The Impact
41.7
x
ROI projected for every $1 invested in Unframe
2,000
+
SKUs monitored daily across 143 stores
15
days from POC to production deployment
AI ROI: 2026 Benchmarks
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Dorothy Gaynor is a leading Mexican footwear and accessories retailer with over 40 years in business, operating hundreds of stores across shopping malls throughout Mexico and managing thousands of SKUs across shoes, bags, and accessories. Part of the Haber Holdings portfolio, the company is known for its fast-moving inventory, frequent product launches, and a highly seasonal business driven by Mexico's distinct weather patterns. Dorothy Gaynor is a powerhouse leader in retail with enterprise-scale data complexity.
Dorothy Gaynor’s commercial and planning teams had extensive data but limited actionable insight. With more than 2,000 SKUs across 143 stores, buyers manually reviewed sales and inventory reports each week. In practice, they could realistically review only the first five or six pages of top sellers, leaving much of the catalog without consistent oversight.
This limitation led to missed replenishment signals. In one example, a new shoe sold through 45 percent of its initial inventory within the first month. That sell-through rate indicated strong demand and should have triggered immediate replenishment. The reorder did not occur in time, resulting in stockouts and lost sales momentum.
Inventory allocation inefficiencies compounded the issue. Some stores were underperforming on specific SKUs while others were selling out, yet these imbalances were not consistently identified early enough to prevent excess transfers. As a result, Dorothy Gaynor was spending between $40,000 and $50,000 per month moving unsold inventory backward between stores and warehouses, often handling the same product multiple times.
Existing tools surfaced raw data but did not provide recommendations. As one stakeholder stated, “We want some AI that really does something productive and pays for itself.” The team needed a system that could continuously monitor the full assortment and translate data into clear, prioritized action.
Unframe deployed an AI-powered inventory intelligence solution that connects securely in read-only mode to Dorothy Gaynor’s MS SQL environment.
The platform continuously analyzes daily sales, inventory levels, and open purchase orders, delivering a prioritized morning brief that highlights SKUs requiring attention, including reorders, emerging high performers, and slow-moving inventory.
Through a natural language interface, including Spanish-language queries, team members can ask direct questions about SKU performance, store-level trends, and lifecycle dynamics and receive traceable answers supported by data tables and visuals. The system provides SKU- and store-level intelligence such as sales rankings, inventory health indicators, phantom stock detection, and competitor proximity analysis.
Reorder recommendations include suggested quantities and size breakdowns based on historical sales velocity and lead times, while dynamic pricing signals flag products eligible for price adjustments. Business rules such as lead times, reorder thresholds, and critical product definitions are fully configurable by the Dorothy Gaynor team. The platform operates alongside existing ERP systems, strengthening visibility and accelerating decision-making without disrupting core infrastructure.
Speed to value was a decisive factor. From the initial deep-dive use case call to a working proof of concept, the timeline was measured in weeks rather than quarters. The urgency was clear: “Time is money and we would like to have this working as soon as possible.”
Dorothy Gaynor also valued real business outcomes over theoretical demonstrations. Unframe’s POC-first approach enabled the company to validate return on investment using its own data before committing to scale.
Unframe did not position itself as a retail consultancy, but instead brought deep AI and solutioning expertise. While not retail operators by background, Unframe was able to design a highly effective retail inventory solution by combining AI capabilities with a structured, collaborative scoping process grounded in Dorothy Gaynor’s real workflows and data. Control and configurability were equally important. Dorothy Gaynor retained the ability to adjust parameters such as reorder triggers, lead times, and critical product definitions independently, without relying on external support, ensuring the solution could evolve with the business.
Finally, scalability across Haber Holdings played a strategic role. Dorothy Gaynor became the blueprint for broader deployment, with an architecture designed to replicate the solution across additional retail entities with minimal incremental effort.
