A retail decision layer for omnichannel operations
A governed intelligence and action layer that preserves existing systems of record while giving leadership one place to detect issues, compare options, and approve a response.
This entry does not name a real client. It is labelled “Representative scenario” and should be read accordingly.
Business context
Leadership and operating teams work across ERP, commerce, stores, warehouse, customer, and finance platforms. Important signals exist across these systems, but decisions and actions currently travel manually between them.
Problem
A supplier delay or demand spike can take days to surface as a coordinated business decision, by which time the commercial opportunity or risk has often already passed.
Constraints
- Existing ERP and commerce platforms were to remain systems of record
- Any automated recommendation required explicit human approval before action
Existing systems
- ERP
- Ecommerce platform
- Store point-of-sale
- Warehouse management
- Finance system
What Anav did
- Mapped the operating reality across merchandising, inventory, and finance decisions
- Designed a decision layer that surfaces evidence and options without replacing systems of record
- Built role-based assistants for merchandising, supply, and finance questions
- Defined explicit human-approval boundaries for every recommended action
Solution view
A cross-system decision layer sits above existing platforms, aggregating signals, modelling impact, and presenting a small number of evidence-backed options for human approval before any action is taken.
Human & governance controls
- Every recommended action requires named human approval before execution
- Confidence and evidence sources are shown alongside every recommendation
- Full audit trail of signal, recommendation, and approval decision
Delivery stages
- Operating discovery and evidence mapping
- Working prototype tested against real scenarios
- Staged build across merchandising, supply, and finance modules
- Controlled rollout with human-approved actions only
Outcomes
Illustrative time-to-decision improvement
Evidence source: Representative scenario, not a measured client result
Technologies used
- Large language model APIs
- Cloud data platform
- Existing ERP and commerce APIs
What remains to be proven
- Real-world adoption and time-to-decision improvement have not yet been measured with a live client
- Long-run model drift and evaluation approach require ongoing monitoring once deployed
Bring us the operating problem.
Not a brief, not a platform shortlist — the decision that arrives late, the workflow held together by people, the AI question without a clear answer. That is enough to start.