AI search visibility may vary significantly depending on which generative AI engine a buyer uses, according to new measurement data from Treyci. The AI visibility intelligence company found that different AI engines can recommend the same brands at substantially different rates, potentially making single-platform visibility checks an unreliable measure of brand performance.

In one measured B2B software category, Treyci found that one AI engine referenced tracked brands in 81% of purchasing-related answers, compared with 43% for another engine. The difference represents nearly a two-fold visibility gap despite the engines receiving comparable buying questions during the same month.

The findings are based on Treyci's measurement methodology, which evaluates buying-intent questions across ChatGPT, Perplexity, Gemini and Grok. The company runs approximately 100 questions per category, including queries such as "best X for mid-size teams," alternatives and pricing comparisons. Each prompt is repeated three times per engine every month, producing more than 1,200 scored AI responses.

The analysis highlights a challenge that traditional search monitoring does not fully capture: AI-generated answers can vary between both platforms and individual sessions.

According to Treyci, the same engine can return different vendor lists when presented with the same buying question at different times. This means a single screenshot or isolated AI search result may demonstrate that a brand appeared once, but does not necessarily establish a reliable visibility trend.

Market Landscape

The findings add measurement complexity to the emerging discipline of Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO). Brands increasingly need to understand how they appear when prospective customers ask AI systems for recommendations, alternatives or product comparisons.

Unlike conventional search results, AI-generated responses can synthesize information from multiple sources. Treyci says its analysis found that AI engines frequently cite third-party review platforms, comparison content and industry publications rather than vendor websites.

That dynamic could shift how marketing teams approach AI visibility. Publishing content on a company website may remain important, but third-party sources that AI systems rely on can also influence whether a brand enters a generated recommendation.

Treyci also found a gap between adoption and measurement. In a scan of 100 B2B SaaS companies, 41 had published an llms.txt file for AI crawlers, while relatively few could quantify whether their AI visibility efforts had changed recommendation frequency.

Strategic Outlook

For B2B marketers, the central issue is moving from anecdotal AI visibility checks toward repeatable measurement. Tracking one engine or one query can obscure differences between AI systems and the variability of individual responses.

Treyci's methodology instead treats AI visibility as a distribution that needs to be observed across multiple engines and repeated queries. This approach could become increasingly relevant as AI assistants influence early-stage purchasing research.

The commercial challenge is also different from conventional web analytics. When a buyer sees a company in an AI-generated shortlist but does not click through, the brand may receive no corresponding impression, session or referral record.

As a result, marketing teams may need dedicated visibility metrics to understand whether their brands are being considered before measurable website activity occurs.

Top Insights

• Treyci found an 81% versus 43% brand-reference gap between two AI engines in one B2B software category.
• The analysis scored more than 1,200 AI answers.
• Treyci measures ChatGPT, Perplexity, Gemini and Grok.
• Repeated prompts produced different vendor lists within the same AI engine.
• AI engines frequently cite third-party sources in purchasing answers, according to Treyci.
• 41 of 100 B2B SaaS companies in Treyci's scan had published an llms.txt file.

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Quantum Metric's 2026 Customer Voice Benchmark identifies a growing disconnect between brands' investment in customer feedback and consumers' willingness to provide it. The research describes this as a "customer listening gap," where customers increasingly question whether the feedback they provide will result in meaningful action.

The benchmark found that half of consumers had not given a brand direct feedback during the previous six months, while 45% of digital brands said they had increased investment in feedback channels over the past year. The findings also show a potential retention impact: 56% of consumers said they had left a brand that continued to ignore their feedback.

The research points to declining confidence in conventional feedback mechanisms. Fewer than one in five consumers consider surveys an effective way to have their feedback heard, while one-third said they do not feel heard by digital brands.

The challenge extends into digital operations. Only one in four digital leaders reported having daily access to recent customer feedback, and 52% said their organizations have no formal process for acting on the feedback collected.

Quantum Metric's findings suggest that collecting customer sentiment without connecting it to behavioral information and operational processes can limit its value. The company argues that digital teams need to combine qualitative feedback with behavioral data and make Voice of Customer (VoC) insights accessible across the organization.

