New product development in food manufacturing has always been slow, iterative, and expensive. Formulation teams test dozens of variations, measure sensory performance, adapt to ingredient availability, and try to anticipate how a product will perform with consumers months after launch. Those cycles are shrinking, but the expectations placed on R&D teams continue to grow.
Canadian companies are now using AI to cut through some of that uncertainty. Not to replace food scientists, but to give them better information before they commit to expensive prototypes and long development cycles.
Predictive Insight Before a Product Ever Hits the Shelf
Consumer tastes shift fast. Input prices change even faster. Every category is crowded with competing claims. Getting a product "right" isn't only about nailing the flavour or texture. Brands need to understand whether there's actual room in the market for what you're making. Saskatoon-based startup BetterCart Analytics is making that easier
BetterCart has built a machine learning system that tracks Canadian retail pricing and product assortments in real time. Their platform spots whitespace opportunities and competitive pressure points across categories before you've mixed a single batch.
Instead of relying on anecdotal competitive scans or retailer feedback gathered late in the process, BetterCart gives product teams a data-informed view early. That allows for more focused formulation work and avoids pursuing concepts that don’t have market viability from the start.
Digital Sensory Modelling and Faster Iteration
Traditional sensory testing is thorough but painfully slow. It involves panels, coordination, and multiple rounds of samples. Small changes in moisture or fat content can send product teams back to square one.
Toronto’s Genuine Taste uses AI to accelerate this process and address one of the most difficult challenges in plant-based and cell-based food development: reproducing the flavour of animal fat. The company is developing an AI model that predicts optimal culture media formulations for growing cultivated fat with specific sensory attributes.
Instead of running multiple rounds of expensive wet-lab experiments, Genuine Taste simulates flavour outcomes digitally. This reduces trial-and-error, accelerates R&D cycles, and helps developers understand how changes in media formulations will shape the taste of the final cultivated fat product
The technology is aimed at improving flavour realism in alternative meats and pet foods, where authentic fat characteristics remain a major barrier to consumer adoption.
Earlier Insight, Fewer Blind Spots
BetterCart and Genuine Taste are solving very different product development problems by using AI to surface information that was previously inaccessible or prohibitively slow to obtain. BetterCart turns an enormous, constantly shifting retail landscape into structured signals about where a product can realistically compete. Genuine Taste applies modelling to a part of R&D that has always been guesswork: how an ingredient will taste before it has been produced
Both reduce the “unknowns” that usually define early product development. Instead of building prototypes or commissioning research to find the limits of a concept, companies can see those limits upfront. AI is doing much more than speeding up work. It’s changing the order in which the work becomes possible.
Download the Full Whitepaper: AI and the Future of Canada’s Food Sector
See how Canadian companies like BetterCart Analytics and Genuine Taste are applying AI to product development, automation, food safety, supply chains, and waste reduction.
Our latest whitepaper will help you understand the obstacles to adoption, the competitive advantages at stake, and the policy supports needed to strengthen Canada’s food innovation ecosystem.
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