Tag: Generative AI

NotCo: AI-Powered Fragrance With Purpose

Back in 2014, Oscar Mayer showed how powerful scent becomes when it stops behaving like a message and starts behaving like a mechanic. Its bacon alarm let people wake up to the sound of sizzling bacon on the stove, while the brand inserted itself into a daily habit instead of a one-off impression.

Fast forward to 2026, and NotCo is pushing scent from playful activation into AI-enabled product development. With Giuseppe AI and its fragrance formulation work with Cramer, a Latin American multinational in flavors and fragrances, NotCo is showing how a sensory cue can become a personalized product proposition. Giuseppe is positioned as an end-to-end product development platform, meaning it helps move from idea to formulation to scalable output within one workflow.

How Aroma Best Friend makes Giuseppe easy to understand

Aroma Best Friend does not try to explain AI through dashboards, technical architecture, or speed claims. It explains the platform through a very human tension point: a dog struggling when its owner leaves home. The story is simple, emotional, and commercially useful at the same time.

The mechanism is easy to retell. The campaign presents a personalized fragrance generated from the owner’s scent profile so a dog is left with an olfactory stand-in for presence. An olfactory profile is the identifiable mix of volatile compounds associated with a person’s scent signature.

In consumer goods, this is the kind of AI story that travels fastest because it links formulation capability to a sensory outcome people can instantly understand.

The film frames the idea around making your dog happier, which keeps the promise focused on an outcome instead of a technology demo.

Why this lands harder than most AI demos

Most AI campaigns still make the same mistake. They tell you the model is powerful and then expect the audience to infer the commercial value. Aroma Best Friend works better because the technology claim is attached to a felt problem and a tangible output, which makes the platform easier to understand and easier to remember.

Extractable takeaway: AI becomes more persuasive when it is shown solving a problem people can emotionally grasp, not when it is described as a capability stack. The sharper the human tension and the clearer the output, the stronger the commercial story.

Scent is not decorative here. It is the proof. That turns Giuseppe from a backstage R&D engine into the source of a new kind of product experience. NotCo is not just advertising AI. It is advertising the kinds of product experiences AI can now help create.

The business play behind the emotion

The real question is whether an AI platform can turn an invisible R&D capability into a story that brand teams, partners, and future buyers instantly understand.

The official waitlist for the product makes clear that joining does not guarantee access to or availability of the product. That suggests this is as much about validating demand and capturing interest as it is about launching a ready-to-scale offer.

That is the smarter move. Aroma Best Friend works as a campaign, a proof-of-capability demo, and a demand signal test at the same time. Instead of saying that Giuseppe enables personalization and creativity, NotCo dramatizes a specific version of personalization that people can picture, repeat, and remember.

What FMCG and CPG teams should borrow now

  • Turn capability into consequence. Do not market the model first. Market the human outcome the model makes possible.
  • Use one emotionally legible use case to explain a broader platform. Aroma Best Friend is about dogs on the surface, but the deeper message is that Giuseppe can work where formulation and personalization matter.
  • Make the demo do double duty. The strongest AI campaigns are not just communications assets. They also test demand, capture leads, and reposition the company.
  • Choose outputs people can feel, not just read about. Text is easy. Fragrance is harder. That is exactly why this idea carries more weight.
  • Prove customization through specificity. Personalized fragrance is stronger than generic AI-powered personalization because it gives the claim an object, a use case, and a memory.

A few fast answers before you act

What is Aroma Best Friend really marketing?

Aroma Best Friend markets a personalized scent concept for pet separation anxiety on the surface, but at a deeper level it markets Giuseppe AI as a product-development engine that can move into formulation-led use cases.

Why does this explain Giuseppe better than a typical AI demo?

It explains Giuseppe better because it connects the technology to a human problem and a sensory output. That makes the platform easier to understand than abstract claims about intelligence, speed, or creativity.

Is Aroma Best Friend already a scaled product launch?

Not yet in any proven commercial sense. The waitlist language makes clear that joining does not guarantee access to or availability of the product, so the initiative still functions as a signal test as much as a launch story.

Why is scent such a strong choice for this idea?

Scent carries memory, comfort, and presence more directly than most brand cues. That gives the campaign emotional force and turns formulation technology into something people can instantly imagine in use.

What should marketers and innovation teams steal from this?

They should steal the structure. Start with a real human tension, let the technology solve it in a tangible way, and make the output specific enough that people can retell the story in one sentence.

Pomelli Photoshoot: Fast studio-quality assets

Start from a single image of your product and easily create high quality, customized product shots to elevate your marketing.