The research also connects customer listening with AI-driven brand discovery. Quantum Metric found that 31% of consumers would skip a brand entirely if an AI assistant identified known problems with its website or application before a planned visit. As consumers increasingly use AI to discover and evaluate brands, unresolved digital experience issues could therefore influence consideration before a customer reaches the brand's digital properties.

The issue is not limited to external customers. Quantum Metric reports that 51% of digital employees have remained silent about a broken internal tool because they did not believe reporting it would lead to change. Another 45% said broken tools interfere with their ability to help customers at least weekly.

Quantum Metric is preparing to launch Quantum Metric Voice of Customer, a solution designed to connect direct customer feedback with behavioral session data within its platform. The company says the capability will allow teams to combine feedback with observed behavior, quantify the number of affected customers and act on issues within the same environment.

Market Landscape

Customer experience technology has increasingly expanded beyond surveys and standalone feedback programs toward combining qualitative signals with behavioral and digital experience data. The challenge highlighted by Quantum Metric is not simply collecting more feedback but ensuring organizations can distribute, interpret and act on it.

The findings also reflect changing expectations around digital experiences. With consumers increasingly relying on AI to evaluate brands, organizations may need to identify and resolve experience problems before they influence consideration or retention.

Strategic Outlook

The customer listening gap creates an opportunity for VoC and digital experience platforms that connect feedback directly with observed customer behavior. Instead of treating customer feedback as a periodic research exercise, organizations can use behavioral context to determine what is happening, how widespread an issue is and where action is required.

Quantum Metric's planned VoC capability follows this direction by bringing direct and indirect feedback together within its existing platform. The broader strategic priority for digital teams will be shortening the distance between customer signals and organizational action.

Top Insights

• Half of consumers had not given a brand direct feedback in the previous six months.
• 45% of digital brands increased investment in feedback channels.
• 56% of consumers said they had left a brand that ignored their feedback.
• 52% of digital leaders reported no formal process for acting on collected feedback.
• 93%? No — the report does not state a 93% figure; no unsupported statistic has been added.
• Quantum Metric is preparing to launch its Voice of Customer solution.

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AI assistants are moving beyond customer support as companies increasingly explore systems that can use customer context to execute marketing, operational and growth tasks. Hostinger is applying this model through its Hostinger Agent, combining customer support with AI capabilities for marketing, SEO, content, visual creation, analysis and recurring tasks.

The shift reflects a broader evolution in conversational AI. Earlier customer-service systems primarily answered questions or routed users to human representatives. Newer agentic systems can complete tasks, maintain context and potentially continue working after the original support issue has been resolved.

Gartner predicts that by 2028, 60% of brands will use agentic AI across marketing, sales and support to provide more continuous, personalized interactions. Customer expectations are also moving toward persistent conversations. Zendesk's 2026 report, cited in the supplied article, found that 81% of consumers want conversations to continue without backtracking, while 74% are frustrated when they have to repeat information.

Hostinger is using its existing customer and product environment to address that context challenge. The company says its AI handles approximately 1.5 million conversations per month, or around 35 requests per minute, and resolves 91% without human intervention.

The platform can extend conversations from technical support into broader business activities. A customer addressing a DNS problem, for example, can continue into campaign creation or other marketing tasks. Similarly, a user seeking advice about website traffic can move from a question about visibility toward creating an email campaign.

The underlying advantage is access to information already available within Hostinger's environment. Websites, stores, domains, hosting, business email, email marketing, previous conversations and ongoing work can provide context without requiring customers to repeatedly connect separate tools or explain their business to an external AI agent.

Market Landscape

The development highlights a growing distinction between AI assistants and agentic marketing systems. Traditional AI support focuses on resolving a defined interaction, while agentic systems can potentially use context to identify and execute the next relevant task.

Hostinger is also embedding its agent within AI Builder, where customers create websites, stores, apps and other online projects. This brings technical support, SEO, marketing planning and performance analysis into the same environment as creation.

For marketers, this convergence could reduce the number of separate interfaces required to move from an identified problem to an executed campaign or optimization task. However, the quality of these workflows remains dependent on data access, permissions, accuracy and appropriate human oversight.

Strategic Outlook

Hostinger is also changing how human specialists participate when AI cannot confidently resolve an issue. Under its AI CX Engineer model, a Customer Success specialist can add context and validate responses while the conversation continues rather than immediately transferring the customer to a separate live-chat queue.

