Selected work
From difficult problem
to working product.
A closer look at the thinking, design and engineering behind the systems we build.
Selected work / owned platform
PaktorPlaces
A Singapore discovery platform built around how people actually choose where to go.
Product strategy · UX · Engineering · Editorial infrastructureExplore the case study
Concept studies / illustrative scenarios
The thinking behind
a working system.
Explore three example challenges. These are conceptual demonstrations of our approach, not client projects or published results.
01 / INPUTOrders & records
02 / SYSTEMShared workflow
03 / EXPERIENCECoordinated operations
ILLUSTRATIVE SYSTEM / NO CLIENT DATA
A business that works as one.
- The situation
- Orders, customer records and fulfilment live in separate tools. Every handoff requires someone to reconcile the details.
- The design decisions
- Model the shared information, connect the existing tools and make ownership of each step explicit.
- What production demands
- Permissions, exception handling, traceability and a migration path from the current workflow.
01 / INPUTCustomer needs
02 / SYSTEMConnected product
03 / EXPERIENCESelf-service experience
ILLUSTRATIVE SYSTEM / NO CLIENT DATA
A better experience, end to end.
- The situation
- Customers depend on email and manual updates to request a service, follow progress and get the information they need.
- The design decisions
- Design the customer journey alongside the operational system, with a clear interface and connected business rules.
- What production demands
- Identity, accessibility, data boundaries and a consistent experience across screen sizes.
01 / INPUTDocuments & requests
02 / SYSTEMProcess & review
03 / EXPERIENCEReliable handoffs
ILLUSTRATIVE SYSTEM / NO CLIENT DATA
Less repetitive work. More control.
- The situation
- Documents and requests arrive in different formats. People repeatedly extract information, move it between systems and check the result.
- The design decisions
- Connect ingestion, processing and review. Use AI where it helps, with explicit rules for human decisions and exceptions.
- What production demands
- Validation, audit trails, recoverable failures and visibility into what the automation has done.
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