Elastly vs Invisible Hand Technologies

Elastly is an AI pricing platform for retailers, manufacturers, and distributors. It recommends prices using costs, demand, competitor data, and each product’s sales history, then recalculates as those conditions change. Recommendations show contributing factors such as elasticity, competitor moves, inventory, and seasonality. Teams can review proposed prices and set automation rules, margin floors and ceilings, and limits on price changes. Approved prices can be sent to sales channels automatically; Elastly says they can go live in under 60 seconds after approval. Native connectors include Shopify, Salla, SAP, NetSuite, QuickBooks, Xero, Adobe Commerce, BigCommerce, WooCommerce, Salesforce, Amazon, and Walmart. Developers can use a REST API, webhooks, and SDKs for TypeScript, Python, PHP, Java, and Go. The platform is available on the web and through an API. A 3-day free trial requires no card, and the site says first recommendations are available within 48 hours. Paid pricing is listed from $41/mo (annual). Growth includes two connectors and manual approval; Scale adds unlimited connectors and auto-apply within guardrails. Enterprise pricing is custom.

Invisible Hand Technologies is a web platform for consumer brands and CPG manufacturers that turns store-level retail data into pricing and promotion recommendations. It models demand by SKU, retailer, and local market, then simulates outcomes before recommending price changes. Promotion analysis separates incremental sales from subsidized volume to identify value-creating or margin-destroying activity. Root-cause analysis traces changes in sales, margin, or share to price, promotion, distribution, availability, or competition, down to individual stores. The platform monitors competitor pricing and promotion trends and shows out-of-stock conditions across retailers carrying a customer’s products. Its machine-learning engine combines store-by-store price, promotion, and availability data collected up to daily with internal, market, and third-party data. Recommendations appear in dashboards, alerts, and inspectable plain-language answers from an AI assistant. Users can question what is happening, why, and what to do next, and challenge the reasoning.