Agentic Storefronts and Universal Commerce Protocol: AI Driven Shopping for Merchants

Shopify’s new Agentic Storefronts and the co‑developed Universal Commerce Protocol mark a decisive step toward making AI assistants not just discovery surfaces but full shopping environments — letting Google’s Gemini, Microsoft Copilot, ChatGPT and other agents find, present and complete purchases while keeping merchants as the merchant of record. The announcements bundle three technical primitives — canonical, machine‑readable product catalogs; delegated, tokenized checkout rails; and conversational orchestration with provenance — into a single merchant‑controlled workflow that is already rolling into pilots and early rollouts.

Background / Overview​

Shopify’s Winter ’26 “Agentic Storefronts” is positioned as a “configure once, distribute everywhere” solution: merchants prepare a single, canonical catalog inside Shopify, add brand voice and policy content, then toggle which AI assistants may read and present their products. Shopify says orders that originate in AI conversations will flow back into the Shopify admin with attribution and full merchant control of fulfillment and returns. The Winter ’26 announcement (December 10, 2025) and subsequent platform updates accelerate that strategy into production pilots and partner rollouts. Concurrently, Shopify and Google unveiled the Universal Commerce Protocol (UCP) — advertised as an open, extensible standard to unify how agents and merchant backends exchange product data, build carts, handle discounts and loyalty, and initiate delegated checkouts. Google has already signalled native shopping integrations for Gemini’s AI Mode and the Gemini app, and independent reporting at the National Retail Federation’s conference confirms major retailers and marketplaces are among the early UCP backers. Microsoft’s Copilot Checkout and OpenAI’s Instant Checkout sit alongside these moves, creating a multi‑platform competition to own the agentic shopping moment. Why this matters now: conversational AI has matured into a discovery surface that can ask clarifying questions, compare products and carry contextual intent across multiple turns — shortening the path from research to purchase. The technical breakthrough enabling in‑chat buying is not a single invention but the disciplined combination of clean product feeds, secure delegated payments, and auditable orchestration. Shopify’s productization of those primitives is intended to make agentic commerce accessible to millions of merchants without bespoke engineering work for each assistant.

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How Agentic Storefronts and UCP Work (Technical Anatomy)​

1. Canonical, machine‑readable catalogs​

At the foundation is rich, normalized product metadata: SKUs, GTINs, images, live inventory, dimensions, shipping options, tax rules, and merchant policies. Shopify Catalog performs deduplication and attribute inference so agents can reliably match intent to product records rather than hallucinating or misattributing items. This canonicalization is the practical antidote to the “broken data chain” that sabotages many early agentic experiments.

2. Conversational orchestration and brand control​

Agents — whether Gemini, Copilot, ChatGPT or Perplexity — run the front‑end discovery and question flows. Brand voice, FAQs and policies pushed from Shopify’s Knowledge Base are exposed so agent responses remain consistent with merchant tone and rules. Microsoft’s Brand Agents and Copilot Studio templates are examples of how platforms let merchants shape responses while retaining conversational context. The orchestration layer asks clarifying questions (size, color, delivery constraints) and surfaces only rows from the canonical catalog that match the answers.

3. Delegated, tokenized checkout​

When a shopper confirms an item, the assistant typically requests a short‑lived checkout token from a payments provider (Shop Pay, Stripe, PayPal, or platform-specific token APIs). That token delegates settlement and fraud checks to the PSP or merchant system while preventing the assistant from handling raw card data. The merchant completes the settlement and remains the merchant of record; order details and attribution flow back into the merchant admin. This delegated payment pattern is the security backbone that makes in‑chat checkout tolerable for regulators and payments firms.

4. Universal Commerce Protocol (UCP)​

UCP is a specification intended to provide a common language for agents and merchant backends: product discovery, cart building, identity/loyalty linking, discount negotiation, checkout initiation and recovery paths for failed steps. The goal is to remove bespoke integrations between every assistant and every merchant, enabling rapid scale across platforms. Shopify presents UCP as extensible: universal primitives plus custom extensions for complex commerce features. Independent outlets reporting on NRF coverage corroborate Google and Shopify’s public push for such a standard.

