From Search to Answers: How LLMs’ Hunger for Fresh Content Is Changing B2B Discoverability

In the age of generative AI, the rules of online visibility are being rewritten. Traditional search engines are no longer the sole gateway between businesses and their buyers. Large language models (LLMs) like ChatGPT, Claude, Google’s new Search Generative Experience (SGE), and others are transforming how information is delivered – providing direct answers and recommendations instead of just links. Crucially, these AI “answer engines” increasingly pull from real-time, up-to-date content rather than relying only on their static training data. This shift has profound implications for B2B product discoverability, especially in fast-moving sectors like SaaS, software development, and cybersecurity.

Why does fresh content matter so much? Consider that nearly 90% of B2B buyers by the end of 2024 were using generative AI tools like ChatGPT during their buying process b2b-marketing.transmissionagency.com. Many now simply ask an AI assistant, “Who are the top providers in X?” and receive a concise, conversational shortlist – often without ever visiting a website or search results page b2b-marketing.transmissionagency.comb2b-marketing.transmissionagency.com. In fact, AI-driven product recommendations are already driving a 436% year-on-year increase in sales conversions in some cases b2b-marketing.transmissionagency.com. In this new landscape, if an AI doesn’t mention your product or content, you virtually don’t exist to that buyer. This white paper dives deep into why LLMs prioritize real-time content over stale training data and what that means for how B2B brands can be discovered. Backed by recent case studies, expert research (Gartner, Forrester, LeadSpot), and practical examples, we’ll explore how you can ensure your brand is visible in AI-generated answers… without relying on ad spend.

LLMs Evolving: From Static Training Data to Real-Time Answers

Traditional LLMs (like early ChatGPT releases) were trained on massive datasets with a fixed cutoff date. This meant their knowledge was frozen in time. For example, GPT-3.5’s training data mostly ends around 2021, so it wouldn’t naturally know facts or products introduced in 2022 or later. The downside of this static approach became apparent as users noticed answers referencing outdated information. A striking example was observed by marketer Rand Fishkin: when asking ChatGPT about top restaurants in Seattle, the AI repeatedly listed a restaurant that “hasn’t existed in many years” and even mentioned a chef who had passed away a year prior sparktoro.com. The model was pulling from training data that hadn’t caught up with reality.

Why do such mistakes happen? Simply put, LLM training data is updated only periodically (often every few months or more), whereas search-based responses can reflect information that is current to the day or hour. Industry experts note that this gap means search-integrated AI responses have the advantage of real-time content hosanagar.substack.com. An LLM that can access up-to-date web content (through search engine integration, plugins, or retrieval augmentation) will prioritize that fresh content to provide timely, accurate answers. Microsoft’s Copilot (powered by GPT-4) is a case in point; it augments the base LLM with live search results from Copilot’s index. As a result, GPT-4 with Bing can pull updated data via web search to answer queries, ensuring the response isn’t limited to year-old knowledge upgrad.com. By incorporating current web data, these AI systems aim to avoid the pitfalls of stale training info and give users answers that reflect the latest news, product releases, or research.

There is also a user-experience incentive: people expect AI assistants to be as up-to-date as a Google search. If an AI tool repeatedly returns outdated recommendations, users lose trust. To maintain credibility, LLMs “prefer” to draw on sources that are current and authoritative whenever possible. In practice, this means that an LLM-enabled search (like Copilot’s AI mode or Google’s SGE) will often favor newly published content, product announcements, recent reviews, fresh blog posts, or updated documentation over older data from its model memory. It’s a bit like how Google’s algorithm has long given some weight to content freshness; now, AI answers are doing the same to avoid hallucinations and irrelevance.

Another factor is the emergence of retrieval-augmented generation (RAG), where an LLM fetches relevant documents from a knowledge base or the web and uses them to construct its answer. This retrieval step inherently prioritizes whatever content is most relevant and recent in the index it searches. As a result, even if an LLM “knows” something from training, it might lean on a newer article or source if that better answers the current query. In summary, LLMs prioritize real-time content to enhance accuracy, relevance, and trustworthiness qualities that static training alone cannot guarantee in a fast-changing world.

