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Case Studies

Real systems.
Real constraints.

A selection of engineering problems drawn from real-world work. Some details are intentionally generalized to respect client confidentiality; the architecture lessons are not.

01

HEALTHTECH · AI · INTEGRATION

Applied AI in healthcare

Turning structured medical data into patient-friendly explanations without turning an AI model into a clinical decision-maker.

Challenge

Integrate AI-generated explanations into an existing clinical workflow while keeping outputs structured, reviewable and appropriately bounded.

Approach

Separate structured clinical inputs, model generation, validation and downstream document flow. Add escalation paths for exceptional results and preserve human oversight where it matters.

Engineering lesson

The model is only one component. Data contracts, validation, observability, workflow ownership and failure handling determine whether AI becomes useful infrastructure.

02

TRAVELTECH · APIs · DATA

Multi-provider travel technology

Making several global providers behave like one coherent product when each has a different model of hotels, rates, availability and pricing.

Challenge

Normalize provider-specific data and live pricing while preserving enough source detail to debug differences and make correct commercial decisions.

Approach

Build provider adapters, normalization rules, source-aware pricing logic, caching and explicit fallback behavior rather than hiding differences behind a fragile common schema.

Engineering lesson

A “single source of truth” often has to be engineered. Normalization is not just field mapping—it requires business semantics and traceability.

03

ENTERPRISE · AUTOMATION · INTEGRATION

Enterprise process automation

Reducing manual handoffs across operational systems without creating another disconnected application.

Challenge

Processes span people, ERP/CRM systems, internal applications and data owned by different teams. Manual movement creates delay and inconsistent state.

Approach

Map the operational flow first, establish system ownership and then automate specific transitions using APIs, workflow services and controlled integrations.

Engineering lesson

Good automation removes ambiguity as well as clicks. The hardest part is often deciding which system owns each state and exception.

Beyond the case study

The lessons become
Field Notes.

Our Field Notes explore the architectural and operational lessons behind problems like these in more depth.

Read Field Notes →