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What Shopify, Google and Microsoft Announced — The Practical Details​

• Shopify Agentic Storefronts: Merchants set up a single Agentic Storefront, choose channel toggles, and publish a Shopify Catalog feed optimized for agents. Orders from AI channels are attributed in the Shopify admin.
• Universal Commerce Protocol: Co‑developed with Google, UCP is described as an open protocol to handle cart semantics, discounts, loyalty and checkout flows across agent platforms.
• Google / Gemini integrations: Google is rolling UCP‑powered shopping into Gemini (AI Mode and Gemini app) and piloting merchant partnerships for instant, inline checkout without leaving the chat. News coverage and Google partner statements indicate Walmart, Wayfair and others are participating in pilot programs.
• Microsoft / Copilot Checkout: Microsoft’s Copilot Checkout embeds a branded checkout widget inside Copilot conversations; Shopify merchants will be automatically enrolled after an opt‑out window. Copilot Launch partners include PayPal and Stripe. Microsoft also introduced Brand Agents to let merchants shape in‑conversation experiences.
• OpenAI / Instant Checkout: OpenAI’s Instant Checkout program (built on Stripe’s Agentic Commerce Protocol concepts) already supports a set of merchants and has been expanding eligibility to Shopify sellers; its delegated payment model is aligned with the same tokenization patterns.

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Business and Merchant Implications​

Faster conversion, new discovery surfaces​

Agentic commerce can materially shorten the path from intent to purchase by moving discovery, clarification and checkout into a single conversational flow. For merchants with clean product data and competitive fulfillment, agentic channels can provide incremental, high‑intent sessions and higher conversion velocity compared with traditional search or social referrals. Shopify management has publicly described sharp increases in AI‑attributed traffic and orders as early indicators of momentum, though those specific multipliers are company‑reported and require independent validation.

Centralization vs. distribution tradeoff​

Shopify’s pitch — one integration to serve many agents — is attractive for scale. It reduces engineering overhead and lowers the barrier for smaller merchants to appear in AI channels. But it also concentrates gatekeeping on a small number of platforms and protocols, making merchant economics dependent on placement rules, default enrollment mechanics, and fee structures defined outside the merchant’s direct control.

Payments and monetization​

Tokenized delegated checkout is the monetization fulcrum. When Shop Pay or other Shopify-aligned payment rails are used for in‑chat purchases, Shopify captures merchant‑services revenue and valuable signal data. Platforms may also introduce new promotional placements inside chats (advertising or “preferred merchant” slots), creating a new ad surface and potential incremental fees. Merchants should expect negotiation around fees, chargeback rules, and settlement timings to matter materially.

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Risks, Operational Headaches and Governance Concerns​

While the technical blueprint is sensible, execution risks are real and immediate.

• Data quality and catalog hygiene: Agents depend on high‑fidelity metadata. Poor SKUs, mismatched GTINs, inconsistent dimensions, or stale inventory will lead to bad customer experiences and returns. Fixing data at scale is non‑trivial for many SMBs.
• Fraud, disputes and chargebacks: Delegated tokens reduce assistant exposure to card data but transfer settlement and fraud responsibility to PSPs and merchants. Patterns of higher conversion from agents may also correlate with different types of disputes; merchants should expect new fraud‑mitigation rules and possible underwriting changes from payment processors.
• Opt‑out enrollment and default settings: Automatic enrollment (Shopify merchants being auto‑enrolled into Copilot Checkout after an opt‑out window, for example) accelerates reach but raises governance questions. Merchants must watch default toggles, channel visibility rules, pricing display, and the mechanics of withdrawal.
• Fee economics and disclosure: If platforms take placement fees, add checkout fees, or introduce promotional ad pricing inside agents, the net economics for SMB merchants could erode margins. Merchants should negotiate clear, auditable terms and monitor attribution carefully.
• Regulatory and privacy scrutiny: New checkout surfaces and the linkage of identity, loyalty and payment tokens will invite regulator attention. Rules about disclosure (making clear the assistant is acting as a transactional agent), liability allocation, and consumer protections will evolve quickly and may differ across jurisdictions.

Flag: some headline growth numbers quoted in vendor briefings (e.g., 7x AI traffic, 11x AI‑attributed orders) are company‑level, directional claims and are not audited GAAP metrics; treat them as indicative, not definitive, until verified by independent studies or second‑party audits.