Why Fresh Content Matters for Product Discoverability

For businesses, especially B2B SaaS and tech providers, this shift toward real-time content in AI has one clear implication: your product is more likely to be discovered (or recommended) by an AI if there is fresh, relevant content about it on the open web. Gone are the days when simply having a well-SEO-optimized homepage and a handful of backlinks would ensure you show up in search results. If today’s buyers are asking ChatGPT or another LLM, you need to “appear” in the answers those models give sparktoro.com.

The challenge is that LLMs don’t use traditional search ranking signals like links and clicks when determining answers. As SparkToro’s Rand Fishkin explains, “the currency of large language models is not links… it’s mentions, specifically, words that appear frequently near other words across the training data” sparktoro.com. In other words, AI models decide what to say based on patterns they’ve seen. If your brand or product hasn’t been part of those patterns (in either their trained knowledge or in the content they retrieve), the AI has no reason to mention you.

Now that LLMs can draw from real-time info, the discovery process is increasingly about what the AI has “seen and trusts” in recent content lead-spot.net. Think of a B2B buyer asking an AI, “What are the best cloud storage solutions for enterprise?” A traditional search engine might return a list of links (which a user could click to find vendors, including perhaps your company). But an AI like ChatGPT will answer with a narrative: “Companies often choose solutions like VendorA, VendorB, and VendorC for enterprise cloud storage due to their security and scalability.” If you are VendorX hoping to be in that answer, the AI needs to have encountered authoritative, timely content that includes “VendorX” in the context of “enterprise cloud storage” and associated positive attributes.

Recent research confirms this new reality. LeadSpot, a B2B marketing firm specializing in AI-era content, observes that in 2025, buyers don’t just use Google; “they ask AI”, and if your content isn’t part of what the models have seen, “you’re invisible in the new way to search.” lead-spot.net. That means companies have to make sure their expertise, case studies, and product news are present in the places LLMs are likely to look. According to Forrester, these AI-rich search experiences (they call them “organic-first expertise engines”) like ChatGPT and Google’s AI mode are “dramatically reshaping traffic patterns and media mixes”, pulling users away from traditional search browsing forrester.com. By 2025, SEO is no longer just about ranking on a Google results page, but about securing a spot in AI-generated answers b2b-marketing.transmissionagency.com.

Not surprisingly, B2B buyers are embracing this shift en masse. A recent Forrester survey (cited by Transmission) found that by late 2024, nearly 90% of B2B buyers use generative AI tools in at least one stage of their buying journey b2b-marketing.transmissionagency.com. Peer recommendations and analyst reports still matter, but AI assistants have joined that trusted mix b2b-marketing.transmissionagency.com. These buyers are using ChatGPT to rapidly evaluate vendors in minutes, not weeks, without ever visiting a single landing page b2b-marketing.transmissionagency.com. And if ChatGPT’s summarized shortlist doesn’t include you, you might never even be considered. It’s a stark “appear or disappear” scenario: “Optimize for LLM visibility, lest you risk disappearing from the consideration set entirely,” warns one 2025 B2B marketing report b2b-marketing.transmissionagency.com.

The implications for product discoverability are huge:

• Zero-Click Exposure: In the AI answer paradigm, the user might not click at all because the answer is the destination. So being one of the few sources or brands named by the AI is priceless. As one guide put it, “Users are increasingly satisfied with AI-generated summaries… This makes a direct citation or mention by an LLM incredibly valuable.” clickbank.com. Unlike a search results page where ten companies might be listed, an LLM answer might only cite one or two sources (or sometimes none at all) clickbank.com. The spoils go to the top one or two players the AI deems worthy.

• Trust and Authority Matter More: AI models are programmed to avoid presenting dubious or low-quality info. They look for trust signals like authority of the source, consistency of information, corroboration across multiple sources when choosing what to present clickbank.comlead-spot.net. If your company is mentioned only in your own blog and nowhere else, an AI might treat that with skepticism (or miss it entirely if it wasn’t in its training data). But if your insights or product are mentioned on respected industry blogs, news sites, or research portals, the AI is far more likely to “trust” that and include it. Repetition and consistency across the web build this trust. In fact, LLMs effectively reward “repetition + reputation”: content that shows up in multiple trusted places and says the same thing about your brand signals credibility lead-spot.netlead-spot.net.