A jar in your hand. A whole shoot in your CMS

Start with the most ordinary thing in e-commerce. A single product photo, shot on a desk, held in a hand, good enough for internal approval but nowhere near “campaign-ready”. Then imagine turning that one image into a set of studio and lifestyle shots that look like you planned the lighting, the surface, the props, and the framing.

That is the pitch behind Photoshoot, a feature inside Pomelli from Google Labs: take a basic product image and generate professional-grade marketing imagery fast, without booking a studio for every new variant. “Studio-grade” here means assets that can sit on a PDP or paid social without instantly looking like “placeholder content”.

How Photoshoot turns one product photo into usable marketing imagery

Photoshoot is not just “generate me a nicer background”. It is a guided flow designed to keep output consistent.

  1. Pick a product photo. The input can be imperfect. The tool is explicitly designed to handle “don’t worry about polish”.
  2. Choose a template. Templates are pre-built shot styles (for example studio or lifestyle) that constrain composition so results do not drift into random aesthetics.
  3. Generate. Pomelli applies your brand aesthetic via its Business DNA, then generates new shots. Business DNA is Pomelli’s saved brand profile derived from your website (voice, fonts, imagery, color palette).
  4. Refine. You iterate with finishing touches, then download assets or store them back into Business DNA for reuse in later campaigns.

Under the hood, Google describes this as combining business context (Business DNA) with Nano Banana image generation to produce the final scenes.

In high-velocity retail and FMCG e-commerce teams shipping new SKUs (Stock Keeping Units) and promos weekly across many markets, this is the shortest path from “we have a product” to “we have compliant, channel-ready variants”.

The real question is whether one approved product shot can produce enough on-brand variants to increase throughput without increasing review drag.

Why it lands. Because it cuts the real friction, not the fun part

Most teams are not blocked on “having ideas”. They are blocked on throughput with consistency: getting enough variants, in enough formats, that still look on-brand, pass review, and do not trigger rework across design, legal, and local markets.

This is why the mechanism matters. Because Photoshoot grounds outputs in Business DNA and constrains composition via templates, the results tend to feel brand-consistent faster, which reduces review churn and makes variant production scalable.

Extractable takeaway: If you want generative creative to survive enterprise review, do not start with infinite freedom. Start with constraints that encode your brand (a reusable brand profile) and your channel rules (shot templates), then let the model fill in the pixels inside that box.

The business intent is blunt. Production leverage for asset variants

“Production leverage” is the multiplier you get when one person-hour produces many more usable assets without multiplying headcount or agency spend. For e-commerce teams, Photoshoot is essentially a variant engine.

  • More PDP (Product Detail Pages) imagery coverage without re-shooting every pack change.
  • More paid social iterations without waiting on design queues.
  • Faster seasonal refreshes when the same SKU needs a new context (spring, gifting, back-to-work).
  • A tighter loop between merchandising and creative because the cost of “try another angle” collapses.

Important reality check: you still need governance. Treat outputs like any other marketing asset. Rights, claims, pack accuracy, and local compliance do not disappear just because generation is fast.

Where to try it?

The Pomelli app on Google Labs is where you can access the experience.

However availability is currently limited. Pomelli has been launched as a public beta experiment in the United States, Canada, Australia, and New Zealand (English).

What to steal for your next asset sprint if the app is available in your region

  • Codify brand constraints first. Build a reusable “brand profile” (fonts, tone, visual rules) before you chase more generations.
  • Template your shots like you template layouts. Decide the 6 to 10 shot types you actually need (hero studio, detail crop, lifestyle context, ingredient cue) and standardize them.
  • Design for review speed. Define what “acceptable” means (pack legibility, logo integrity, claims, background rules), then generate inside those rails.
  • Run a SKU ladder test. Start with 10 SKUs across easy and hard surfaces (glass, reflective, metallic). If it fails there, it will fail at scale.
  • Instrument the pipeline. Track time-to-first-usable, approval rate, and rework causes. That is how you prove leverage, not by “wow, looks nice”.

A few fast answers before you act

What is Pomelli Photoshoot, in one sentence?

Pomelli Photoshoot is a feature inside Google Labs’ Pomelli that turns a single product photo into professional-style studio and lifestyle marketing images using brand context and image generation.

What is the mechanic marketers should care about?

You choose a product image, select a curated template (studio or lifestyle), generate variants grounded in your Business DNA, then refine and download or reuse those assets in future campaigns.

What does “Business DNA” actually mean here?

