A context layer for life sciences.
NeuraTrace converts fragmented operational records into a connected, evidence-backed representation of your product and its lifecycle. Systems → graph → context → AI → decisions.
A shared model of the life-sciences domain.
The entities that define a product and its lifecycle, modelled once and shared by every application built on top.
Relationships turn records into context.
A list of entities is a schema. The relationships between them are what make reconstruction possible.
└── Batch
├── uses → Material Lot
│ └── supplied by → Supplier
│
├── manufactured on → Equipment
│
├── tested by → Test
│ └── governed by → Specification
│
└── affected by → Deviation
└── investigated through → Investigation
Every node knows where it came from.
This is where a life-sciences graph stops looking like generic knowledge-graph software.
Ask a question. Reconstruct the relevant subgraph.
"Show everything relevant to Batch FP-2411-A." NeuraTrace traverses the graph and returns only what bears on that decision — with provenance attached to every node.
Give AI context before asking it to reason.
LLMs should not be responsible for discovering enterprise truth from scratch. NeuraTrace assembles the evidence and relationships first; AI operates on that trusted context.