• Real-Time Relevance: In fast-evolving fields like cybersecurity, developer tools, or SaaS, new developments are constant. An AI pulling from real-time content will highlight up-to-date solutions. For example, if a critical cybersecurity threat emerges and a user asks an AI for mitigation tools, the AI will emphasize products mentioned in the latest security bulletins or analyses. If your cybersecurity product team quickly publishes a well-distributed advisory about how to handle the threat, the AI is more likely to mention your solution. If you stay silent or gated, it won’t appear. In essence, product discoverability now favors those who contribute timely knowledge to the public domain.

To put it bluntly, brands can no longer rely on buyers “finding” them through a web search ad or a homepage visit; increasingly, the AI itself is the new gatekeeper. As Gartner analysts have noted, these LLM-powered experiences are an “inflection point,” and businesses must adapt or risk obsolescence hbr.org. The next sections will explore how exactly companies can adapt by shifting from traditional SEO thinking to an “LLM SEO” strategy focused on getting cited in AI answers.

From SEO to LLM SEO: Earning a Spot in AI Answers

If large language models operate on different rules than traditional search, how do we optimize for them? Enter LLM SEO (Large Language Model Search Engine Optimization), a new discipline aimed at making content visible and favorable to AI platforms. LLM SEO is about structuring and distributing your content such that it gets cited, summarized, or retrieved by generative AI tools like ChatGPT, Claude, Perplexity, and Google’s Gemini lead-spot.netlead-spot.net. It recognizes that “it’s not enough to rank for keywords anymore; you have to earn visibility without clicks.” lead-spot.net In other words, success is when the AI mentions your brand or uses your content in its answer, even if the user never visits your site.

Let’s break down how LLM SEO, or AI SEO, differs from the old playbook:

• Goals: Traditional SEO aimed for high rankings on search engine results pages (SERPs) to drive clicks to your site. LLM SEO’s goal is AI citation and inclusion of your brand or content being part of the answer itself lead-spot.net. Instead of a blue link, you want a mention in the AI’s response.

• Strategies: The old SEO toolkit was keywords, backlinks, and technical optimization of webpages. Those still matter, but LLM SEO places more emphasis on content distribution, structured data, and authority signals lead-spot.net. It’s about making sure the AI sees your content in the first place (training data or retrieval sources) and recognizes it as trustworthy.

• Outcome Metrics: Traditional SEO measures success by clicks, traffic, and SERP position. LLM SEO measures success by zero-click visibility: how often does an AI cite or summarize your content, and what uplift in brand awareness or direct traffic results from those AI mentions lead-spot.netlead-spot.net. For example, after implementing LLM-focused strategies, some companies track increases in branded searches (people directly searching your brand after hearing about it from AI) or even direct traffic from users who were influenced by an AI recommendation lead-spot.net.

How can brands practice LLM SEO effectively? Recent practical guides and case studies point to a few key tactics:

1. Syndicate and Distribute Your Content Widely. One of the most powerful levers is content syndication: republishing and sharing your content across multiple reputable platforms. The logic is simple: wider distribution = higher AI trust lead-spot.net. When your whitepaper or case study isn’t just on your website, but also featured on industry association sites, niche blogs, or platforms like Reddit and LinkedIn, LLMs are more likely to encounter it and treat it as significant. LeadSpot’s research shows that content syndicated to 10 or more trusted domains was up to 5× more likely to appear in LLM answers lead-spot.net. Likewise, content appearing across 20+ partner sites led to a 5-6× higher likelihood of being mentioned by AI in their client campaigns lead-spot.net. The takeaway: don’t hide your best content on your own site alone. Get it out on portals, guest posts, and any credible outlet that will host it (ensuring you use canonical tags properly, as we’ll note).

One reason syndication works is that LLMs “learn” from high-authority public datasets, for instance, well-known publisher networks, research hubs, and respected blogs lead-spot.net. By placing your insights into those streams, you are effectively “training the AI” with your content lead-spot.net. In B2B tech, that could mean getting a snippet of your whitepaper featured on a site like TechCrunch or a popular industry Substack, or having your data cited on Wikipedia or a government tech report. Those placements become part of the AI’s knowledge. As an added bonus, buyers who see your syndicated article on a third-party site might later prompt an AI with your brand name (“What does ChatGPT say about [Your Company]’s solution?”). These “ChatGPT referral mentions” can drive significant traffic and awareness lead-spot.net.