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Practical Steps for Merchants: A Checklist​

• Audit and normalize your catalog now
• Ensure accurate GTINs, SKUs, weights, dimensions, images, and live inventory.
• Consolidate duplicate variants and fix common schema errors.
• Publish clear policy and FAQ content
• Use the Knowledge Base or similar tools to expose return rules, shipping windows, and brand voice so agents can answer follow‑ups consistent with your policies.
• Validate checkout and payments flows in a sandbox
• Test delegated token flows, chargeback handling, and settlement timing with your PSP (Shop Pay, Stripe, PayPal).
• Run fraud and dispute simulations.
• Review default channel enrollees and opt‑out windows
• Confirm whether your platform automatically enrolls you in Copilot Checkout, Gemini integrations, or other agents and adjust toggles intentionally.
• Instrument attribution and analytics
• Establish A/B tests to compare agentic channel conversion, return rates, lifetime value, and dispute incidence versus existing channels.
• Negotiate commercial terms and fee transparency
• If platforms offer promotional placement or new ad surfaces, demand audit logs, clear pricing, and attribution guarantees.
• Plan a staged rollout
• Start with a conservative pilot: select a subset of SKUs with robust margins and simple shipping rules.
• Expand based on controlled results and independent measurements.

These steps reduce the risk of surprise costs and poor customer experiences while giving merchants the ability to assess whether agentic channels produce sustainable value.

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What IT, Payments, and Platform Teams Should Monitor​

• Conversion vs. returns/disputes: a high conversion rate that is accompanied by higher return or dispute rates may indicate poor intent quality or catalog‑matching issues.
• PSP rule changes: payment providers may revise token semantics or underwriting if fraud patterns emerge.
• Platform policy evolution: watch default visibility rules, promotional placement mechanics and ad introductions that could change economics.
• Regulatory guidance: stay current with consumer protection agencies and payments regulators that will likely issue clarifications on agentic checkout liability and disclosure.
• Standards evolution: both UCP and Agentic Commerce Protocol (ACP) specifications are early stage; monitor updates and multi‑vendor interoperability tests.

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Early Verdict — Strengths and Weaknesses​

Strengths

• Practical standardization: UCP and Shopify’s Agentic Storefronts address the fragmentation problem head‑on by standardizing catalog semantics and checkout handoffs. This lowers engineering friction for merchants and increases the probability that AI channels will scale beyond bespoke pilots.
• Merchant control on paper: Shopify’s design preserves merchant‑of‑record responsibilities, attribution and data flows into the admin — a critical requirement for merchants who must manage fulfillment, returns and customer relationships.
• Payments security architecture: Delegated, tokenized checkout patterns reduce exposure of raw card data and align with current PSP capabilities, which makes in‑chat checkout operationally feasible now.

Weaknesses / Risks

• Data hygiene requirement: Many SMBs lack the catalog discipline necessary to appear reliably in agent queries; fixing this at scale will be costly.
• Operational responsibility shifts: Fraud, chargebacks and settlement now sit squarely with merchants and PSPs, potentially changing underwriting and cost structures in the medium term.
• Concentration and commercial leverage: Default enrollments and new internal ad surfaces give platform owners leverage to extract fees or change placement economics quickly. Merchants must retain contractual levers and data access.

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Conclusion​

Agentic commerce is no longer an experiment — it has graduated to productization and standardization. Shopify’s Agentic Storefronts and the Universal Commerce Protocol, combined with Google’s Gemini shopping rollout and Microsoft’s Copilot Checkout, create a credible path for AI agents to perform discovery, comparison and checkout in one seamless flow. That path relies on disciplined catalog management, robust delegated payment rails and auditable orchestration — all areas where merchants must invest before expecting reliable returns.
For merchants, the immediate imperative is pragmatic: clean the data, test payments and instrument outcomes. For platform and payments teams, the work is governance and standards—ensure transparent enrollment mechanics, clear fee economics and auditor‑friendly provenance logs. If executed with strong guardrails and transparent terms, UCP and agentic storefronts can unlock new discovery channels and faster conversions. If mishandled, they risk creating new operational burdens, unexpected fees and regulatory headaches.
Merchants and IT teams should treat the next 60–120 days as a phase of careful pilots and measured rollouts: validate conversion lifts, monitor dispute behavior, and hold platforms to auditable guarantees before scaling into broad participation. The places agents can act will quickly become places where sales happen — and the early movers who prepared for this era will have a material advantage.