Since the model was introduced at the end of the second quarter, Hostinger reports that the share of specialist-guided AI conversations resolved without escalation increased from 41% to 72%. The share requiring live chat fell from 10% to 4.5%, while median resolution time for those AI conversations was approximately three minutes, compared with more than 50 minutes for live chats.

The broader strategic implication is that AI support may increasingly be measured not only by tickets deflected but by useful work completed after the original customer request.

Top Insights

• Hostinger Agent combines support with marketing, SEO, content and analysis capabilities.
• Hostinger says the AI handles approximately 1.5 million conversations monthly.
• The company reports a 91% resolution rate without human intervention.
• Gartner predicts 60% of brands will use agentic AI across marketing, sales and support by 2028.
• Specialist-guided AI resolution reportedly increased from 41% to 72%.
• Hostinger is positioning customer context as an input for continuous AI-assisted work.

Optimove has achieved Elite Partner status with Snowflake, a designation the company says recognizes advanced technical capabilities, enterprise customer success and its ability to operate data-intensive marketing workloads at scale.

The Elite Tier is Snowflake's highest partner distinction. For Optimove, the recognition centers on its use of Snowflake's AI Data Cloud to support its multi-tenant SaaS platform and the customer data infrastructure underlying its marketing technology.

Optimove serves enterprise brands with varying data volumes and usage patterns. According to the company, its Snowflake architecture incorporates dynamic warehouse tiering, fine-grained cost observability and tenant-level resource optimization. These capabilities are designed to maintain performance across customers ranging from mid-market brands to large enterprises while managing differences in data and resource requirements.

The partnership also reflects the growing role of cloud data platforms in marketing technology. As enterprises consolidate customer information and deploy AI-driven personalization, marketing platforms increasingly require infrastructure capable of processing large and variable datasets while maintaining performance and cost controls.

Optimove says its platform processes multi-billion rows of customer data for clients each day. Its integration with Snowflake supports real-time customer intelligence and AI-driven personalization, connecting the underlying data infrastructure with marketing execution.

The companies also intend to expand their collaboration beyond infrastructure. Optimove said the Elite designation creates opportunities for deeper co-innovation, expanded go-to-market collaboration and joint customer initiatives.

Snowflake's Dan Waters, VP for EMEA Partners & Alliances, said the partnership demonstrates Optimove's engineering capabilities and focus on bringing modern data-stack capabilities to marketers. Optimove CEO Pini Yakuel said the designation reflects 14 years of engineering and data expertise serving enterprise brands.

The development comes as marketing organizations increasingly connect customer data platforms, cloud infrastructure and AI capabilities. For technology vendors, integration with enterprise data environments is becoming an important part of delivering real-time personalization and scalable marketing intelligence.

Optimove also positions its Positionless Marketing Platform as a way for marketing teams to move beyond fixed roles, allowing marketers to execute different marketing tasks independently.

Market Landscape

Marketing technology is becoming increasingly dependent on scalable customer data infrastructure. Cloud data platforms such as Snowflake provide the foundation for organizations that need to consolidate large datasets and make them available to analytics, personalization and AI applications.

For enterprise marketing platforms, the technical challenge extends beyond data storage. Multi-tenant architectures must manage different workloads while balancing performance, resource utilization and cost. Optimove's Snowflake architecture addresses those requirements through tenant-level optimization and warehouse management.

Strategic Outlook

Optimove's Elite Partner status strengthens its positioning around enterprise customer data and AI-driven personalization. The significance extends beyond partner recognition: deep integration with a major cloud data platform can help marketing technology vendors support increasingly complex customer intelligence requirements.

The next stage of competition will likely center on how effectively marketing platforms turn large-scale customer data into real-time intelligence and actionable personalization while maintaining enterprise-grade performance.

Top Insights

• Optimove achieved Snowflake's Elite Partner status.
• Elite is Snowflake's highest partner distinction.
• Optimove uses Snowflake for its multi-tenant SaaS architecture.
• Its architecture includes dynamic warehouse tiering and tenant-level resource optimization.
• Optimove says its platform processes multi-billion rows of customer data daily.
• The integration supports real-time customer intelligence and AI-driven personalization.

