I’m introducing support for GPT-5.6 in Profound, bringing OpenAI’s newest flagship model family directly into the workflows I rely on for advanced AI performance.
With GPT-5.6, I can work across the new Sol, Terra, and Luna tiers, giving me the flexibility to support everything from frontier reasoning to high-throughput production workloads.
I’m especially focused on what this means for agentic workflows, coding, research, and enterprise knowledge work. GPT-5.6 delivers meaningful improvements in capability, reliability, and efficiency, making it easier for me to apply AI across a wide range of business use cases with more confidence.
I am introducing support for Grok 4.5 in Profound, bringing SpaceXAI’s newest flagship model into workflows built for deeper, more capable AI analysis.
Grok 4.5 is designed for agentic workflows and knowledge work, which makes it a strong fit for teams and operators who need AI systems that can reason, assist, and move complex tasks forward with more context.
With this support now available in Profound, I can use Grok 4.5 as part of a broader AI workflow and explore how its capabilities help with research, strategy, automation, and day-to-day knowledge work.
I use Conductor’s MCP Server to ground the AI tools my team already relies on in verified AEO and SEO intelligence, instead of depending on a stale snapshot of the web.
A bold launch visual introduces an AEO and SEO Intelligence Layer, framing verified search and AI visibility data as a modern layer for marketing teams.
I’m introducing FactCheck as a new way for brands to understand how accurately AI engines describe them at scale.
AI engines can make claims about my brand that simply are not true. With FactCheck, I can measure what is accurate, identify what is wrong, and see which sources are driving those errors.
That visibility matters because AI-generated answers are increasingly shaping how people discover, evaluate, and trust brands. FactCheck helps me move from guessing about AI accuracy to actually analyzing it with clarity.
I’m thrilled to introduce the latest addition to Profound: the External MCP Connectors. With these, I’ve found it incredibly easy to link my favorite CMS tools, project trackers, and team communication platforms directly to Profound via MCP.
This seamless integration has transformed the way I manage projects, allowing me to streamline workflows and enhance team collaboration. Now, all my critical tools are accessible from one central hub, boosting my productivity like never before.
Try it out and see how Profound can help you connect everything you need in one cohesive system. It’s a game-changer for efficiency and team synergy.
In this report, I’m excited to share the findings from a research study I conducted with my team on the emerging field of Agentic Search Optimization, or ASO. We’ve developed a strategic framework that businesses and marketing agencies can leverage to stay ahead in this dynamic landscape.
What is Agentic Search Optimization?
Agentic Search Optimization, often referred to as Agentic GEO, involves optimizing your online presence so AI agents choose your products or services on behalf of users. Unlike Generative Engine Optimization (GEO), which focuses on gaining human trust after an AI recommendation, ASO targets conversions by persuading AI to recognize your offering as the best choice for users.
ASO might seem similar to GEO since both aim to drive leads or purchases, but there’s a significant difference: GEO involves human decision-making, while ASO transfers that responsibility to intelligent bots.
For instance, in ASO, a user doesn’t ask ChatGPT for the best gift card platforms. Instead, they might say, “Send $50 holiday gift cards to my remote team at their preferred stores”. The AI agent interprets, evaluates options, and makes the purchase autonomously.
So far, the ASO landscape hasn’t been thoroughly researched to identify universally accepted best practices. Our study attempts to build a framework outlining agentic search stages, determinants of company selection, and actionable tactics to influence search results.
The Study
Between March 4, 2026, and June 10, 2026, our research team conducted 2,417 agentic search commands using popular AI agents across the U.S. These commands were task delegations such as purchases, bookings, quote requests, or vendor shortlists, rather than just informational quests. We observed the entire behavior chain of agents, including sub-queries, source retrieval, candidate evaluation, and the final action or inaction.
Our analysis revealed that ASO follows three key stages: Retrieval, where AI scans the web (primarily Google) for top results and compares them to its beliefs; Evaluation, where the best company, product, or service is chosen to fit user needs; and Action, where the task is completed, often involving a transaction.
Through our research, we’ve identified three crucial insights:
Agents Review Complete Results: Across all commands, AI agents opted for the platform’s top-ranked recommendation 44.6% of the time. However, they selected options ranked 4th or lower in 38.2% of cases, demonstrating a choice based on suitability over rank.
Agents Possess Predetermined Brand Beliefs: In 81.6% of evaluations, agents relied on pre-existing brand beliefs established during their training or via web searches, indicating that brand perception heavily influences ASO.