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Source: SMBtech https://smbtech.au/uncategorized/sh...ct-merchants-to-google-microsoft-and-chatgpt/

 

[ChatGPT]

Microsoft’s Copilot puts an AI-powered recipe maker in your pocket — one that can turn a photo of your fridge, a five‑word prompt, or an old family staple into a usable, scaled recipe, a grocery list, and step‑by‑step guidance — but it’s not a substitute for food‑safety knowledge or human judgement.

Overview​

Microsoft has pushed Copilot into everyday tasks, and one of the most practical consumer use cases is recipe generation. Copilot can suggest dishes based on a short prompt, create recipes from an ingredient list or image, adapt recipes to dietary preferences, scale servings, and produce shopping lists and timed prep plans. These features make Copilot an attractive AI recipe generator for busy home cooks, people trying to reduce food waste, and anyone who wants faster meal planning.
At the same time, generative AI has well‑documented limitations: models can hallucinate details, mix up measurements or techniques, and occasionally produce unsafe recommendations for high‑risk processes such as canning or long‑form preservation. Copilot also collects and stores user activity by default and offers privacy controls that let you limit personalization and delete conversation history. Those two realities — capability and risk — are central to using Copilot as an effective, safe kitchen assistant.

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Background: How AI turns your pantry into a menu​

What “AI recipe generator” actually means​

An AI recipe generator is a conversational system built on a large language model (LLM) or multimodal model that synthesizes cooking knowledge from training data and web resources. When you ask Copilot to “make dinner with chicken, rice, and spinach,” it doesn’t taste the food — it aggregates patterns it has seen and composes a recipe that looks, structurally, like human recipes: ingredient list, measurements, cooking steps, timing, and common tips.
This approach is powerful for ideation and adaptation. Use cases include:

• Turning a short prompt into full recipes.
• Translating recipes between units (metric ↔ imperial) and scaling for headcount.
• Substituting ingredients for allergy or diet constraints.
• Generating timed prep plans for multi‑dish cooking.
• Producing consolidated shopping lists sorted by aisle.
• Explaining techniques step‑by‑step for novices.

Where Copilot fits in the Microsoft ecosystem​

Copilot integrates across Microsoft’s consumer and productivity products. For recipe generation you’ll most commonly use:

• The Copilot app or the Copilot sidebar within Windows and Microsoft 365 apps for conversational prompts.
• Copilot Vision (image input) to extract ingredients from photos or screenshots.
• Copilot in Word or OneNote to document and save modified recipes, and Excel to build meal plans.

Copilot stores conversations by default so you can revisit prior prompts and results, but privacy controls let you manage personalization and delete history. Administrators and users also have options to control Copilot usage in organizational deployments.

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Why use Copilot to generate recipes: benefits and best fits​

Speed and inspiration​

If you’ve ever stood in front of the fridge asking, “What can I make with this?”, Copilot converts that question into instant choices. The AI excels at:

• Producing multiple menu options in seconds.
• Recommending creative flavor combinations you might not consider.
• Reducing the time spent browsing dozens of recipe pages.

Reduce waste and optimize grocery trips​

By accepting a pantry list or photo, Copilot helps you use what you already have — pairing ingredients efficiently and consolidating a grocery list that minimizes extra shopping trips. This “pantry‑to‑plate” workflow is particularly useful for meal prepping and weeknight dinners.

Personalization and dietary support​

Copilot can adapt recipes to dietary needs (vegetarian, gluten‑free, low‑sodium) and to skill level — offering more hands‑on or more hands‑off versions depending on what you ask for. That makes it a strong tool for those who track macros, manage allergies, or teach new cooks.

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Practical, step‑by‑step: using Copilot to generate a recipe​

1. Inventory: give Copilot a precise starting point​

• Type a tidy list of what you have (include package sizes or counts when possible).
• Or use Copilot Vision: take a clear photo of your fridge or pantry and ask Copilot to extract ingredients.
• Add constraints: number of servings, maximum total time, equipment (air fryer, Instant Pot), and preferred cuisine.

Why precision matters: LLMs produce better technical outputs when the prompt contains explicit constraints. If you want a weeknight meal for four in under 30 minutes, say that upfront.

2. Ask for variety, not just one option​

Prompt example:

• “I have 2 boneless chicken breasts, 1 cup rice, 1 onion, canned tomatoes, and spinach. Give me 3 dinner options for 4 people under 45 minutes: (a) comfort, (b) spicy, (c) low‑carb. Include difficulty, total time, and one‑line cost estimate.”