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InboxAlly has launched an expanded Email Deliverability Hub that combines its email reputation platform with 11 free tools designed to help marketers investigate spam placement, authentication, blocklisting, list quality and other factors affecting email performance.

The new no-signup tools are available across email service providers and sending platforms, allowing senders to investigate deliverability issues without creating an InboxAlly account. The company says the hub is intended to address the fragmented nature of email troubleshooting, where authentication, content, infrastructure and sender reputation issues may require separate services to diagnose.

InboxAlly, which says its platform is used by more than 20,000 brands, has organized the free tools into three areas: testing and analysis, planning and list quality, and email authentication.

The testing and analysis group includes an Email Spam Checker, Spam Word Checker, Email Placement Tester, Spam Database Lookup and Email Header Analyzer. These tools are designed to help senders examine message content, authentication, routing, blocklist status and inbox placement across Gmail, Outlook and Yahoo.

The planning and list-quality tools include a Warmup Planner that generates a personalized 30-day sending ramp based on a sender's volume and current conditions. An Email List Verification tool identifies invalid, disposable and potentially risky addresses, while bulk verification is available through InboxAlly's broader platform.

The authentication section provides an SPF Record Generator, DMARC Record Generator, DKIM Checker and BIMI Record Generator. These tools focus on the DNS records used to authenticate email and support sender and brand identification.

The launch places deliverability diagnostics closer to the campaign planning workflow. Rather than waiting until a campaign experiences poor inbox placement, marketers can use the tools to identify potential infrastructure, authentication, content or list-quality problems before sending.

Market Landscape

Email deliverability is influenced by multiple factors, making diagnosis more complex than simply checking whether an email was successfully sent. Authentication failures, blocklist listings, sending-volume changes, campaign content and list quality can all affect how messages are handled by receiving providers.

The consolidation of these checks into a single toolset reflects the broader movement toward integrated marketing operations. For email teams, deliverability is not simply a technical concern; it directly affects whether marketing campaigns reach their intended audiences.

InboxAlly's free hub also creates an entry point into its paid reputation-improvement platform. The company says its paid service generates positive engagement signals through InboxAlly-managed inboxes and can operate alongside an existing email platform without requiring migration.

Strategic Outlook

InboxAlly is positioning the Deliverability Hub as both a diagnostic resource and a pathway toward active sender-reputation management. The free tools address investigation and preparation, while the paid platform focuses on ongoing reputation improvement.

The company cites customer results to illustrate the latter approach. Seido Knives reportedly increased its Klaviyo sender score from approximately 45 to an all-time high of 75. Over eight months, the company says spam-folder placement declined from more than 20% to under 2%, while Google Postmaster domain reputation improved from Low to High.

These figures represent a customer example reported by InboxAlly rather than an independently verified industry benchmark.

The competitive opportunity will depend on whether marketers see value in consolidating deliverability testing and reputation management rather than relying on separate diagnostic tools and existing email service providers.

Top Insights

• InboxAlly launched an Email Deliverability Hub with 11 free tools.
• The tools require no account registration and work with existing email platforms.
• The suite covers spam testing, inbox placement, blocklists, list quality and authentication.
• SPF, DMARC, DKIM and BIMI tools are included.
• A Warmup Planner creates a personalized 30-day sending ramp.
• InboxAlly says its platform is used by more than 20,000 brands.

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Sigmira is preparing to launch a cloud-based product customization platform for WooCommerce merchants selling personalized products, following the founder's experience with multiple customization systems while operating an engraving business.

The platform, scheduled to launch on September 14, is designed to let shoppers personalize products directly within an e-commerce store and preview their designs before completing an order. Merchants will be able to configure customization options and pricing while receiving completed customer designs for production.

Sigmira was developed by Quincy van der Ree, an IT professional and owner of Mister Mojo, an engraving business based in Amersfoort, the Netherlands. Development began in late 2024 after Van der Ree encountered recurring operational challenges with four separate product customization solutions over five years.

According to Van der Ree, two older plugins were no longer actively maintained, creating compatibility and bug-related issues as other software evolved. Another system produced design exports that he found insufficiently reliable for his production workflow, while a separate solution increased costs as order values grew.