Business DNA is Pomelli’s saved brand profile derived from your website, such as tone of voice, fonts, imagery, and color palette, which Pomelli uses to keep generated outputs consistent.

Where is Pomelli available right now?

Pomelli is in public beta in English in the United States, Canada, Australia, and New Zealand. It is not currently available in Germany.

What is the first safe way to pilot this in an enterprise team?

Pilot it on a small SKU set with strict shot templates and review criteria, then measure approval rate and rework reasons before scaling variant production.

From AI Tool List to Working AI Tech Stack

From “pick 20 tools” to “run a working stack”

I recently came across the below video from Dan Martell which frames “zero-code million-dollar business” as a tool-selection problem. That framing is useful. However the right conclusion for marketers and brands watching is not “go pick 20 tools”. The right conclusion is “stop shopping. Start stacking”. In 2026, you should start focusing more on the ability to pick, connect, and operationalize capabilities.

By “working AI tech stack” I mean a small, repeatable set of tools that moves work from input to output with the least friction. It is not a folder of bookmarks. It is a production line.

The useful takeaway isn’t the list. It’s the operating model.

Most people consume AI content and walk away with a shopping list. That is the wrong takeaway. The useful takeaway is operational. Arrange capabilities into a workflow that consistently produces outputs. Briefs, assets, approvals, launches, responses, and measurable improvements.

A list creates options. A stack creates throughput. Throughput is how reliably your team converts intent into shipped work, week after week, without rebuilding the process every time.

The mechanism: a stack is just clean handoffs

A working AI tech stack is a sequence with explicit handoffs:

Inputs → Synthesis → Creation → Automation → Distribution → Measurement

Each step has one job. Each step produces an artifact someone else can use. Each handoff is defined so the work does not stall in Slack, email, or “waiting for approval”.

In global FMCG and retail marketing organizations, the bottleneck is rarely ideas but the handoffs between people, tools, and approvals.

Why this lands with leaders

Tool lists feel like progress because they are concrete and low-commitment. You can bookmark them and feel “covered”. Stacks feel harder because they force decisions: what is the workflow, who owns each step, where do we enforce quality and risk controls.

Extractable takeaway: If you cannot name the exact step a tool owns in a repeatable workflow (input → transformation → handoff → output), it is not part of your stack yet. It is just potential.

The business intent: less software. More shipped outcomes

For marketers and brands, the goal is not “using AI”. The goal is operational leverage:

The real question is not how many AI tools you can name, but whether your team can move work through a repeatable line with clear ownership and handoffs.

  • Faster cycle time from brief to asset.
  • Fewer revision loops because synthesis and constraints are done upfront.
  • Fewer dropped balls because handoffs are automated.
  • More reuse of institutional knowledge because answers are captured once and searchable.
  • Higher output without lowering standards.

This is also where governance belongs. A stack needs rules about what data can go where, who can approve what, and which steps require a human decision.

The working stack blueprint: tools mapped from Inputs to Measurement

Below are the 20 tools referenced in the video, placed where they most naturally fit in the production line. You can use fewer than 20. The point is the flow.

Inputs: capture raw material without losing signal

Manus

Manus is designed to act more like a task runner than a chatbot. You give it a goal and it works through steps to deliver outputs, not just advice. Example: collect competitor screenshots, extract claims, summarize patterns, and deliver a brief plus a slide outline.

SocialSweep

SocialSweep is positioned as a way to search your network and relationship graph with context. It helps you identify who you know, why they are relevant, and what to say. Example: find warm paths to retail media decision-makers, then draft an intro message that references shared context.

HireAlli

HireAlli is positioned around capturing commercial intent from website traffic so teams can follow up faster. Example: flag repeat visits to pricing pages, then route the lead to sales with a summary of pages viewed and a recommended next message.

Synthesis: turn messy inputs into a usable brief and plan

NotebookLM

NotebookLM is useful when you want answers grounded in the sources you provide. It helps you summarize, compare, and extract structure from documents. Example: upload research PDFs and prior campaign docs, then generate a launch FAQ and a messaging hierarchy that stays consistent with those materials.

Claude

Claude is a general assistant that excels at drafting, rewriting, and structuring thinking. Use it to turn raw notes into clear decisions and action plans. Example: paste a workshop transcript and request a decision log, assumptions, risks, and a one-page brief for stakeholders.

ChatGPT

ChatGPT is a general-purpose assistant for ideation, drafting, analysis, and reusable workflows. It is especially useful when you iterate toward a spec. Example: ask clarifying questions for a campaign brief, then output a structured creative and media spec the team can execute.

