I’m thrilled to share some fantastic news with you. We’ve just launched support for Claude Fable within Profound, and it’s an upgrade that I’m genuinely excited about.
Incorporating Claude Fable into our system not only enhances user experience but also brings a new level of efficiency to our platform. This integration is designed to provide seamless functionality and improve overall productivity.
I’m confident that this addition will greatly benefit all users by offering enhanced capabilities and features that are both intuitive and powerful. Stay tuned for more updates as we continue to innovate and evolve.
Are you looking to elevate your affiliate marketing efforts? With Profound and PartnerStack, I’ve been able to efficiently activate the right affiliate publications on a larger scale than ever before.
Through this powerful collaboration, I’ve discovered new ways to enhance my campaigns and drive significant growth by engaging with the right audiences at the right time.
Hey there! I’m excited to introduce you to something that has truly changed the way I approach coding projects—the Profound API Cookbook. If you’ve ever started with the thought, ‘I want this number,’ and wished for a seamless way to transform that into runnable code, this is for you.
Imagine having a collection of end-to-end recipes right at your fingertips, perfectly layered on top of our REST API references. This isn’t just about coding; it’s about enhancing your workflow and efficiency in a whole new way. Each recipe is designed to guide you from concept to execution with ease.
Struggling with maintaining brand consistency? I’ve learned that it’s not about having more tools, but rather having the right tools, perfectly aligned with your brand’s goals.
I’ve seen marketing teams overwhelmed with tools. The average B2B company might use up to 20 different martech solutions. Despite this, keeping brand consistency at scale can be tough. Fewer than 10% of brands manage to maintain strong cohesiveness across all products and channels. The core issue? Tools rarely work in harmony to support a unified brand experience.
Managing a brand across various channels, whether through campaigns or social media, can lead to brand elements drifting. It’s those small inconsistencies—a slightly off-color logo here, outdated messaging there—that can gradually erode the hard-earned brand equity.
The solution isn’t about increasing the number of tools. It’s about selecting the right ones and arranging them with deliberate intention.
Start with strategy, then stack
Before diving into an audit of your current software or seeking out new options, it’s crucial to develop a framework for what brand equity means to your organization. David Aaker’s brand equity model—which focuses on loyalty, awareness, perceived quality, and brand associations—is a sound approach. It transforms brand management into a sustainable growth strategy. In terms of a martech stack, this means utilizing tools that both build and protect your brand.
On the strategy side, platforms like Notion, Miro, and Lucidchart are invaluable. They help document positioning, define messaging, and map out customer journeys. These may not be glamorous, but they provide the solid foundation for successful execution. Without such a framework, design and content teams are left guessing.
The core of the stack: Digital asset management
If there’s one tool that differentiates a cohesive brand management stack from fragmented apps, it’s digital asset management (DAM). Unlike typical cloud storage services such as Google Drive or Dropbox, a DAM solution organizes and governs brand assets comprehensively, offering features like approval workflows and version management that cloud storage lacks.
Consistent branding can increase revenue by 10–20%, and a DAM provides the structure needed to maintain this consistency at scale. By ensuring all team members and partners access the same approved asset library, you eliminate brand drift.
Modern DAMs further simplify brand management by integrating AI to speed up content discovery and automated metadata tagging, reducing creative bottlenecks and accelerating go-to-market timelines.
Execution tools that reinforce brand standards
Apart from DAM, execution tools are essential for converting brand strategy into consistent published content. Depending on your team, Adobe Creative Cloud, Figma, or Canva can be used. They offer varying degrees of design flexibility and guardrails to maintain brand standards.
Balancing creativity with adherence to brand guidelines is key. Tools with brand templating features allow teams autonomy while ensuring brand consistency. Alternatively, using brand templates within your DAM offers greater control and tracking capabilities.
For social media and content distribution, platforms like Hootsuite and HubSpot ensure cohesive publishing across channels. It’s crucial these tools connect to your DAM to guarantee only brand-approved content is shared widely.
SEO tools like SEMrush and Ahrefs help reinforce your brand’s voice and authority online. In today’s market, where SEO extends to geo-targeting, it’s vital to ensure your brand is accurately represented from the start of customer interaction.
Governance closes the loop
A martech stack without governance is simply a mix of tools. Governance—including approval workflows and brand monitoring—is what makes your stack effective and protective.
Incorporating workflow tools into project management or your DAM ensures faster and accountable proofing cycles. Tools like Mention help track external brand perception, highlighting areas of potential drift before they escalate.
The takeaway
The aim of a streamlined brand management martech stack is not complexity but efficiency. It should empower any team member or partner to access and create on-brand content swiftly, independently, and without needing constant design team input.
This requires a strategic approach, a robust DAM as the central hub, integration with execution tools, and governance practices that uphold standards. When these elements work together, your brand transforms from a reactive endeavor to a proactive tool for long-term success.
As I look ahead, I’m thrilled to share what we have in store with our latest product, Profound. Over the coming weeks and months, we are embarking on a journey that represents a much bolder move than anything we’ve previously attempted.