Asking for multiple options reduces the risk of locking into a suggestion that doesn’t fit your pantry or skill set.

3. Pick one and refine​

After choosing the menu option you like, ask Copilot to:

• Scale the recipe to the exact headcount.
• Produce an ingredient list with exact measurements.
• Convert units (g → cups) or adjust for dietary swaps (dairy → nondairy).
• Produce a timed prep schedule that staggers steps to avoid oven or stove conflicts.

4. Generate a consolidated grocery list and export​

Request a single grocery shopping list with quantities consolidated and optionally sorted by aisle. Ask Copilot to export the list in a simple format (CSV or plain text) so you can paste it into a grocery app or smart list.

5. Ask for technique clarifications and safety checks​

• “Explain how to check chicken is done.”
• “What internal temperature should a roast reach?”
• “Is this canning recipe safe for water‑bath canning?”

Treat Copilot’s answers as a draft — always verify critical safety steps from specialist sources for high‑risk processes.

6. Test, iterate, and save​

Try the recipe at home. If something is off, feed Copilotback the results: “I found the sauce too salty. Suggest adjustments for less salt without losing flavor.” Copilot can iterate quickly, but practical testing by a human remains essential.

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Example prompts you can copy and paste​

• “Create a weeknight Italian dinner for 4 using chicken thighs, cherry tomatoes, pasta, and ricotta. Keep total time under 40 minutes and include a simple 10‑minute dessert.”
• “I only have eggs, frozen peas, a bag of rice, and an onion. Give me 3 recipes, one vegetarian, one with eggs as main protein, and one lunch‑box idea.”
• “Scale this recipe to 8 servings and make it gluten‑free: [paste recipe].”
• “Convert the measurements to metric and include a prep timeline with oven temperature and resting times.”

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Technical verifications and safety essentials​

Food safety first: check temperatures and the danger zone​

When Copilot gives cooking times and temperatures, cross‑check essential safety numbers against authoritative guidance. For example:

• All poultry should reach 165°F (74°C) internal temperature.
• Ground meats should reach 160°F (71°C).
• Whole cuts of beef, pork, lamb: minimum 145°F (63°C) with a 3‑minute rest.
• Reheats and leftovers should reach 165°F (74°C).
• Keep perishable food out of the “danger zone” between 40°F and 140°F to prevent bacterial growth.

Always use a calibrated food thermometer for proteins and sensitive dishes. Copilot can tell you temperatures, but the thermometer is the ultimate authority.

High‑risk processes: do not blindly trust AI for preservation or canning​

Canning and long‑term preservation require exact acidification, processing times, and pressures. AI outputs have sometimes included incorrect instructions for canning — which can create real hazards such as botulism when low‑acid foods are canned improperly. Treat AI‑generated preservation instructions as hypotheses that must be validated against dedicated food‑safety references and official guidelines.

Allergens and nutrition​

AI can adapt recipes for allergies, but you should manually confirm substitutions and check cross‑contact risks. Nutrition estimates generated by Copilot are approximate and can vary based on portion size and ingredient brands; rely on certified nutrition tools or dietitians for medical diets.

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Privacy and data handling: what to expect and what to control​

Copilot saves conversations by default so you can access prior prompts, but you can delete history and manage personalization. Microsoft provides privacy controls that let you:

• Turn off personalization so Copilot does not use your conversations to tailor future responses.
• Delete saved Copilot conversations from the Copilot history or Microsoft privacy dashboards.
• Control what Copilot can access across Microsoft apps (for example, whether it can analyze content in Word, Outlook, or Excel).

Be cautious when sharing sensitive personal data in prompts. Avoid including medical conditions, detailed personal health information, or other sensitive identifiers when asking for recipes related to health issues; instead, use generalized constraints (e.g., “low‑sodium” rather than listing medical diagnoses).
Note: Microsoft’s documentation on how long uploaded files or activity are retained has evolved; double‑check your account and privacy dashboard for the most current retention settings and deletion procedures.

