Proof that focuses on live use, not just the build.

Situation

A business-critical recipe platform had become difficult to change, support, and trust. Core workflows around recipe development, optimisation, and management were fragmented across the estate, while the platform handled highly confidential operational data.

What changed

• Replaced a legacy recipe-management platform with a modern implementation.
• Improved workflow across recipe development, peer review, reporting, scheduling, labelling, and factory integrations.
• Stayed involved after launch to support the live service and ongoing global rollout.

Why it mattered

• Created a modern, maintainable, and secure environment for highly confidential data.
• Supported daily operational use as well as continued rollout activity.
• Showed transformation work that held up beyond build stage and into live service.

Ongoing role

• Support continued into live operational use.
• Rollout support helped factories launch on the new platform.
• The work stayed accountable after go-live rather than ending at implementation.

This is the kind of work Plainstep is built for: legacy replacement that improves workflow, integrates across operations, and stays accountable after go-live.

Case facts

• Client type: global manufacturer.
• Programme shape: business-critical legacy replacement.
• Workflow footprint: recipe development, peer review, reporting, scheduling, labelling, and factory integrations.
• Support model: rollout and live support continued after go-live.

Why this proof is useful

What matters here is that the work was business-critical, crossed a broad workflow footprint, and stayed accountable after go-live.

This is the kind of work Plainstep is built for: legacy replacement that improves workflow, integrates across operations, and stays accountable after go-live.

Situation

Clinical requests were arriving across multiple facilities and telehealth workflows with mixed structured and unstructured data, state-specific rules, urgent response windows, limited practitioner capacity, and coordination work spread across phone calls and spreadsheets.

What changed

• Built a rules-driven triage engine that assessed clinical requests against hundreds of rules, set priority from low to urgent, and triggered required follow-up consults.
• Built automated scheduling that aligned practitioner and nurse rounds across facility hours, availability, preparation time, travel, mandated breaks, mixed telehealth and in-person delivery, and time-zone differences.
• Integrated the platform with external clinical operations and billing workflows so schedules, appointment outcomes, notes, and billing data stayed synchronised and exceptions could be recalculated or queued for review.

Why it mattered

• Replaced manual coordination via calls and spreadsheets with structured digital workflows in a high-trust clinical setting.
• Improved practitioner utilisation while still respecting urgent care windows, nurse preparation needs, and operational handover constraints.
• Delivered material operational impact, including an estimated $4M annual saving and roughly 65% of appointments being auto-scheduled.

Ongoing role

• The platform handled day-to-day exceptions such as staff sickness, availability changes, hospital admission, and resident-status changes.
• Schedules could be recalculated automatically or routed into an exception queue when operating conditions changed.
• The work covered triage, scheduling, operational integration, and billing flow rather than stopping at a single workflow step.

This is the kind of work Plainstep is built for: operational complexity, constrained capacity, and governed automation that still keeps human accountability visible.

Case facts

• Client profile: telehealth provider operating across Australia.
• Service footprint: approximately 10,000-15,000 people supported across about 150 facilities.
• Operating scale: around 300 practitioners and nurses, with roughly 800 clinical tickets per day at peak.
• Reported outcomes: estimated annual savings around $4M, with roughly 65% of appointments automatically scheduled.

Why this proof is useful

This proof shows experience with rules-heavy clinical operations where urgency, availability, coordination, and downstream compliance all have to work together in live service.

This is the kind of work Plainstep is built for: operational complexity, constrained capacity, and governed automation that still keeps human accountability visible.