2. Provide Structured, Machine-Friendly Information. LLMs appreciate content that is easy to parse and factual. This means using structured formats and data where possible. For instance, Q&A-style content is highly LLM-friendly. An AI summarizer like Perplexity or Google’s SGE often extracts direct answer pairs from text. If your blog post literally has a section titled “Q: What is the difference between X and Y?” followed by a concise answer, that’s gold the AI can lift that answer directly (potentially citing you) lead-spot.net. Similarly, using clear headings, short paragraphs, bullet lists, and summary tables makes it easier for the AI to digest and reuse your content lead-spot.net. Long, unstructured walls of text are more likely to be ignored or misinterpreted by AI.

Another important aspect is schema markup (structured data like JSON-LD). By adding schema (FAQ schema, HowTo schema, product schema, etc.) to your pages, you not only help Google’s crawler but also any AI that taps into the web data. As LeadSpot notes, LLMs (and search engines) can better understand content that’s annotated with schema, which clarifies the context and facts lead-spot.net. In fact, some savvy teams are now publishing dedicated machine-readable “fact sheets” about their company or products. For example, a startup might host a file at yourcompany.com/facts.jsonld containing up-to-date facts: founders, funding, product specs, awards, etc. Why? Because “LLMs don’t cite PDFs, they cite facts” growthmarshal.io, and providing those facts in a structured feed is like hand-feeding the AI your information. In one remarkable case, a fintech startup that couldn’t outrank a big bank’s website simply published a lightweight JSON-LD facts endpoint, and within weeks, Google absorbed this into its Knowledge Graph and ChatGPT started surfacing that startup as a “regulated alternative” in answers about the domain, “no ad spend, no backlink crusade” required growthmarshal.io. This demonstrates the power of being explicit, structured, and current with your data: the AI picked up the startup’s authoritative fact file and began recommending it alongside a trillion-dollar competitor.

3. Emphasize Credibility and Consistency. Trust is the cornerstone of getting cited by AI. You should ensure your content and brand come off as reputable and consistent everywhere. Some best practices include: use the same brand name, tagline, and facts in every publication (consistency helps the AI connect the dots that all those mentions are the same entity) lead-spot.net. Always include a brief company bio and link when you syndicate content, so any site re-publishing your piece clearly ties back to you lead-spot.net. This helps LLMs confidently associate your brand with the content topic in question lead-spot.net. Also, keep metadata accurate: titles, meta descriptions, alt text – as these often end up in what AI “sees” about your page lead-spot.net.

Crucially, pick your distribution targets with care. Focus on niche, high-authority sources in your industry lead-spot.net. A mention in a well-regarded industry journal or a community site (like a popular developer forum for a dev tool product, or a cybersecurity hub for a security product) carries more weight than a generic content farm. LeadSpot’s analysis suggests that industry-specific publishers and professional communities are more likely to be in LLM training datasets than broad sites lead-spot.net. In short, to get the trust factor, get cited where the trustworthy voices in your field congregate.

4. Use Canonicals and Ungate Your Best Content. When syndicating or sharing content on multiple platforms, use canonical URLs or other signals to avoid confusion. A common SEO fear is duplicate content, but with proper use of rel="canonical" linking back to your site’s original, you can safely spread content without losing SEO credit lead-spot.netlead-spot.net. More importantly for LLM SEO, you want the AI to see the content at all – which means avoid strict gating. Content behind login forms or paywalls is generally invisible to LLMs lead-spot.net. If your most valuable case study requires an email form, consider publishing a substantial summary or key findings publicly. You might even “leak” some of the juicy stats through a guest article or press release so that the facts make it into the public web that AI scans. Remember, if the AI can’t read it, it can’t recommend it.

By applying these strategies, companies have reported tangible improvements. LeadSpot cites real client data: for instance, a 34% increase in AI citations after syndicating content to 5+ domains, and over 300% increase in prompt-driven traffic (users asking AI about them) following a targeted content campaign lead-spot.net. Another campaign saw case studies syndicated to 20+ sites start getting cited by ChatGPT and Claude when users asked about related products lead-spot.net. And beyond just AI mentions, there were side benefits like a 24% lift in branded search traffic and 19% more direct traffic, implying that the AI exposure was sending more people to seek out the brand directly lead-spot.net. Perhaps most strikingly, leads influenced by this content had a 37% higher sales-qualified conversion rate compared to those coming from paid ads lead-spot.net. That suggests that AI-driven visibility isn’t just vanity, it attracts prospects with higher intent or trust, arguably because being cited by an AI carries an implied endorsement.

