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Tag: generative ai

  • Estée Lauder Partners with Profound to Track Brands Across Generative AI

    Estée Lauder Partners with Profound to Track Brands Across Generative AI

    The Estée Lauder Companies has partnered with AI marketing platform Profound to monitor and optimize how its beauty portfolio appears across generative artificial intelligence platforms.

    Earlier this summer, industry leaders highlighted a new visibility battle for beauty brands as consumers increasingly consult 2 leading AI assistants, ChatGPT and Gemini, for product comparisons and personalized beauty advice.

    Through the collaboration, Estée Lauder gains a consolidated view of how its brands are represented across major AI engines worldwide, deploying Profound’s tools to ensure conversational recommendations align with product positioning.

    Tracking Generative Engine Optimization

    Retail marketing is shifting rapidly from search engine optimization to generative engine optimization. Traditional search engines return ranked links based on keywords, indexing and paid placements. Conversational models work differently. They synthesize answers from training data and live retrieval, producing direct recommendations without standard search results.

    Profound co-founder and chief executive James Cadwallader noted that brand owners must actively monitor how algorithmic systems describe their products to shoppers. The software scans model outputs across regions to flag inaccurate claims, missing product lines and unapproved comparisons.

    “The way people discover beauty is being rewritten in real time, and we intend to shape that shift rather than reacting to it,” said Aude Gandon, global chief digital and marketing officer at The Estée Lauder Companies.

    Shifting Digital Spend From Keyword Search

    Beauty conglomerates face higher customer acquisition costs across standard digital channels as retail media networks fragment and search traffic slows. Deploying optimization software lets brand managers identify which datasets feed recommendation models.

    That transition creates operational friction for teams accustomed to bidding on static keywords. Generative models update retrieval patterns frequently. As a result, visibility is harder to predict and measure across regional variations in product nomenclature.

    Dynvibe chief executive Anne-Cécile Guillemot characterized the rise of conversational discovery tools as a new visibility battle for consumer brands. She noted that companies must rebuild how they structure product data and publish ingredient documentation.

    Monitoring Brand Accuracy Across Platforms

    Estée Lauder will direct its portfolio teams to standardize product attributes, clinical claims and application guidance across databases that large language models crawl. The initiative covers the group’s global stable of skincare, makeup, fragrance and hair care labels.

    Consumer adoption of generative search interfaces has spread across North America, Europe and Asia over the past two years. Beauty remains one of the most queried retail categories on conversational platforms. Shoppers frequently seek advice on complex ingredient combinations and personalized skin routines.

    Looking ahead, the two companies will integrate regional language queries into the monitoring platform as AI engines expand local conversational capabilities.

  • FamilyMart Plans 10 AI-Developed Desserts by End of Fiscal Year

    FamilyMart Plans 10 AI-Developed Desserts by End of Fiscal Year

    FamilyMart will release its first generative AI-developed dessert in Japan later this month. The company aims to roll out roughly 10 algorithm-assisted products before the current fiscal year ends.

    Store buyers want faster development cycles for fresh sweet counters. They are using machine learning to parse consumer demand patterns and speed recipes straight to shelves.

    Building on July Pilot Sales

    Executives cleared the wider rollout after a limited run in July. Strong customer uptake for an AI-recommended cream sandwich during that trial convinced management to expand the pipeline across the chilled dessert category.

    That push slashes the months normally required to pitch, test and approve new confectioneries for nationwide distribution.

    Shifting Shelf Battles in Tokyo

    Japanese convenience chains compete fiercely on chilled sweets, where weekly turnover drives regular foot traffic and protects basket margins against inflation. Rivals such as 7-Eleven and Lawson pour heavy investment into proprietary foods. Generative design cuts development lead times down to weeks.

    Suppliers face tighter turnaround windows as retail buying teams swap conventional seasonal planning for continuous, data-driven formulation. Manufacturers will need flexible production lines capable of switching ingredients rapidly to meet shorter product life cycles.

    Next Pipeline Milestones

    September’s launch sets the operational template for the remaining automated recipe rollouts scheduled across the rest of the financial year.

