Platform Architecture for an Animal-First Genetic Testing LIMS

The challenge

The organization was building a genuinely new product — not a modernization of an existing system, but a platform designed from the ground up for animal DNA lab operations. The core requirement was an operational LIMS that handled animal intake, sample management, case queues, result review, and report release as explicit, auditable workflows. But the longer-term vision extended beyond operations: a trusted data platform for durable animal records, verifiable genetic reports, organization and provider networks, search and identity resolution, and privacy-aware analytics.

Designing for that future without over-building in the present required clear architectural decisions about what constituted the durable core — the data and workflow layer that would remain stable as the platform expanded — and what was legitimately deferred. The platform also needed to support multiple species from the start without creating species-specific modules that would multiply maintenance burden as the animal domain grew.

The approach

We began with a domain model review and platform architecture design phase. The foundational question was what this platform actually managed at its core: not tests or samples or orders in isolation, but durable animal records — identity, ownership history, organization relationships, genetic facts, and report history — that would persist and accrue value over the lifetime of the platform.

From that foundation, we designed the operational LIMS layer as explicit workflow states rather than scattered status fields: case intake, sample receipt, queue assignment, result review, result release, and report publication each as defined, auditable transitions. The report model was designed for verifiability from the start — every published report versioned, signed, and independently verifiable via a verification ID and QR-backed URL.

The multi-species requirement was addressed architecturally through a generic genetics core: species as configuration data, not separate modules, so the same processing engine could support canine, feline, equine, bovine, and future species without code duplication. We designed the data model to hold genetic facts (health markers, parentage, ancestry, relationship classifications) as structured, species-aware records that could support downstream search, match, and breeding analytics.

The engagement concluded with documented architecture decision records, a phased implementation roadmap, and a platform design that separated the lab operations foundation from the longer-horizon platform capabilities in a way that allowed the team to build confidently in the near term without foreclosing future extensibility.