Life Sciences

Build software around the decisions scientists make.

Design research applications that connect scientific workflows, data, and usable interfaces without hiding important context.

Discuss Your Challenge

The challenge

Scientific code outlives the person who wrote it.

Spreadsheets and scripts can become essential research infrastructure without clear ownership, versioning, or support.

Implementation considerations

Make the boundaries explicit.

Understand the scientific task

Observe how researchers use the current tools and where interpretation matters. Translate those observations into domain models, interaction flows, and acceptance criteria that scientists can review.

Build clear interfaces and contracts

Keep scientific calculation logic separate from presentation and integration. Define API contracts, data validation, and error states so that incorrect inputs cannot quietly become plausible outputs.

Plan for continued use

Document deployment, configuration, dependencies, and support responsibilities. Include reproducible test fixtures and a process for reviewing changes to scientific logic with domain experts.

Technical perspective

The architecture behind the work.

A domain model that matches the scientific task is what keeps an application maintainable.

Scope the first step

Before the first commitment.

Why not extend the spreadsheet that already works?

Sometimes that is the right answer. The question is whether the scientific task is stable enough to encode as a domain model with tested interfaces, and whether anyone is accountable for it after the author moves on.

Scope, deliverables & terms ↗

Keep exploring

See how the pieces connect.