— Solution · Agritech →

Agritech

Seed traits, formulations and field-trial samples on the same connected record research uses today — defined by your own ontology, not built around pharma.

01Trait & formulation R&D
02Trial sample management
03Analytical testing
04Production batch record

The graph doesn’t know it’s pharma

Holonyx’s data model is a user-defined ontology, not a pharmaceutical schema with agritech fields bolted on. What you track — a seed line, a trait, a formulation, a field trial — is described once, and the registry, inventory, chemistry and analytical mechanisms already built into the platform apply to it exactly as they do to a drug substance.

A formulation’s stoichiometry, a residue analysis run on a chromatography instrument, a batch record for a production lot — these are real, generic tools already live in the platform. What runs through them is defined by you.

What’s honest about this page

There is no pre-built agritech ontology, seed-trait template, or regulatory report shipped today — those would need to be built against a real agritech customer’s actual data model, not guessed at here. What’s real is the underlying mechanism: schemas, registry, chemistry and analytical tooling that were built generically, not pharma-only, and a real graph model behind them.

See it on the platform: The graph model · Research · Lab & QC.

In the product

Entity schema builder — A seed line, a formulation, or a trial sample is defined once as a schema — its own fields, its own name — not forced into a pharma compound record.

A registry of entities with the selected entity's details — The registry and inventory mechanism that tracks pharma compounds and lots tracks any physical material the same way — genealogy included.

Chromatogram viewer with detected peaks — Residue and composition analysis runs through the same real instrument-gateway and chromatography pipeline used across the platform.

Environmental monitoring readings linked to a batch — Field and greenhouse trial conditions can be tracked as monitoring data linked to the samples they affected, the same way a cleanroom's readings link to a batch.

Book a walkthrough All solutions →