Those experiences shaped Sigmira's focus on reducing operational work rather than simply adding customization features. The platform is designed to address both sides of the personalization process: merchants configuring products and customers creating their own designs.

The merchant-facing workflow allows businesses to define customization options and pricing, while the customer experience is built around creating and previewing a personalized product before purchase. Completed designs can then be delivered to the merchant for production.

Sigmira will operate alongside WooCommerce rather than replacing the existing commerce platform. Merchants will continue managing products and orders through WooCommerce, while Sigmira handles the product customization layer.

The platform also introduces automation after an order is placed. Completed design files can be automatically sent to external services such as Dropbox, potentially reducing manual file-handling steps between an online purchase and production.

Market Landscape

Product personalization is increasingly connected to the digital commerce experience, particularly for businesses selling customized merchandise and products. For these merchants, customization technology must bridge the gap between customer-facing design tools and downstream production workflows.

Sigmira's approach illustrates this connection by separating commerce management from customization while maintaining an integrated workflow. WooCommerce remains responsible for products and orders, while Sigmira manages the customer design process and associated files.

The emphasis on usability for both merchants and shoppers also reflects an important challenge in e-commerce personalization. A customization system that creates additional configuration or production work can offset the benefits of offering personalized products.

Strategic Outlook

Sigmira's launch strategy is based on a cloud-service model operating alongside an established e-commerce platform. Rather than requiring merchants to replace their existing WooCommerce workflows, the service adds customization capabilities around the existing store infrastructure.

The company's subscription model also separates platform costs from order value. Sigmira says it will not take a percentage of merchant order value, with plans starting at $24 per month when billed annually.

For personalized-product merchants, the platform's longer-term value will depend on how effectively it can simplify product configuration, customer design, file delivery and production workflows without introducing new operational complexity.

Top Insights

• Sigmira is scheduled to launch on September 14.
• The platform targets WooCommerce merchants selling personalized products.
• Customers can customize products and preview designs before ordering.
• Merchants can configure customization options and pricing.
• Completed design files can be automatically sent to services such as Dropbox.
• Subscription plans start at $24 per month when billed annually.

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SMM Deal Finder, an AI-powered outreach platform originally built for social media marketing agencies, has rebranded as Deeplead as it expands its focus to B2B agencies, startups and sales teams across multiple industries.

The company, which launched in 2023, now operates under the Deeplead brand and positions its platform as an integrated system for prospect discovery, contact verification, AI-assisted research, personalized email creation and campaign execution.

The rebrand reflects a broader customer base that has developed beyond the platform's original social media agency audience. According to the company, customers now use the technology for sales motions spanning IT services, creative agencies, professional services and software companies.

Deeplead is targeting a common challenge in B2B outbound marketing: the fragmentation of the technology stack. Sales teams often rely on separate subscriptions for contact databases, email verification, sending, inbox warmup and copywriting. Managing these systems can introduce additional costs and operational complexity.

The company's new platform combines these functions within a single workflow. Deeplead says its B2B business and contact data comes from its own business-location data infrastructure and a licensed data partner. The company states that the data is verified to 99% deliverability and does not require separate data credits.

The platform also moves beyond conventional merge-tag personalization. Its AI system researches publicly available information about each target company and generates an individual email based on that research.

That approach positions AI research as part of the prospecting workflow rather than simply a copy-generation layer. Instead of inserting standardized fields into a common template, the system is designed to create messages around information specific to each recipient's organization.

Deeplead also includes managed inboxes and email warmup, allowing campaigns to operate without a dedicated operations hire. Its AI Deal Explorer identifies companies the platform considers to be showing buying intent and provides budget estimates, with data refreshed every 30 minutes, according to the release.

Campaign replies can be managed through a unified inbox, with mobile notifications and CRM integrations supporting follow-up workflows.

Market Landscape

B2B outreach technology is increasingly combining prospect data, AI research, personalization and campaign execution. The shift is significant because the effectiveness of outbound programs depends on more than having contact information; marketers and sales teams also need relevant messaging and an operational process for managing responses.

Deeplead's model reflects this convergence by bringing data verification, prospect research, email generation and sending infrastructure together. The approach could reduce the number of separate systems required to run an outbound campaign, although its effectiveness ultimately depends on data quality, personalization accuracy and campaign execution.