Internally, our team is buzzing with excitement, and we believe it’s time to extend that excitement to you, our valued customers. We’re eager to unveil our vision for the future and how it aligns with your needs.
I’ve always found the ability to share insights seamlessly to be crucial in our fast-paced digital world. One tool that I’ve come across is the generation of links to custom dashboards, which can be viewed by absolutely anyone.
Imagine the convenience of sending a link to your team or stakeholders, enabling them to access the dashboard data in real-time. This not only promotes transparency but also enhances collaboration by ensuring everyone has access to the same data, whenever they need it.
Through these easily shareable links, I’ve been able to bring a level of accessibility and efficiency to data sharing that seemed challenging before. It’s truly a game-changer, especially when managing multiple projects across different teams.
Analyzing nearly two million LLM sessions across nine industries throughout 2025 was a fascinating journey for me. I began with the assumption that ChatGPT would dominate and that AI usage patterns would be relatively uniform with minimal impact.
The findings, however, were surprising.
While ChatGPT does indeed control 84.1% of the trackable AI discovery traffic, it’s primarily serving as a broad-market tool. This discovery significantly impacts strategic approaches.
In today’s landscape, relying solely on a single discovery strategy is not viable. A multi-platform approach that aligns with how and where users find productivity is essential.
Brands must now discern which platforms are empowering productivity rather than merely supporting initial discovery phases.
Various LLMs are excelling in different sectors, often with stark differences. The key takeaway for 2026 is more complex than simply focusing on ChatGPT.
Here’s what I’ve discovered from the data.
The Growth Rate Divergence: ChatGPT vs. Competitors
Throughout 2025, major LLM platforms exhibited significant growth discrepancies:
ChatGPT: 3x growth
Copilot: 25x growth
Claude: 13x growth
Perplexity: 1x growth
Gemini: 1x growth
Although ChatGPT grew, Copilot and Claude experienced much more rapid growth. Platforms like Perplexity and Gemini remained steady, reinforcing specific workflows.
These numbers highlight strategic priorities:
Satya Nadella celebrated Copilot reaching 100 million monthly users.
Dario Amodei revealed that Anthropic’s revenue grew from $100 million to $8–10 billion in under two years.
Aravind Srinivas noted significant interest in Perplexity Finance.
The focus on growth is crucial because it signals true user value:
Copilot excels in the Microsoft ecosystem.
Claude appeals to developers.
Perplexity thrives among finance professionals.
Different LLMs are thriving in various industries at markedly different rates.
Pattern 1: Copilot’s Striking Growth
Copilot’s remarkable 25x growth is indicative of its premier position in B2B environments reliant on Microsoft tools.
SaaS
ChatGPT: 2x growth
Copilot: 21x growth
The rapid adoption mirrors modern SaaS practices, embedding LLMs directly into workflows.
Education
ChatGPT: 6x growth
Copilot: 27x growth
Copilot benefits from educational settings fostering knowledge sharing and synthesis.
Finance
ChatGPT: 4.2x growth
Copilot: 23x growth
Finance aligns with Copilot due to automation needs and context dependency.
Copilot’s growth is most pronounced in industries where professionals are deeply integrated with Microsoft tools.
Instruments like Excel transform into data interpretation powerhouses with Copilot, eliminating the need for external searches.
Implications
For work-centric audiences like SaaS, finance, and education specialists, AI discovery is shifting into LLMs embedded in workflows.
Pattern 2: Perplexity Shines in Finance
While Perplexity has flat growth overall, it stands strong in finance with a 24% market share, unlike in other sectors where it has diminished.
SaaS: down to 7.3%
E-commerce: down to 3.4%
Education: down to 5.2%
Publishers: down to 3.6%
Finance demands accuracy; thus, traceable sources make Perplexity vital in this sector.
Partnering with Benzinga, FactSet, and others, Perplexity offers in-depth data vital for financial decisions.
Trust and verifiability are crucial in finance, and that’s where Perplexity excels.
Implications
In finance, selection of platforms that integrate with licensed data and credible sources is critical. Success hinges on being part of these authoritative ecosystems.
Pattern 3: Claude’s Dominance in Analysis
With just a 0.6% share, Claude might appear to be an underdog, but it thrives in specialist sectors like publishing and finance.
Publishers: 49x growth
Education: 25x growth
Finance: 38x growth
SaaS: 10.3x growth
Claude’s strength lies in standalone, strategic thinking rather than integrated tools like Copilot.
Publishing professionals and financial analysts use Claude for its substantial context window, enabling complex and strategic queries.
Implications
Target audiences that require in-depth analysis should focus on creating structured and detailed content. Claude’s user base is smaller but highly influential.
Pattern 4: Challenges in Tracking Gemini
The data concerning Gemini is puzzling, showing both growth and declines. This could be attributed to issues with attribution rather than an actual decline in users.
Education: −67% tracked traffic
SaaS: +1.4x growth
Finance: +1.3x growth
E-commerce: +2.7x growth
Gemini’s interaction model keeps users within its ecosystem, making measurement challenging.