Agents Forfeit Companies Unable to Transact: If a conversion page was machine-actionable, agents completed 78.3% of attempts. When not, completion fell drastically to 9.6% with many agents substituting transactable competitors without user input.
This study further explores the ASO process in detail, showcasing tactics that our team tested and validated in early 2026.
The Three Stages of Agentic Search
When I delegate tasks to an AI agent, it performs query interpretation, creating an average of 6.3 sub-queries. The process proceeds through three stages: Retrieval, where it constructs a result set; Evaluation, narrowing choices to the best fit; and Action, executing the conversion. During this, agents cross-reference claims with multiple sources; inaccuracies result in immediate rejection of a candidate.
To benefit from agentic search, companies must achieve two goals: securing the #1 rank on AI platforms, aiding the Retrieval stage, and clearly defining their fit, crucial for Evaluation. Technical prowess ensures seamless Action.
Stage 1: Retrieval
The Retrieval stage encompasses traditional GEO: agents scan the web and build a pool of companies or products. All previous GEO strategies apply here—Comparison blogs, metric pieces to boost rankings, and brand authority statements that AI platforms might trust help form this candidate set.
What’s innovative in ASO is understanding the AI’s pre-existing beliefs. This necessitates mapping the AI Belief Landscape, an audit scoring AI model beliefs about a brand, alongside sentences exemplifying these beliefs.
This assessment guides marketers in pinpointing areas where their brand falls short in the eyes of AI, a crucial step in adjusting perceptions during ASO.
Tactic: AI Belief Correction
AI Belief Correction involves publishing evidence to transition model beliefs from weak to strong. For instance, for a skincare brand like Rejuve, enhancing its perception involved producing detailed scientific explanations onsite and acquiring third-party verification offsite, establishing credibility.
Stage 2: Evaluation
Evaluation diverges drastically from traditional SEO. Agents, not humans, select candidates based on user knowledge. Our study showed agents broke user commands into prioritized categories: Hard Requirements, Important, Nice to Have, and Optional, with evaluations leading to a “Fit Verdict.”
Properly communicating fit information is crucial. Content detailing product suitability increases selection odds.
Tactic: Suitability Pages
Suitability Pages—criterion-specific pages that declare who a product is suited for and, critically, who it isn’t—are vital. Noting “non-fit” conditions paradoxically increases credibility by adding authenticity, improving agentic evaluation rates.
Stage 3: Action
Achieving the third stage requires technical readiness: machine-readable pages and APIs enable seamless agent transactions. The disparity in conversion rates between machine-actionable and non-actionable setups is significant, underscoring the importance of technical preparation.
The Future of Agentic Search Optimization
I anticipate that AI-driven commercial transactions will rise dramatically in the coming years. As that shift occurs, here’s what I foresee:
Suitability content will become essential: Just as landing pages are vital for SEO today, clearly defined fit will become mandatory for ASO visibility.
Tougher verification layers: Securing third-party endorsements will become even more critical, emphasizing PR’s value in ASO.
Selection share will surpass rankings: The focus will shift to actual AI agent selections over mere recommendation visibility.
Marketers excelling in GEO are already poised for agentic success, but comprehensive strategy across all stages is necessary for ultimate triumph.
Downloading This Report & Inquiries
Got questions or need a PDF copy of this report? Feel free to contact us here.
Discover more about our Agentic Search Optimization services by reaching out here.
Appendix A: Command Categories in Agentic Search Study
Category
Commands
Ecommerce purchasing
612
B2B software evaluation & signup
489
Travel booking
343
Professional services inquiries
291
Consumer & local services
274
Financial products
213
Healthcare services & products
195
Total
2,417
Appendix B: # of Commands Issued in Agentic Search Study
AI Agent
Commands Issued
Notable Behavior
ChatGPT (agent mode)
884
Most likely to verify claims against third-party sources before acting
Gemini (agentic tasks)
519
Strong integration with data feeds; likely to abandon when pages aren’t machine-actionable
Claude (browsing & computer use)
397
Thorough evaluator; applies the largest number of distinct criteria per command
Perplexity Comet
462
Widest retrieval fan-out; often selects options ranked outside top 3
Other browser agents
155
Diverse behavior observed; included for completeness
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.