    FamilyMart will track repeat purchase rates and sales velocity from this month’s debut before locking in the final batch of AI recipes due on shelves before the fiscal year closes.

  • FamilyMart Launches AI-Designed Desserts to Speed up Product Cycles

    FamilyMart Launches AI-Designed Desserts to Speed up Product Cycles

    Japanese convenience store operator FamilyMart will roll out its first dessert formulated by generative artificial intelligence in September 2026, aiming to cut product development workloads by 20 per cent.

    The Tokyo-based chain is deploying algorithm-driven recipe creation across its chilled dessert counters to capture fast-moving consumer tastes faster than human product teams can test them.

    Automating Recipe Formulation

    FamilyMart is using generative AI platforms to analyse consumer social discussions and purchase patterns, generating finished dessert recipes directly from raw trend data. The software evaluates ingredient combinations, texture profiles, and flavour pairings before human development teams test the formulations for mass production.

    Sweets and chilled bakery items generate disproportionate store traffic compared with daily staples like rice balls and bento meals. Convenience store operators in Japan rotate novelty desserts every few weeks, creating intense pressure on research teams to find fresh flavour profiles that stand out on shelves.

    Rival Pressures Across Japanese Convenience Chains

    Convenience retailers across Japan face shrinking domestic margins and high labour costs, pushing operators to automate back-office operations and supply chain decisions. Seven & i Holdings and Lawson have similarly turned to algorithmic demand forecasting, but using automated systems directly for consumer recipe design shifts technology from logistics into core merchandising.

    For food ingredient suppliers and packaging manufacturers, shorter development timelines will require more agile production lines. Suppliers that cannot adjust ingredient batches within tight AI-driven launch schedules risk losing shelf space to more flexible competitors.

    The Timeline for Store Rollouts

    FamilyMart has spent recent quarters testing automated shelf-stocking tools and digital signage across its store network to trim operational overhead. Applying generative technology to food product design represents the next stage of that operational overhaul.

    The company will track sales performance and inventory waste for the September 2026 AI dessert before deciding whether to expand automated development into its wider bento and prepared beverage lines.

  • Tripo AI Raises 3 Billion Yuan in Series B and B+ Rounds

    Tripo AI Raises 3 Billion Yuan in Series B and B+ Rounds

    Tripo AI has raised 3 billion yuan across combined Series B and Series B+ funding rounds this financial year to scale its generative 3D foundation models and commercial toolsets.

    This capital injection gives the company one of the largest war chests among Chinese artificial intelligence startups focused on automated spatial asset creation.

    Scaling 3D Asset Generation

    Users can convert text descriptions and two-dimensional images into production-ready 3D meshes within seconds. That speed cuts digital modeling time from days to minutes. It also directly lowers production expenses for game developers, virtual retail designers, and industrial visualization teams.

    Capital from the combined rounds will fund compute infrastructure and dataset acquisition. The money will also back larger engineering teams focused on multi-view reconstruction algorithms.

    Commercial Pressure on Digital Studios

    For consumer brands and e-commerce merchants building virtual storefronts, rapid 3D generation removes a persistent cost bottleneck. Traditional digital catalog creation requires manual sculpting and texture mapping for every stock keeping unit. Automated mesh generation shifts that workflow toward batch processing, forcing regional digital agencies and outsourced modeling studios to adjust their pricing structures.

    Adoption speed and export limits present the main risks. Consumer software platforms adopt synthetic assets quickly. Enterprise retail and manufacturing clients, however, demand strict geometric precision and clean topology that generative models still struggle to deliver without manual touch-ups.

    Prior Traction and Next Milestones

    Earlier funding rounds allowed Tripo AI to roll out browser-based generation tools and integrate application programming interfaces with major graphics engines. The platform processed millions of user queries over previous product cycles, building an initial base among independent creators and digital design shops.

    Looking ahead, management will focus on rolling out enterprise tier subscriptions and expanding direct integrations with global rendering pipelines before the end of the financial year.