What is deterministic AI in retail?
Deterministic AI for retail separates calculation from language generation. Business engines calculate sell-through, stock cover, margin, concentration, footprint, compliance readiness or scenario outputs from defined inputs and formulas. The AI identifies the relevant calculation, interprets its result and explains the next decision.
Why not ask the language model to calculate everything?
A language model is useful for intent, synthesis and explanation, but core operational numbers should be reproducible. If two users ask the same question over the same data, the underlying metric should not change with wording. Defined formulas also make missing fields, assumptions and sensitivity visible.
Which calculations belong below the agent?
- Sell-through, sales velocity, stock cover and stock-out exposure.
- Gross margin, markdown depth, margin erosion and price scenarios.
- Size-curve gaps and reorder quantities by size.
- Supplier and country concentration, including HHI.
- Material composition, screening footprint and circularity signals.
- Product-data completeness and regulatory readiness.
What should the AI add?
The agent turns an array of metrics into a diagnosis: what changed, which products drive it, whether the evidence is sufficient, what trade-off matters and which action should be considered first. It should link conclusions back to the calculation and source date.
How does Cycle apply this?
Cycle Copilot calls product, supply, sales, impact and compliance engines rather than improvising their outputs. Its what-if views recompute scenarios from explicit levers. When the required historical or comparable data does not exist, the intended behaviour is to disclose that limitation instead of manufacturing a benchmark.