The reality is that usage might still be robust, but the tracking systems need to catch up with user behaviors.
Implications
As AI-assisted conversions increasingly occur, traditional last-click attribution models need reconsideration.
Monitor brand search performance and invest in broader visibility strategies.
Strategizing Your LLM Approach
AI discovery is diversifying rather than converging. Tailoring strategies based on your audience’s preferences and behaviors is crucial.
Enterprise Audiences: Focus on Copilot integration for SaaS and B2B environments.
High-Stakes Decisions: Consider Perplexity’s reliability in providing traceable data.
I used to rely heavily on vanity metrics, thinking they were the key to my digital marketing success. However, I soon realized that they did little more than paint a pretty picture with no real substance. That’s when I decided to focus on what’s truly important: the numbers that actually drive business growth.
In today’s digital landscape, it’s crucial to pinpoint which KPIs truly matter. By doing so, I can build reports that genuinely tell the story of my business’s progress. These metrics go far beyond just surface-level statistics.
It’s all about understanding the complete picture and tailoring my strategy to focus on those key performance indicators that have a tangible impact on my business’s bottom line. Join me as I delve into which digital marketing KPIs deserve our attention.
I’ve realized that building a business that thrives solely on advertising is risky. We can’t let our ventures be at the mercy of fluctuating ad performances.
Instead, let’s explore how to establish e-commerce growth engines. These strategies focus on compounding growth over time, emphasizing customer loyalty and enhancing brand strength.
By shifting our approach, we can generate sustainable revenue that doesn’t hinge solely on ad spend.
In my experience, the open web often feels like the Wild West, especially in recent times. Many creators, myself included, have watched as our hard work is scraped and fed into large language models without any hint of permission.
This situation has become a free-for-all, leaving website owners with almost no means to opt out or safeguard their creative endeavors. There have been attempts to address this, such as Jeremy Howard’s llms.txt initiative. Much like robots.txt helps us manage site crawlers, llms.txt aims to provide guidelines for AI companies’ crawling bots.
However, a promising new protocol is on the horizon, potentially granting site owners like myself more control over how AI firms utilize our content. It looks like this might become part of robots.txt, allowing us to set definitive rules around AI system access and usage.
IETF AI Preferences Working Group
In response to this issue, the Internet Engineering Task Force (IETF) began the AI Preferences Working Group earlier this year in January. Their mission is to craft standardized, machine-readable rules to empower site owners to articulate AI usage preferences for their content.
Since its inception in 1986, the IETF has established core Internet protocols like TCP/IP, HTTP, DNS, and TLS. Now, they’re laying down foundations for the open web’s AI era. Leading this group are co-chairs Mark Nottingham and Suresh Krishnan, joined by figures from Google, Microsoft, Meta, and more.
Of particular interest is Google’s involvement via Gary Illyes, who is part of this working group.
“The AI Preferences Working Group will standardize building blocks that allow for expressing preferences about how content is collected and processed for Artificial Intelligence (AI) model development, deployment, and use.”
What the AI Preferences Group is Proposing
This group aims to deliver new standards that empower site owners to determine how LLM-powered systems can utilize their open web content.
A standard track document detailing a vocabulary to express AI-related preferences, independent of content association methods.
Standard track document(s) that explain how to associate these preferences with content using IETF-defined protocols and formats, for example, Well-Known URIs and HTTP response headers.
A standard approach for reconciling multiple preference expressions.
At the time of writing, nothing is set in stone yet. Early documents, however, provide a sneak peek into potential standards.
This working group published two crucial documents in August.
These documents propose significant updates to the Robots Exclusion Protocol (RFC 9309), suggesting new rules and definitions enabling site owners to specify AI content usage permissions.
How It Might Work
AI systems on the web are categorized and assigned standard labels. Whether a directory will exist for site owners to identify system labels remains unclear.
Currently, the defined labels include:
search: for indexing/discoverability
train-ai: for general AI training
train-genai: for generative AI model training
bots: for all types of automated processing, such as crawling and scraping
For each label, you can set two values:
y to allow
n to disallow.
I found it interesting that these rules can be applied at the folder level and customized for different bots. In robots.txt, they’re implemented using a new Content-Usage field, akin to existing Allow and Disallow fields.
Here’s an example robots.txt that the working group shared in their document:
Explanation Content-Usage: train-ai=n indicates that no content on this domain may be used for training any LLM model, whereas Content-Usage: /ai-ok/ train-ai=y permits model training using content within the /ai-ok/ folder.
Why Does This Matter?
There’s significant buzz about llms.txt within the SEO community and its use alongside robots.txt. Yet, no AI company has confirmed adherence to these guidelines, and Google disregards llms.txt.
Website owners, including myself, crave more explicit control over how AI companies leverage our content—be it for training models or RAG-based responses.
I feel that the IETF’s new standards signify positive progress. With Illyes as a contributing author, I remain optimistic that once finalized, companies like Google will embrace these standards, respecting new robots.txt rules during content scraping.
