AI Engines for Forecasting & Budgeting Platforms

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We work with forecasting, budgeting, or scenario planning software looking to improve the accuracy and reliability of planning outputs.

If you are looking to introduce more dynamic, AI driven forecasting without building it internally, our AI engines provide that capability within your product.

These engines combine internal financial and operational data with external demand drivers to generate more accurate forecasts that feed directly into budgeting, scenario planning and performance forecasting.

What solutions we power in Forecasting & Budgeting Platforms

AI Budget Forecasting

Enables your platform to generate budget forecasts that reflect expected demand across key financial lines, including revenue, cost and capacity utilisation. This allows your customers to build plans with stronger alignment between demand expectations and financial outcomes, improving forecast accuracy and reducing variance.

AI Demand Forecasting

Generates forward-looking forecasts for core budget drivers (sales, transactions, demand, workload, costs) across products, locations and time horizons. This provides consistent, data-driven inputs for revenue projections, demand modelling and cost assumptions within budgeting and scenario planning workflows.

AI Labour Demand Forecasting

Translates demand forecasts into workforce and capacity requirements that feed directly into labour cost planning. This enables your customers to align staffing assumptions with expected demand, improving cost control and reducing the gap between planned and actual labour spend.

Case Study

How it works

1

Forecast the future

SolvedBy.Ai engines ingest financial, operational and driver data from your platform via API, along with external signals, to generate forecasts across required planning horizons. Assumptions are applied as model parameters, allowing forecasts to be recalculated under different conditions.
2

Decide the outcome

Forecast outputs are converted into structured budget inputs, including revenue and cost drivers, and organised into scenario versions that can be compared within your planning workflows.
3

Execute in your platform

Outputs are exposed via API and integrated into your platform’s planning workflows, where users interact with scenarios, adjust assumptions and publish approved versions into reporting and finance layers.
4

Improve

Compare plan vs actual and refine models and assumptions over time.
Actuals and variance data are fed back into the models, enabling recalibration of both forecasts and parameter sets, improving accuracy and consistency across planning cycles.

AI Engines that can be used in Forecasting & Budgeting Software

Powers the AI Demand and AI Budget Forecasting solutions by generating forecasts for key financial and operational drivers across planning horizons, forming the basis for revenue, cost and scenario modelling.
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Enhances all forecasting and budgeting solutions by providing external demand drivers that are incorporated into models to improve forecast accuracy and responsiveness to changing conditions.
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Powers the Labour Demand Forecasting solution by translating demand forecasts into workload and capacity requirements that feed staffing and labour cost assumptions within budgeting workflows.
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Powers the AI Inventory Optimisation solution by translating demand forecasts and stock data into replenishment quantities and ordering logic.
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The engine optimises 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.

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FAQ

Accuracy varies by use case, data quality, horizon and volatility, but in many scenarios we see material improvements over naive or in‑house baselines. We typically run a retrospective evaluation on historical data so you can compare our accuracy to your current approach before rolling out to customers.

Yes. The idea of the dataset API is that you upload historical data once (with incremental updates) and then reference dataset IDs in your forecast requests. You do not need to send raw history for every call.

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.

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.”

We design endpoints to respond within agreed SLO targets and can support asynchronous workflows where long‑running jobs are required. If your platform has strict timeout limits, we will design the integration to fit (for example, pre‑creating forecasts and returning cached results).

Yes. Forecasting AI accepts historic feature data (the metric to forecast) and historic customer-specific exogenous datasets (e.g., promotions, bookings, loyalty activity, campaigns) via our APIs.

How you collect that data is up to you as the SaaS partner:

  • For smaller customers you might provide a simple CSV upload UI in your platform and then push the data to SBAI via API, or
  • For larger/strategic customers you might build a bespoke integration and UI to automate data feeds into your system, then pass the historic datasets to SBAI.
  • In all cases, SBAI receives the historical datasets through the same API pattern, with datasets linked by your system IDs (e.g., location_id, sku_id) and time alignment. This gives you flexibility to support different customer tiers without changing the underlying SBAI integration.

The Forecasting UI pack includes reference screens for:

  • Uploading datasets (e.g., CSV upload UX on the partner platform side, then pushed to SBAI via API)
  • Capturing latitude/longitude per rota/location (including bulk upload) to enable platform exogenous datasets
  • Selecting datasets and scheduling forecasts (cadence, horizon, granularity, and multiple forecasts per rota)

AI Demand Forecasting (Solution) maps to Forecasting AI (Engine).

AI Budget Forecasting (Solution) maps to Forecasting AI (Engine).

Within a given forecast, we expect consistent intervals across the primary time series and relevant exogenous variables. Different forecasts can use different intervals (e.g. 15‑minute for labour planning, daily for stock).

In most cases, the partner owns or licenses the dataset and decides how it is sourced, maintained, and packaged. SolvedBy.AI integrates it into the forecasting pipeline as a reusable driver dataset, available to the partner’s customers alongside platform datasets.

AI Labour Demand Forecasting (Solution) maps to Demand AI (Engine).
TL;DR for AI Forecasting

TL;DR

SolvedBy.AI enables forecasting and budgeting software providers to embed AI-driven, budget-grade forecasting via API. We improve confidence in plans by combining historical data with exogenous drivers and optional demand-to-capacity translation. This supports a forecast → decide → execute → improve loop—while you retain budgeting workflows, scenarios and customer ownership.
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