How Integration Works

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How this works in practice

  • You map your data model into our payload format
  • You call SBAI APIs to run forecasts / schedules / decisions
  • You embed decision outputs into your UI workflows
  • You publish outputs back into your platform

Integration workstreams

Map your data model to the SBAI payloads, send inputs, and ingest outputs with clear run monitoring and error handling.
Embed configuration, run, review/override and publish workflows inside your product (we provide reference wireframes + journey storyboards for the WFM engines).
Expose a small set of confidence and outcome metrics (forecast vs actual; plan vs actual; budget vs actual) so end users trust the AI and partners can iterate quickly.
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.

UI wireframes + user journey storyboards

We provide UI wireframes and storyboards that show how end users configure, run, review and publish each AI engine inside a SaaS product.
These UI patterns have been used in other workforce management platforms and proven with end users.
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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Become a Partner

FAQ

Partners typically implement:

  • A way to ingest historic time series (and incremental updates)
  • A UI/workflow to select the “feature” to forecast (e.g., sales, orders, work orders, failures) and attach optional drivers (exogenous variables)
  • A mechanism to bind datasets/fields to identifiers (location/SKU/asset/team)
  • Storage and UX to display forecasts and make them usable (charts, exports, downstream modules)

A practical approach is:

  • Use the storyboards to align product/UX/engineering on the end-to-end flow
  • Use the wireframes to build your UI backlog and acceptance criteria
  • Use the UI↔API mapping to implement the integration without guesswork
  • Use the partner portal’s docs/FAQs to validate edge cases, roles, and run-state handling

Partners typically implement (1) data feeds for forecasts/history, stock position and constraints; (2) UI to review/override/approve recommendations inside existing workflows; and (3) basic analytics to validate outcomes (service level vs target, stockout/waste risk, working-capital indicators) plus optional adoption metrics (acceptance rate, overrides).

Partners typically implement:

  • A payload builder that compiles supply + constraints (people/resources, contracts/skills, availability, rules) and demand/requirements
  • Review of data gaps (what you capture vs what scheduling needs) and decisions on new fields
  • Ingestion of scheduling outputs (proposals and/or allocations)
  • UI to review/approve/edit and then publish into your system of record

It covers how a partner can let customers register a dataset name, upload a file, choose dataset type (feature vs exogenous), download templates, and see instructions/help. The key principle is: SBAI receives historic datasets via API—how you collect them (CSV upload UI vs integration) is your choice.

We commonly support 15‑, 30‑ and 60‑minute intervals for WFM and operational use‑cases, and daily or weekly granularity for supply chain, inventory and financial planning. We can also support bespoke intervals where justified.

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).

They are implementation references that map UI actions and states to the API interaction pattern partners need to implement. This includes a mapping matrix such as: UI action/state → API endpoint(s) → expected response(s) → UI state.

Because most integration time is lost on UX and workflow decisions, not on writing HTTP calls. The toolkit removes ambiguity by showing proven UI patterns for configuration, execution states, results review, and manual override—so partners can ship faster and avoid redesign cycles during pilots.

Yes. The mapping artefacts explicitly cover:

  • partial outputs
  • empty outputs
  • validation errors
  • error-flagged-but-usable responses

…and special attention for scheduling outputs that may contain unassigned shifts, so the UI can present “what’s filled vs what needs manual completion” clearly.

They’re designed for:

  • Partner Product & UX teams (to understand where AI fits and adapt patterns into your design system), and
  • Partner Engineering teams (to map each UI action/state to the correct API interactions and handle results and edge cases).

The partner website includes a Partner UI Toolkit: a downloadable wireframe pack, end-to-end storyboards, UX recommendations, and UI ↔ API mapping artefacts. Together, these show where AI appears in your product, what users do before/during/after a run, and how to implement results safely and consistently.
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