Forecasting AI

Bring World Class Forecasting To Your Platform
Become a Partner
Man using tablet for Enterprise AI Solitions

What is Forecasting AI

Forecasting AI is a high-performance prediction engine that plugs into your SaaS platform to provide reliable, adaptive forecasts, without requiring you to build or maintain AI infrastructure. It analyses historical patterns, seasonality, external variables, and real-time signals to produce accurate, explainable predictions.

It powers forecasts used to drive “decide” solutions such as labour planning optimisation, production scheduling.

Direct solutions this engine powers

AI Demand Forecasting

Predict future sales and service demand with confidence ranges that adapt in real time. By embedding our Forecasting AI engine into your platform, you give your customers a powerful forecasting capability that strengthens operational planning, reduces uncertainty, and drives adoption of your product’s decision-making features.

AI Footfall and visitor forecasting

Model visitor flows to optimise staffing, inventory and capacity decisions, a critical need for retail, hospitality and venue-based customers. When integrated into your solution, this forecasting layer unlocks automated resourcing workflows, making your platform more intelligent, more proactive, and substantially more valuable to end-users.

AI Budget Forecasting

Generate dynamic, demand-aligned budget forecasts that move beyond static spreadsheet planning. Adding this capability to your platform allows your customers to create financial plans rooted in real operating patterns, increasing accuracy while positioning your software as a strategic forecasting and budgeting engine.

Why partners need it

Designed for operational SaaS products, with APIs that output forecasts in a way that drives downstream decision engines.
Designed for operational SaaS products, with APIs that output forecasts in a way that drives downstream decision engines.
Supports Exogenous Data integration (weather, promotions etc).
Self improving models that maintain accuracy as data changes
Transparent, explainable outputs that support the forecasting insights

Typical Outputs

Daily/weekly/hourly forecasts per site / SKU / department
Your platform can request highly granular predictions tailored to the operational structure your customers use. This allows you to embed precise, context-aware forecasting into planning, scheduling, inventory and reporting workflows.
Confidence ranges and forecast curves
Our engine provides clear confidence ranges and visual forecast curves that your platform can present within dashboards or planning modules. These help your customers understand prediction certainty and make more informed, risk-aware decisions.
Forecast history and baselines
Access historical forecast performance, baselines and comparisons that help customers identify trends, seasonality, and long-term shifts. Your platform can display this alongside actuals to strengthen analytics and build trust in AI-powered forecasts.

What partners need to implement

Provide historical time-series data along with the identifiers and dimensions needed to request forecasts and ingest outputs back into your platform. This allows our engine to generate accurate, context-specific predictions that your application can retrieve and present through a simple API call.
Embed the forecasting workflow in your product, typically: (dataset upload/sync + mapping), (define forecast target, horizon, granularity), (review/select forecasts and publish). This ensures your users interact seamlessly with forecasting outputs while all underlying intelligence continues to be powered by our AI engine.
Expose simple confidence metrics (forecast vs actual; error over time) so end users can validate outputs and build trust. Your platform provides the visual layer and reporting UI, while our engine continually enhances forecast accuracy in the background.
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.

Case Study

Ready to explore a partnership with SolvedBy.Ai?

Become a Partner

FAQ

The partner programme is aimed at product, engineering and commercial teams building software that already supports operational decisions but needs best‑in‑class AI forecasting and optimisation. Typical partners include WFM, ERP, POS, restaurant, inventory and supply chain platforms that want to launch AI features without building a large data‑science team.

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.

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.

At minimum we need a year’s worth of historical time series for the metric you want to forecast (e.g. sales per store per interval, work orders per site, units sold per SKU). Accuracy improves when you add exogenous datasets such as promotions, prices, events, weather or telemetry signals.

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

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)

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

The partner site is intentionally curated to include only partner-ready Engines (repeatable across SaaS platforms, standardised integration patterns, and supported by partner assets). The direct site may showcase additional “Forecast & Decide” solutions that can require more bespoke data, workflow design, or customer-specific tailoring—so they aren’t always published as standard partner Engines at launch.

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.

TL;DR for AI Forecasting

TL;DR

AI Forecasting generates accurate, explainable time-series forecasts for demand, sales and operational drivers using your historical data and exogenous signals. The outputs are designed to feed directly into downstream decision engines such as staffing, inventory and production planning. Partners integrate via API, embed a simple forecast workflow in their UI, and expose lightweight accuracy metrics so users can trust and act on the forecasts—while SolvedBy.AI powers the forecasting engine.
Become a Partner
Scotia Bank House, 6 South
Charlotte Street, Edinburgh,
Scotland, EH2 4AW
AI Engines
AI Engines Forecasting AI Demand AI Scheduling AI Inventory AI Resource Scheduling AI Task Scheduler AI Production Scheduling AI Exogenous Data
Partners
Partner Solutions Workforce Management Platforms Enterprise Resource Planning Platforms Supply Chain Management & Planning Platforms Restuarant Management Systems Point of Sale Systems Inventory Management Software Budgeting & Forecasting Software
About
Partnering Pricing Security and Trust
Resources
Developer Centre Case Studies Terms & Conditions
© 2026. SolvedBy.Ai. Solved By Ai Ltd. All Rights Reserved.
Privacy Policy | Cookie Policy