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Critical analysis: strengths, limitations, and where Copilot shines​

Strengths​

• Speed and convenience: Copilot drastically shortens the ideation loop from “what to cook” to “what to buy” to “cook now.”
• Personalization at scale: Copilot tailors meals to dietary constraints, serving sizes, and equipment, saving time for cooks with specific needs.
• Integration: Tightly integrated with Windows and Microsoft 365 apps, Copilot can export recipes and shopping lists into the tools users already use.
• Accessibility: Step‑by‑step guidance and technique explanations help novice cooks learn culinary skills on demand.

Limitations and risks​

• Model hallucinations: LLMs can invent measures, omit crucial steps, or recommend unsafe processing times. This is the single most important operational risk when using AI for cooking instructions.
• Food safety liability: For preservation and other high‑risk activities, the cost of a model’s mistake can be bodily harm. Never rely on AI alone for canning, pressure‑canning, or fermenting instructions.
• Privacy and data exposure: In enterprise contexts, Copilot may access large volumes of sensitive files if permissions are overly broad. Administrators must govern data boundaries and audit usage.
• Nutritional accuracy: Nutrition outputs are estimates and vary by product; they are not a substitute for professional dietetic advice in clinical settings.
• Erosion of human‑tested craft: Recipes that haven’t been tested in a real kitchen may lack nuance, mouthfeel guidance, or fail under real‑world timing constraints.

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Mitigations: how to use Copilot safely for cooking​

• Always verify critical numbers (temperatures, processing times) with official food‑safety resources before following instructions for high‑risk procedures.
• Use an instant‑read thermometer and follow the “clean, separate, cook, chill” food‑safety framework.
• For canning or long‑term preservation, consult validated, tested recipes from recognized authorities and treat any AI suggestion as a starting point only.
• Keep personal or corporate data scope narrow when using Copilot: review privacy settings, disable personalization if you prefer, and delete sensitive conversations when finished.
• Test AI‑generated recipes at low scale before serving to guests. Small trial runs reveal timing issues and flavor balances without risk.
• Keep a human in the loop for content you publish: if you intend to publish AI‑assisted recipes, conduct hands‑on testing and disclose that you validated the method.

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Advanced workflows: automation, meal planning, and smart kitchen integration​

For power users, Copilot can be woven into broader automation:

• Export grocery lists as CSV and import them into grocery apps or automation tools.
• Use Copilot to create weekly meal plans with shopping consolidation and batch‑cooking schedules.
• Pair Copilot with smart appliances where available: some dedicated cooking platforms and smart ovens accept AI recipes or can be configured to follow precise temperature profiles. When doing so, always confirm the appliance’s safety features and local code for unattended operation.

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The editorial verdict: a tool, not a replacement for culinary judgement​

Copilot is a meaningful step toward an always‑available, AI‑powered kitchen assistant. It excels as an ideation engine, a pantry‑optimization tool, and a way to democratize cooking knowledge for new cooks. The convenience and productivity gains are real: fewer wasted ingredients, faster meal planning, and accessible technique coaching.
But the kitchen has non‑negotiable constraints — chemical, microbial, and physical — that require exactness and domain expertise. AI is a remarkable assistant for creativity and logistics; it is not yet a reliable authority for every technical or safety‑critical decision.
Use Copilot to spark ideas, speed routine tasks, and learn techniques; always verify critical safety details using established, domain‑specific resources and real‑world tools like thermometers. When you combine the speed of an AI recipe generator with careful human checks, you get the best of both worlds: inspiration that’s fast, plus food that’s safe and tastes great.

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Quick reference: practical prompts and safety checklist​

• Copyable starter prompt: “I have [list ingredients]. I have [equipment]. I need dinner for [n people] in under [X minutes]. Give me 3 options (comfort, healthy, spicy). Provide a scaled ingredient list, step‑by‑step timing, and a consolidated shopping list if I’m missing anything.”
• Safety checklist before cooking:
• Verify internal protein temperature with a food thermometer.
• Keep perishable ingredients refrigerated below 40°F until use.
• Do not follow AI outputs for pressure canning/canning without validating against tested canning guides.
• Check allergy information and potential cross‑contact risks.
• Test unfamiliar substitutions at small scale.

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Microsoft Copilot makes AI recipe generation practical and accessible; when paired with discipline — verification of temperatures and techniques, cautious handling of preservation instructions, and careful privacy controls — it becomes a powerful assistant that saves time while keeping kitchens safe.

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Source: Microsoft How to Use AI to Generate Recipes | Microsoft Copilot