Retail AI your merchandisers can challenge
Retail runs on the most fragmented data estate and the most opaque AI. Archisus encodes demand patterns, supplier lead times, margin floors and pricing rules in a knowledge graph, so when someone asks why the system ordered 10,000 units, the answer is a rule, not a confidence score.
The challenge
Fragmented data, opaque decisions
POS, ERP, WMS, loyalty and supplier portals each hold part of the truth, and forecasting models give recommendations that merchandisers cannot question.
Black-box forecasts
Replenishment recommendations arrive without a business reason.
Pricing rules
Minimum-pricing obligations, margin floors and promotion rules are easy to break.
Consent
Personalisation must respect each customer's consent and stay auditable.
Use cases
Where Digital Experts help
Each recommendation traces back to the rules that produced it.
Demand forecasting with business rules
Demand patterns, supplier lead times, seasonal constraints and margin floors in one knowledge graph, so every replenishment recommendation is explainable.
Supply chain visibility
ERP, WMS, POS and supplier APIs queried in place, giving cross-silo visibility without a migration project.
Pricing & promotion compliance
Pricing rules, minimum-pricing obligations, margin floors and promotion eligibility as SHACL shapes that block non-compliant price changes before they reach the system.
GDPR-aware personalisation
Customer profiles linked to the ontology with consent as a first-class property, so personalisation can be audited for compliance.
What changes
What changes for retail teams
- Fewer stockouts and better margins
- Every order and price change explainable with a business reason
- Merchandising decisions teams can audit and trust
- Cross-system visibility without moving data
- Pricing obligations enforced before changes go live
- Personalisation with consent tracked in the model
Questions
Frequently asked questions
Does it replace our forecasting models?
Not necessarily. It can govern their outputs, checking each recommendation against your business rules and recording the reason, or reason over demand directly.
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.
Keep exploring
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
Message Sent!
We will be in touch shortly.