In summary, LLM SEO is about feeding the answer engines what they need: clear, widely-available, authoritative content that can be easily mined for answers. It shifts the marketing mindset from just attracting human clicks to also influencing AI selection. Next, we’ll look at how this can be done efficiently, even by small teams, and how one emerging platform is making “AI-optimized content” more accessible without pouring money into ads.

Fresh Content, No Ads: Strategies for Cost-Effective AI Visibility

A major upside to this new approach is that earning AI citations is an organic play, not a pay-to-play ad system. There’s currently no way to buy your way into a ChatGPT answer the only path in is through relevance and authority. That levels the playing field for smaller companies that can’t outspend big competitors on Google Ads. By investing in content and smart distribution, brands can achieve disproportionate visibility “with no ad spend” – as we saw in the example of the fintech startup whose 2 KB fact file helped them punch above their weight growthmarshal.io. For solopreneurs and lean marketing teams in B2B, this is welcome news: your expertise can outrank someone else’s budget, if you package it right for AI.

However, creating the right content and getting it everywhere it needs to be can be challenging for small teams. This is where tools and platforms built for AI-era SEO come into play. One such solution is outwrite.ai, a newcomer designed explicitly to help brands get noticed by both traditional search engines and LLMs. Outwrite.ai is an AI-powered content platform that guides users through writing content that “ranks, resonates, and gets picked up by the systems that power modern discovery.” medium.com In practice, that means when you use Outwrite, you’re not just getting help with grammar or keyword density; the tool is also giving you real-time optimization feedback to ensure your article is structured and formatted in ways that AI models prefer medium.com. For example, it might prompt you to add a question heading here, a statistic there, or to break up a paragraph all to align with what makes content machine-readable and authoritative.

Importantly, Outwrite.ai was built with the resource-strapped team in mind. The platform advertises itself as a “growth partner” for founders, freelancers, and small marketing teams – those who may not have an SEO expert, PR agency, and AI prompt engineer all on staff medium.commedium.com. It combines these functions by helping generate the content (using AI suggestions) and then optimizing it in one workflow medium.com. For instance, a solopreneur can input a prompt about a topic they want to cover, and Outwrite will help outline the post, suggest relevant headings (which often align with what people ask AI about), and ensure the final copy includes the kind of structured elements (lists, Q&As, schema markup recommendations) that we discussed earlier. The goal is that the final piece isn’t just SEO-friendly for Google, but “AI-friendly” for ChatGPT, Perplexity, and others medium.commedium.com.

Consider how this ties back to getting cited with no ad spend: Imagine writing a strong thought leadership article on a problem your SaaS product solves. Traditionally you’d optimize it for Google and maybe run some ads to get eyes on it. With an AI-optimized approach, you instead focus on making that article rich with data, answers, and clarity. You publish it and also syndicate it to, say, a Medium publication and an industry newsletter (manual effort, but aided by the fact your content is already high-quality and structured). If you used a tool like Outwrite.ai, it likely saved you time and ensured you hit the right notes. Now, when potential customers ask an AI assistant about that problem, your article is primed to be one of the sources the AI picks up or cites. You start getting brand mentions in AI-driven answers that users screenshot or directly act on. All of this happens without spending a dime on advertising the traffic and leads are essentially free, driven by the merit of your content and intelligent distribution.

This approach is already yielding results in the field. In some B2B niches, LLM citations are reportedly outperforming traditional search traffic in driving prospects reddit.com. That means more people are coming to those businesses from what an AI recommended or cited than from clicking Google results. It’s a profound shift: the AI is acting like a new kind of search engine, one where the top “result” is woven into the answer. Being that woven-in result should be a key objective of any modern content strategy.

Let’s also not forget the compounding advantage of AI visibility. When an AI cites a source or brand, it often reinforces a cycle: users see it and may specifically ask about that source again (“ChatGPT, who is Company X? what do they offer?”), and the AI, having seen its own citation or the content before, continues to give it attention. Also, unlike a fleeting ad impression, a citation in an AI answer has a patina of credibility (“the AI mentioned them, so they must be noteworthy”). This can shorten the trust-building phase with new prospects. For example, a CTO might be skeptical of a random startup they find via Google, but if an AI assistant told them that “according to a Forbes article, [Startup] is a top innovative solution in this space,” they’ve got third-party validation upfront.

