Global WFM platform: AI suite rolled out at 800k-user scale

A global WFM/HCM platform embedded SolvedBy.AI engines to deliver granular forecasting, staffing demand planning, and scheduling — proving value in high-variance stadium operations and rolling the capability out as a core product.

At a glance

Partner: Global WFM platform (anonymous)
Best for SaaS vendors in: WFM / HCM (stadia, retail, healthcare, public sector)
Engines embedded: Forecasting.ai, Demand.ai, Scheduling.ai
Integration pattern: Two-step scheduling (propose → review → allocate)
Time to market: ~6 months
Launch regions: Australia, UK, USA
Measured impact: Forecast MAPE 4.76% → 3.38% (≈29% improvement); managers saved ~3 hours/week
Commercial impact: Helped meet roadmap expectations and reduce churn risk by delivering demanded features

The product challenge

The product team needed to deliver a credible AI roadmap without derailing core roadmap priorities (UX, analytics, integrations). Their customers existing forecasting approach relied heavily on spreadsheets, which struggled with event-driven volatility and granular staffing requirements.

The engine-powered solution

The platform embedded:

Forecasting.ai configured for multiple forecasts per day at granular operational entities (e.g., concession/station).
Demand.ai to translate demand into role-based staffing requirements by time bucket.
Scheduling.ai to generate schedules that satisfy compliance and staffing constraints.

Partner positioning: the engines are designed for partner embed — meaning other SaaS vendors can integrate the same capabilities into their products via API, while retaining customer and commercial ownership.

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Implementation & productisation notes

Two-step scheduling was a deliberate product decision:
1. AI proposes shifts
2. Managers review
3. AI allocates shifts in a second step
  This reduced trust barriers in complex environments and improved adoption.
The partner focused engineering capacity on product surface area (UI + analytics), while SolvedBy.AI engines handled model performance and iteration.

Rolled out to the entire customer base as a core product capability.

Outcomes

Forecast accuracy improved: MAPE from 4.76% to 3.38% (≈29% accuracy improvement).
~3 hours/week saved per manager through reduced manual planning and rework.
Commercially, the platform could deliver features customers demanded — supporting retention and reducing churn risk.

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Partner playbook (what to copy)

Use two-step scheduling when trust matters and workflows are complex.
Start where variability is highest (stadia/event operations) to prove value quickly.
Protect your roadmap: partner engines can ship AI capability without rebuilding your org.
Support multiple forecast runs/day when customers operate with volatile inputs.

Partner context

This platform serves 800,000+ users globally across multiple verticals. A major proving ground was stadia, where demand and staffing requirements can vary dramatically by event, concession, and service window — and where forecasts sometimes need to be produced daily or multiple times per day.

Quote

“Our customers wanted a real AI roadmap — not a slide deck. Partnering let us bring forecasting, demand planning and scheduling to market quickly while our engineers stayed focused on the product experience and analytics.”

— VP Product (anonymous)

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TL;DR

A global WFM platform embedded the full Forecasting → Demand → Scheduling suite, improved forecast accuracy by ~29%, saved managers ~3 hours/week, and delivered roadmap features at scale.

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