The latest updates to ChatGPT Ads are empowering me as an advertiser with greater control over how I manage my campaigns, especially when it comes to pacing, location targeting, and engaging with ads more effectively.
OpenAI’s recent rollout of updates to the Ads Manager Beta is expanding my capabilities in the realm of campaign pacing, targeting, and reporting. They’re also quietly testing intriguing new ad experiences within ChatGPT.
With these ongoing enhancements, OpenAI is clearly investing in building a robust advertising platform. This makes ChatGPT an increasingly attractive channel for both performance and brand advertising.
What’s new in Ads Manager Beta:
Daily Budgets are Here. Now, I have the option to choose between a daily or a lifetime budget when setting up new campaigns.
Currently, daily budgets apply only to newly launched campaigns. This change provides me with the flexibility to better manage pacing and spending, especially for ongoing campaigns or those requiring tighter control.
Enhanced Geo Targeting. OpenAI is introducing more detailed location targeting options across the U.S.
Now, I’m able to target campaigns by state, designated market area (DMA), and zip code, allowing for more precise audience targeting.
These targeting settings can be applied either during campaign setup or modified later within campaign settings. This update aligns ChatGPT’s ad tools more closely with familiar location controls on platforms like Google and Meta.
Aggregate Totals in Reporting Views. Now, the Ads Manager table views display aggregate totals for essential metrics such as impressions, clicks, and spending.
Having these totals available across campaign, ad group, and ad-level reporting views helps me quickly assess performance without the need for data exports.
Testing New ChatGPT Ad Experiences. In tandem with the Ads Manager updates, OpenAI has begun testing new ad experiences within ChatGPT.
Some ads now feature dynamic calls-to-action (CTAs) such as:
“Shop Now”
“Book Now”
“Sign Up”
“Learn More”
OpenAI indicates that CTAs are automatically chosen based on ad creative and destination experience, with the possibility of advertiser controls for CTA selection in the future.
OpenAI describes this feature as a lightweight enhancement aimed at improving user understanding and engagement with ads seen in ChatGPT.
Why I Care. Essentially, these updates show that OpenAI is committed to developing a sophisticated, performance-driven ad platform within ChatGPT.
With features like daily budgets and detailed geo-targeting, I’m armed with greater spend and target audience control. These tools are indispensable for mature advertising platforms.
The introduction of dynamic CTAs indicates that OpenAI is optimizing ads for higher engagement and conversion, paving the way for performance-centric ad formats in the future. For brands like ours dipping our toes into AI-native advertising, these updates signal that we’re moving beyond initial testing phases to establish ChatGPT as a viable media channel.
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.
Imagine scrolling through Google Search and effortlessly collecting items from various retailers into one convenient Universal Cart. That’s exactly what Google is offering now, a seamless shopping experience that allows me to keep all my desired products in one place and check them out with a single click using Google Wallet.
Recently announced by Vidhya Srinivasan, VP/GM Ads & Commerce, Google’s Shopping Graph has reached an impressive 60 billion product listings, a significant jump from the 50 billion earlier this year. This growth reflects Google’s commitment to enhancing our online shopping experiences.
Universal Cart. With Universal Cart, I can add items from multiple stores while browsing Google Search, or even when I’m on YouTube and Gmail. It’s so liberating not to jump from site to site!
Here’s how it works: as I shop, Google helps me find the best deals and in-stock availability across different retailers. Then I simply choose my preferred store for checkout, leaving no room for the hassles generally associated with online shopping.
Google’s Universal Cart is smart too! Imagine you’re assembling a custom PC—your cart will alert you if any parts are incompatible and suggest compatible alternatives. Built on Google Wallet, it even recognizes payment perks and loyalty offers, revealing savings opportunities I might otherwise overlook.
Merchants. Google has partnered with renowned merchants like Nike, Sephora, Target, Ulta Beauty, Walmart, Wayfair, and Shopify sellers such as Fenty and Steve Madden. This wide array ensures I have plenty of shopping options!
Availability. This feature will roll out in the U.S. this summer, initially available on Google Search and the Gemini app, with plans to expand to YouTube and Gmail soon after.
UCP and AP2. Google is also extending the Universal Commerce Protocol to Canada and Australia soon, with plans for the U.K. The Agent Payments Protocol will support secure, accountable transactions by authorizing agents to shop on my behalf according to my specific criteria.
Moreover, Google’s innovative features are set to debut across Google products, starting with Gemini Spark. It’s an exciting time to be an online shopper!