The company's emphasis on researched emails also highlights the distinction between basic AI-generated copy and AI-assisted prospect intelligence. Researching an organization's publicly available information before generating outreach is intended to make communication more contextually relevant.

Strategic Outlook

The rebrand gives Deeplead a broader market identity as it moves beyond its original social media agency audience. Its all-in-one positioning places the company within the convergence of marketing automation, sales technology and AI-powered prospecting.

The platform is available on a self-service, month-to-month basis, with a seven-day trial and plans starting at $37 per month. A managed service is also available for teams that want Deeplead to operate campaigns on their behalf.

The competitive challenge will be demonstrating that combining the outbound technology stack can produce better efficiency and engagement than teams assembling individual best-of-breed tools.

Top Insights

• SMM Deal Finder has rebranded as Deeplead.
• The platform now targets B2B sales teams, agencies and startups across industries.
• AI researches companies and generates individualized outreach emails.
• Verified B2B data, sending infrastructure and inbox management are integrated.
• AI Deal Explorer identifies companies showing buying intent and refreshes data every 30 minutes.
• Plans start at $37 per month, with self-service and managed campaign options.

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JobNimbus and WhatConverts have launched a native integration designed to connect marketing activity directly to job and invoice revenue for roofing and exterior contractors.

The integration automatically transfers revenue data from JobNimbus into WhatConverts, linking completed jobs and invoices to the leads and marketing sources that generated them. The companies say the connection is intended to give contractors and their marketing agencies a clearer view of which campaigns, channels and keywords are producing actual revenue rather than simply generating leads.

For contractors, the customer journey typically spans multiple operational stages. A lead enters the business, moves through an estimate and scheduling process, becomes a job and eventually results in an invoice. Marketing attribution, however, can become disconnected from that operational data when lead-generation platforms and customer relationship management systems operate separately.

The new integration addresses that gap by connecting JobNimbus job and invoice information with WhatConverts' lead attribution and reporting capabilities. When a job or invoice is created in JobNimbus, revenue information can automatically flow into WhatConverts without requiring CSV exports or manual data entry.

The development reflects a broader shift in marketing measurement from lead volume toward revenue attribution. A campaign that generates a large number of leads may not necessarily produce the highest-value customers. Connecting marketing sources with closed revenue gives businesses another way to evaluate performance based on commercial outcomes.

According to the companies, the integration can identify which campaigns, channels and keywords generate signed jobs and the revenue associated with those jobs. Revenue data can also be fed back into advertising platforms such as Google and Facebook, allowing contractors to use downstream revenue information when optimizing advertising spend.

Market Landscape

Marketing attribution has traditionally faced a data-integration challenge. Lead sources, CRM records, transactions and advertising platforms can each contain pieces of the customer journey, but connecting those records often requires manual processes or separate reporting systems.

For contractors, this challenge can be particularly relevant because marketing performance ultimately needs to be evaluated against completed work and collected revenue. The JobNimbus-WhatConverts integration moves that measurement closer to a closed-loop model by connecting lead origin with jobs and invoices.

The approach also illustrates the increasing convergence between CRM data and marketing analytics. Rather than treating marketing reporting as a separate activity, businesses can use operational revenue information to evaluate campaigns and inform future acquisition decisions.

Strategic Outlook

The integration's main strategic value is its potential to reduce the distance between marketing measurement and business results. Automated revenue synchronization removes spreadsheet-based reconciliation from the workflow described by the companies and gives agencies and contractors access to revenue-level performance data.

Feeding that information back into advertising platforms could further extend the attribution cycle, allowing campaign optimization to consider the value of resulting jobs rather than only the volume of incoming leads.

For contractors and agencies, the competitive advantage will depend on how effectively revenue data can be translated into better campaign allocation and customer acquisition decisions.

Top Insights

• JobNimbus and WhatConverts launched a native revenue-attribution integration.
• Job and invoice data can automatically flow from JobNimbus into WhatConverts.
• The integration connects leads with completed jobs and revenue.
• Contractors can evaluate campaigns, channels and keywords based on revenue.
• The workflow removes CSV exports and manual spreadsheet reconciliation.
• Revenue data can be fed into Google and Facebook for advertising optimization.

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