FMCG and retail operators often rely on manual or legacy analytics that produce inaccurate demand forecasts, leading to stockouts, excess inventory, and suboptimal pricing decisions. These inefficiencies erode sales velocity and margin performance across highly competitive product categories. Harmony AI delivers a cloud‑native, AI‑powered platform that transforms raw sales, POS, and supply‑chain data into actionable forecasts and optimization recommendations.
Funding
Funding not disclosed
Founders
Product
Problem
FMCG and retail operators often rely on manual or legacy analytics that produce inaccurate demand forecasts, leading to stockouts, excess inventory, and suboptimal pricing decisions. These inefficiencies erode sales velocity and margin performance across highly competitive product categories.
Solution
Harmony AI delivers a cloud‑native, AI‑powered platform that transforms raw sales, POS, and supply‑chain data into actionable forecasts and optimization recommendations. The suite includes modular engines for demand forecasting, SKU profiling, product recommendation, price and markdown optimization, stock balancing, and product‑affinity detection. Each engine leverages advanced machine‑learning techniques such as deep time‑series models, gradient‑boosted trees, and reinforcement‑learning‑based pricing policies. Results are exposed via RESTful APIs and an interactive web dashboard, enabling seamless integration with ERP, WMS, and e‑commerce systems. By automating inventory and pricing decisions, retailers can reduce stockouts, lower carrying costs, and improve gross margin without expanding headcount. The platform scales from single‑store pilots to enterprise‑wide rollouts, with usage‑based licensing that aligns cost to value delivered.
Target Audience
Primary customers are FMCG manufacturers, large retail chains, and e‑commerce operators that manage extensive SKU assortments and require data‑driven inventory and pricing control. Category managers and supply‑chain analysts are the main end‑users of the platform’s insights.
Features
- Demand Forecasting engine using multivariate time‑series models that ingest POS, promotions, seasonality, and external signals (weather, events) to produce SKU‑level forecasts with confidence intervals.
- SKU Profiling module that clusters products based on sales velocity, margin contribution, and substitution patterns, supporting dynamic assortment planning.
- Product Recommendation engine powered by collaborative filtering and content‑based similarity to surface cross‑sell and upsell opportunities in real time.
- Price and Markdown Optimization tool employing reinforcement learning to simulate price elasticity and recommend optimal price points across channels.
- Stock Balancing optimizer that aligns replenishment orders with forecasted demand, safety stock policies, and shelf‑space constraints to minimize overstock and stockouts.
- Product Affinity detection using graph‑based analytics to identify complementary and substitution relationships for bundle creation and shelf layout.
- Open APIs and pre‑built connectors for ERP, WMS, and e‑commerce platforms, plus a role‑based web UI for scenario analysis and KPI monitoring.