Tastewise

Tastewise is a New York company, founded in 2018, that calls itself the agentic system of intelligence for food and beverage. Rather than adapting a generic AI to food, it built a purpose-built data engine first: a food intelligence graph spanning menus from more than 1.4 million restaurants across sixty four recipe markets, over twelve thousand retail brands, social signals from ninety million consumers, foodservice coverage of four million plus locations, a proprietary five hundred thousand person consumer panel, and licensed third party datasets, all refreshed continuously. On top run agents trained exclusively on food and beverage behavior, and the company reports that eighty percent of the world's largest F&B brands, including PepsiCo, Kraft Heinz, Nestlé, Mars, and Kroger, use it for high-stakes innovation and sales decisions.

The agent roster is concrete and prebuilt: a Marketing agent for campaign-ready stories, a Sales agent for retailer sell-in narratives, an Insights agent answering business questions with cited data, a Data agent that pulls, joins, and normalizes slices of the graph on demand with methodology notes attached, plus Whitespace explorer, Flavor forecast, Competitor insights, Concept, and Formulation agents. Custom agents go from intake to production in two to four weeks. Governance is a selling point: raw signals pass through validation, confidence scoring, and a human evaluation layer, with more than three hundred thousand hours of human evaluation logged, and every agent run is traceable, every output citable, and every decision auditable, an enterprise posture the company says customer AI committees have cleared. Intelligence is also available directly inside ChatGPT, Claude, and Copilot without copy-paste, and Power BI connects for dashboards.

Tastewise sits in the data analyst lane with a strong GTM orientation, turning demand evidence into sell-in stories, innovation briefs, and campaign assets, and a Tastewise Growth product prospects foodservice buyers on real-time restaurant signals. It is a strong fit for CPG, foodservice, and retail teams that want evidence-backed, citable answers grounded in real consumption data, and a weaker fit for buyers needing self hosting or model choice, neither of which is documented.