Hetal Retail uses in-store shopper video and Computer Vision AI to audit shelf compliance and identify merchandising issues with visual proof. This data allows brands to optimize field execution by prioritizing high-ROI store visits and deploying budgets effectively. By integrating shelf insights with sales data, companies can accurately measure sales lift from merchandising changes and improve category management decisions.
Funding
Funding not disclosed
Founders
Product
Problem
Consumer packaged goods (CPG) brands and retailers often lack real‑time, objective visibility into shelf execution, leading to missed out‑of‑stock events, planogram non‑compliance, and inefficient allocation of merchandising resources. Without visual evidence, it is difficult to prioritize store visits, quantify the impact of shelf changes, or link execution gaps to sales performance.
Solution
Hetal Retail captures in‑store aisle footage through a distributed network of shoppers and applies computer‑vision algorithms to automatically detect execution issues such as out‑of‑stock SKUs, misplaced facings, and planogram deviations. An AI‑driven prioritization engine scores each issue by estimated revenue impact, surfacing the highest‑ROI stores for targeted interventions. The platform attaches timestamped visual proof to each detected problem and synchronizes the execution data with the brand’s sales metrics, enabling analysts to isolate the drivers of incremental revenue. Users can monitor compliance trends, generate actionable work orders for merchandisers, and evaluate the sales lift attributable to specific shelf improvements through an integrated dashboard.
Target Audience
Primary customers are CPG manufacturers, brand managers, and retail category teams that need granular shelf‑level execution intelligence to optimize merchandising spend and demonstrate sales lift. The solution also serves field merchandisers and retail operations groups responsible for on‑site compliance enforcement.
Features
- Continuous video capture from a crowdsourced shopper network, eliminating the need for dedicated store‑level hardware
- Deep‑learning models that identify out‑of‑stock, misplacements, and planogram violations at the SKU level with >95% accuracy
- AI‑based scoring that ranks issues by projected sales impact, allowing teams to focus on high‑ROI stores first
- Automatic attachment of high‑resolution images/video as visual proof for each flagged issue
- Seamless data integration layer that merges execution insights with POS sales data for causal analysis
- Interactive web dashboard with heat‑maps, trend analytics, and exportable reports for category managers and merchandisers
- Real‑time alerting via email or API webhook to trigger immediate field actions
- Role‑based access control and audit logging to meet retail compliance and data‑privacy standards