our library includes 50+ SolvedBy.AI proprietary model families and 70+ open-source model families, allowing different stores, products, or assets to select different best‑fit approaches depending on local patterns and volatility. As new data arrives, we re-run the full selection process so the “best model” can change when the world changes — because forecast quality determines decision quality.

Our pipeline is automated from ingest to output: data ingress, cleaning, gap replacement, correlation testing against exogenous variables, model selection, hyperparameter optimisation, validation/backtesting, and forecast generation. We also run automated analysis to produce diagnostic logs and quality signals so partners can monitor data health and forecast performance. The goal is simple: remove manual data-science work while continuously reducing error.

A typical example: 100 stores × 5 forecasts per store = 500 forecasts generated on a schedule. Because each store and metric may select different best‑fit models, the platform may train thousands of candidate model instances across the run to ensure each forecast is individually optimised. This is how you deliver enterprise-scale forecasting without manual modelling.

The FAQ Hub is the primary self-serve knowledge base for: how the AIs work, data requirements, exogenous data options (customer/platform/partner), implementation workload, security/compliance, and commercials/pricing. It’s structured so partners can quickly find answers by platform type (ERP/WFM/POS/etc.), by engine (Forecasting/Scheduling/etc.), and by audience (engineering, product, legal, security, sales).

SolvedBy.AI is a UK‑based applied AI company that provides forecasting‑driven optimisation engines for software platforms. We specialise in Forecasting, Demand/Labour Planning, Scheduling, Stock & Inventory optimisation, and related decision engines. Our AIs are embedded via APIs and can be white‑label or co‑branded inside ERP, WFM, POS, Restaurant Management, Inventory and Supply Chain systems.

The best messaging is: “You stay in control.” Start with review-and-approve and allow customers to move toward automation when they trust the outputs. This reduces adoption friction and supports enterprise governance.
TL;DR for AI Forecasting

TL;DR

SolvedBy.AI enables ERP vendors to embed AI-driven planning and optimisation via API. We power forecasting, demand translation, scheduling, inventory and stock decisions using a forecast → decide → execute → improve loop—while you keep the ERP workflows, UI and customer ownership. This lets you launch AI planning modules faster, with lower risk, and without building or maintaining a full data science and optimisation team.
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