Where meaning is mission-critical.
We go deep in a few domains where the data is fragmented, the rules are complex, and the cost of a wrong answer is measured in more than money.
Healthcare
In healthcare, a supply-chain gap isn't a missed KPI — it's a patient-safety event. Yet the data that would predict it is scattered across procurement, ERP, clinical, and supplier systems that don't share a definition of a single product or facility.
We build the meaning layer that unifies them: one graph where a product, its substitutes, its suppliers, and the facilities that depend on it are modelled explicitly — so the system can anticipate disruption instead of reporting it after the fact.
- Supply-chain orchestration from procurement signal to clinical availability
- Shortage prediction and substitute recommendation grounded in real relationships
- Supplier-resilience and disruption-propagation modelling
- Grounded operational assistants for supply and pharmacy teams
Life sciences
Research, regulatory, and commercial functions each hold a piece of the truth in incompatible systems and documents. Decisions get made on whichever slice is nearest to hand, with no traceable line back to evidence.
We connect those pieces into one traceable model, so an answer about a compound, a study, or a market carries its provenance with it — the difference between insight you can act on and a claim you have to re-verify.
- Unified knowledge graph across research, regulatory, and commercial data
- Provenance-first retrieval for regulated, auditable answers
- Semantic search over trials, evidence, and documentation
- Governance and lineage designed for compliance from day one
Supply chain
Logistics, supplier, and inventory signals live in fragments, so most supply-chain analytics describe what already went wrong. The relationships that would let you see disruption coming are never modelled.
We turn those fragments into a behavioural graph of the network — how shortage signals propagate, where resilience is thin, which dependencies are hidden — so the system anticipates rather than reports.
- Network-wide graph of suppliers, sites, products, and dependencies
- Disruption-propagation and single-point-of-failure detection
- Scenario reasoning grounded in real network structure
- Agentic operations for planning and exception handling
Working in one of these domains?
Bring the problem your current tools can't model. We'll show you what a meaning layer would change.
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