Agentic AI System for Operational Decision Workflows
Markdown and promotional pricing decisions sit at the intersection of commercial strategy, inventory management, and competitive positioning. When made well and at the right time, they protect margin, clear excess stock efficiently, and sustain customer relationships. When made poorly — or made late — they destroy value through unnecessary discounting, missed clearance windows, and inconsistent execution across the range.
In many businesses, these decisions are made through a process that becomes a constraint as scale increases. The workflow centres on manual review of spreadsheets populated from multiple data sources — current stock positions, recent sales velocity, competitor pricing, promotional calendars, and margin thresholds. Analysts consolidate this information, apply judgement, and produce recommendations that move through an approval process before being actioned.
The process is slow. By the time a recommendation reaches the point of implementation, the trading conditions that prompted it have often shifted. It is inconsistent, because the judgements applied to similar situations vary depending on who is conducting the review and what information they have assembled. And it does not scale — as the product range grows and the pace of trading increases, the volume of decisions outpaces the capacity of the team to make them.
The data required to make good decisions typically exists within the business. The analytical capability required to interpret it typically exists within the team. What is absent is the infrastructure to bring these together at the speed and scale the business requires.
We design and build an agentic AI system capable of ingesting live trading data, applying configurable business logic, and surfacing structured recommendations for markdown and promotional action — with sufficient transparency and control for commercial teams to trust, interrogate, and override the output.
The system connects to the business's live data environment, drawing on current stock levels, sales velocity by SKU, days of cover calculations, margin positions, and pricing data. It monitors this information continuously, identifying products that meet the criteria for promotional or markdown action according to rules defined by the commercial team.
The rule framework is configurable and auditable. Business logic — clearance thresholds, margin floors, category-specific parameters, timing constraints around promotional periods — is encoded explicitly rather than learned implicitly, ensuring that the system's behaviour is transparent and can be adjusted as commercial strategy evolves. This is a deliberate design choice: the system is not a black box, and its recommendations can be traced back to the specific inputs and rules that produced them.
Where the decision logic requires synthesis across multiple variables — for example, balancing the urgency of clearance against the margin impact of a given discount level — the system generates a ranked recommendation with supporting rationale. The commercial team reviews these recommendations within a purpose-built interface, with the ability to approve, modify, or decline each one and to record the reasoning behind departures from the system's suggestions.
This human-in-the-loop architecture is central to the design. The system's role is to eliminate the manual data assembly and routine analysis that consumes the majority of the team's time, freeing attention for the decisions that genuinely require commercial judgement.
The time required to move from trading signal to actioned decision falls from days to minutes. The commercial team operates from a position of continuous visibility rather than periodic review, and the decisions they make are grounded in current data rather than information assembled hours or days earlier.
Consistency improves substantially. Because the same analytical framework is applied uniformly across the product range, similar situations produce comparable recommendations, and the variation that arises from differences in individual analyst approach is materially reduced.
The team's capacity to manage decisions at scale increases without a corresponding increase in headcount. As the product range grows, the system absorbs the additional analytical load, allowing the business to maintain decision quality across a broader range without the process becoming a bottleneck.
This is the practical value of agentic AI designed with operational reality in mind — systems that are fast, transparent, and built to work alongside human judgement rather than to replace it.
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