Of course, achieving this doesn’t happen overnight. It requires a combination of quality content creation, SEO know-how, and savvy PR/distribution. That’s exactly the combination of skills that tools like Outwrite.ai and services like LeadSpot aim to streamline. Outwrite provides the content creation and on-page optimization edge; LeadSpot (and similar agencies) help with off-page syndication and getting content into those high-authority channels. Together, they exemplify an “AI SEO” toolkit for marketers.

Conclusion: Adapt to the AI Era or Fade Away

The rise of real-time content prioritization by LLMs is not a temporary blip it marks a fundamental change in how information is found and filtered. We are entering an era where being the best answer matters more than being the best search result. B2B brands, especially in tech-driven sectors, must recognize that buying patterns are shifting: buyers trust AI recommendations, skip traditional search steps, and expect answers on demand b2b-marketing.transmissionagency.comb2b-marketing.transmissionagency.com. In this environment, product discoverability hinges on feeding those AI recommendation engines with the right inputs.

Fortunately, the path to adaptation is clear. It starts with an honest audit of your content: Is it up-to-date, factual, and structured for easy digestion? Is it present beyond your own website – on the websites and networks that AIs are likely to scan? Are you contributing to the public knowledge in your domain (ungated), or is your insight locked in a PDF or behind a form? As the saying goes in LLM SEO, “If your best content only lives on your site, you’re missing out on AI visibility” lead-spot.net. The brands that will thrive are those who ensure their expertise is broadly distributed, clearly formatted, and consistently branded across the digital ecosystem lead-spot.net.

The good news is that adapting to this AI-driven landscape can be highly cost-effective. Instead of pouring budget into more ads or chasing ever-elusive organic social reach, you can invest in content and optimization that has compounding returns. A well-syndicated whitepaper or a widely-cited benchmark study can keep surfacing in AI answers for months, continually driving leads with zero ad spend after creation. In essence, the effort you put into becoming an “AI-visible” brand is a form of building equity in the new search paradigm – an investment that pays dividends over time.

To recap, LLMs prioritize real-time content over static training data because it makes them more accurate and useful. For businesses, this means your newest content can have outsized influence on whether an AI recommends your product. By embracing LLM SEO tactics – from syndication and structured data to leveraging tools like Outwrite.ai for content creation – you position your brand to be the one that AI trusts and talks about. As one LeadSpot expert succinctly put it, “LLMs reward relevance, repetition, and trust. Syndication helps you build all three.” lead-spot.net In a world where AI might soon be the first (or only) touchpoint in a customer’s journey, building that trust with the machines is as important as building it with humans.

In the final analysis, the brands that succeed will be those who understand that SEO now means optimizing for both humans and machines. By aligning your marketing strategy with how LLMs ingest and output information, you won’t just keep up with the changes, you’ll ride the wave to reach prospects in a powerful new way. The opportunity is there for the taking: adapt your content for AI now, and ensure that when your future customers ask the next-gen digital assistant for a recommendation, it’s your name that comes up as the answer.

Sources:

• Fishkin, Rand. SparkToro Blog – How Can My Brand Appear in Answers from ChatGPT…?, Oct 2024 sparktoro.comsparktoro.com.

• LeadSpot Research – Why Content Syndication Is Now Critical for LLM SEO, 2023 lead-spot.netlead-spot.netlead-spot.net.

• LeadSpot Blog – How Content Syndication Fuels LLM SEO: A Practitioner’s Guide, 2025 lead-spot.netlead-spot.netlead-spot.net.

• Transmission B2B (R. Abbott) – How AI is Transforming B2B Content Strategy in 2025, May 2025 b2b-marketing.transmissionagency.comb2b-marketing.transmissionagency.com.

• Forrester (N. Lai) – GenAI Reshapes Shopping and Revives SEO, Jul 2025 forrester.comforrester.com.

• ClickBank (D. Thrasher) – Optimize Content for LLMs and AI Search: 25 Tips for 2025, Jun 2025 clickbank.comclickbank.com.

• GrowthMarshal – Publishing a Public Fact API (JSON-LD) for Instant Crawl & Citation, 2023 growthmarshal.io.

• Outwrite.ai (J. Buckley) – Introducing Outwrite.ai: AI-Powered Content for Search and AI, Jul 2025 medium.commedium.com.