Clinical AI that shows the guideline behind every recommendation
A clinically wrong AI output is a patient-safety event. Archisus encodes treatment protocols, formularies and patient-safety rules as constraints, so contraindicated recommendations are blocked and every recommendation is traceable to the guideline that produced it.
The challenge
"The AI recommended it" is not a defence
For hospital groups, providers, pharmaceutical companies and trial sponsors, a recommendation needs a traceable evidence base. Probabilistic output alone does not meet that bar.
Patient safety
Contraindications must be caught before prescribing, not flagged after dispensing.
Evidence
Every clinical recommendation needs the guideline it rests on.
Regulatory submissions
Dossiers require complete evidence traceability across trial data.
Use cases
Where Digital Experts help
Clinical pathways are logic and formulary rules are constraints; the reasoning engine enforces both.
Clinical decision support
Live patient data cross-referenced against the treatment-protocol ontology and the drug-interaction graph; contraindications are blocked by SHACL constraints.
Drug interaction checking
An interaction knowledge graph evaluated against patient medication records in real time, before prescription.
Regulatory submission assembly
Trial data reasoned over against submission requirements to assemble FDA and EMA dossiers with full evidence traceability.
Clinical audit & revalidation
Clinical audit evidence packages generated from the patient and protocol knowledge graph.
What changes
What changes for clinical teams
- Safer prescribing, with interactions caught before the prescription
- Faster clinical decisions with the rationale attached
- A traceable basis that satisfies auditors and clinicians
- Submission evidence assembled from the knowledge graph
- Patient data kept on-premise or in your private cloud
- Protocol updates applied once, everywhere they are used
Questions
Frequently asked questions
Does it make clinical decisions on its own?
No. It supports clinicians: recommendations come with the guideline and data behind them, and anything that breaks a constraint is blocked and escalated to a person.
Do we need to move our data to a new platform?
No. The federated layer queries your existing systems where they are, such as SAP, SCADA historians, ERPs and document stores. Only query results move, inside your own perimeter, and the models run on-premise or in your private cloud.
We already use an LLM or Copilot. Does this replace it?
No. The ontology layer works with whichever language model you use. The model handles language; the ontology and reasoning engine check its outputs against your rules before they are released.
How do we start?
With the Business DNA Audit: a fixed two-week engagement that maps the rules of one domain, reports the gaps and contradictions, and gives you a draft ontology and a ranked pilot roadmap. There is no commitment beyond the audit.
The DNA Audit Is Your First Step. And Your Lowest Risk.
In two weeks we map every critical reasoning dependency, broken data link, and governance gap in your current AI or data workflow. You leave with a draft ontology model, a prioritised use case roadmap, and a clear decision on whether - and how - to proceed. No commitment beyond the audit.
Knowledge architecture map
Every logic dependency, data relationship, and constraint your AI needs to reason correctly - visualised and documented.
Gap & risk report
Broken data links, contradictory rules, and governance gaps that are invisible today - surfaced and prioritised by business impact.
Draft ontology & pilot roadmap
A working ontology model for your highest-priority domain, plus a ranked shortlist of pilot use cases with estimated ROI and delivery timeline.
Book Your DNA Audit
2-week engagement · Fixed scope · No ongoing commitment required
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