Creation: produce assets that are actually shippable

Gamma

Gamma helps turn rough thinking into a structured deck or document quickly. It is strong when the bottleneck is narrative structure, not visual polish. Example: paste the brief, generate a 10-slide storyline, then refine the argument and flow before design.

Descript

Descript lets you edit audio and video through text. You edit the transcript like a document and the media follows. Example: clean up a leadership video by removing filler words, tightening sections, and exporting both a long version and short clips.

ElevenLabs

ElevenLabs generates natural-sounding speech from text and supports scalable voice workflows. It is useful for narration, localization, and voiceovers. Example: create a consistent “brand voice” narration for product explainers, then generate localized voiceovers without re-recording.

Lovable

Lovable is positioned as an AI-assisted way to build apps or web experiences without traditional engineering. Think prototypes, internal tools, and simple customer experiences. Example: describe an internal campaign intake tool, generate a prototype, then iterate requirements until it is usable.

Automation: make the handoffs run without nagging humans

Make

Make connects apps into workflows using triggers and actions. It is the plumbing that turns “good tools” into “a working line”. Example: when a brief is approved, create tasks, notify stakeholders, generate a first draft, and route it to review automatically.

ChatAid

ChatAid is positioned as an AI support layer that can answer recurring questions and route issues. It fits both internal enablement and customer-facing support when designed with escalation rules. Example: answer “where is the latest asset” or “what is the policy”, and escalate to a human when confidence is low.

Distribution: move outputs into channels that drive outcomes

Revio

Revio is positioned around managing inbound conversations across social channels in one place. It helps teams respond consistently and not miss high-intent messages. Example: unify DMs so customer questions and sales inquiries do not get lost across platforms.

YourAtlas

YourAtlas is positioned around AI agents that can handle inbound qualification and booking. This matters in service businesses and lead-driven funnels. Example: handle inbound calls or requests 24/7, capture required details, then hand off qualified appointments to humans.

Membership.io

Membership.io supports structured memberships and gated content experiences. It is a distribution layer for expertise and ongoing value, not just content hosting. Example: package a learning path for partners or teams, with searchable resources and a community layer to reduce repeated questions.

BuddyPro

BuddyPro is positioned around turning your content and methods into an always-on assistant people can query. It is a distribution mechanism for expertise at scale. Example: clients query your “playbook assistant” for next steps between calls, and you control what it can and cannot answer.

Measurement: close the loop so the stack improves every cycle

Hiro Finance

Hiro Finance is positioned around cash-flow visibility and planning. It helps decision-makers see financial reality without spreadsheet archaeology. Example: run a weekly check on runway, recurring costs, and upcoming risk points before you scale spend.

HelloFrank

HelloFrank is positioned around deeper business-context finance insights. It can help detect spend anomalies and surface what changed month-over-month. Example: find subscription creep and cost spikes, then turn it into a prioritized cleanup plan.

Revaly

Revaly is positioned around payment performance and reducing failed transactions that create involuntary churn. It matters most where recurring revenue is sensitive to declines. Example: identify where legitimate payments fail and improve recovery rates without harming customer trust.

Precision

Precision is positioned around turning KPIs into a practical operating rhythm. It helps teams focus attention on what moved and what to do next. Example: generate a weekly performance brief. These metrics shifted, here are likely drivers, here is what we should test or fix this week.

How to build a working stack without buying 20 subscriptions

  • Start with one workflow you ship weekly. Brief → assets → approvals → publish → measure.
  • Assign ownership per step. Tools without owners become clutter.
  • Build the handoffs before you add more tools. Automation is what turns tools into a line.
  • Define where humans must decide. Brand-sensitive, compliance-sensitive, and customer-sensitive steps need a review point.
  • Run a monthly keep-or-kill review. If a tool is not improving cycle time or quality, remove it.

A few fast answers before you act

What is the single biggest mistake teams make with AI tools right now?

They treat AI as a chat window to copy and paste from, instead of an execution layer connected to a workflow that ships outputs.

What is a “working AI tech stack” in one sentence?

A working AI tech stack is a small set of connected tools that reliably turns inputs like notes and briefs into shippable outputs, with minimal friction and clear handoffs.

How do I decide if a tool belongs in my stack?

If you cannot name the exact step it owns and the handoff it triggers, it is not part of the stack yet.

What should a marketing leader implement first?

One throughput line, end to end. Inputs → Synthesis → Creation → Automation → Distribution → Measurement. Then automate handoffs before adding new tools.

How do I avoid tool sprawl?

Set constraints: one tool per job, a clear owner, and a monthly keep-or-kill review tied to measured outcomes.