Gradial, an agentic marketing operations platform for enterprises, has been named to the 2026 Intelligent Applications 40 (IA40), placing the company among private AI businesses recognized for making artificial intelligence central to their software rather than adding it as a supporting feature.

The sixth annual IA40, organized by Madrona in partnership with PitchBook, recognizes 40 private companies developing intelligent applications. For the 2026 selection, 72 venture investors from 54 venture and corporate firms nominated and voted across 333 companies. According to Madrona, the winning companies collectively have raised $410.7 billion since their founding.

For Gradial, the recognition comes as enterprise marketing organizations increasingly explore agentic systems capable of executing operational work rather than simply generating recommendations or content.

Market Landscape

Enterprise marketing teams typically coordinate work across content management systems, email platforms, paid media, digital asset management, search and localization tools. The resulting workflows can involve tickets, approvals, handoffs and external agency support.

Gradial is targeting this operational layer with AI agents designed to execute marketing processes from request through publication. The company says its platform can handle content updates, localization, campaign operations, digital asset management and quality assurance within the systems already used by global marketing teams.

Its agents are also designed to orchestrate activities across web, email, paid media, assets and search. This places Gradial within a rapidly developing segment of marketing technology focused on agentic AI and autonomous workflow execution.

The IA40 selection process indicates the broader investment community is also placing greater emphasis on intelligent applications that apply AI to specific business workflows.

Strategic Outlook

The strategic significance of Gradial's recognition extends beyond the award itself. Agentic marketing technology is increasingly shifting the conversation from AI-assisted productivity toward software that can complete operational processes.

For large marketing organizations, the potential value is reducing the coordination burden associated with high-volume content and campaign operations. However, enterprise deployment also requires governance, integration with existing systems, quality assurance and clear accountability for AI-generated actions.

Gradial says its agents operate inside the technology environments global marketing teams already use. That integration-first approach could be important as enterprises seek to introduce AI without replacing established marketing infrastructure.

The IA40 recognition follows significant funding momentum for Gradial. The company announced a $65 million Series C led by Insight Partners, with participation from Madrona, VMG and PruVen Capital, bringing its total funding to more than $110 million.

For the broader MarTech market, the development highlights a growing competitive category: AI systems designed not merely to assist marketers, but to execute portions of the marketing operating model.

Top Insights

• Gradial was selected for the 2026 IA40 mid-stage category.
• The IA40 evaluated 333 companies through nominations and voting from 72 investors.
• Gradial focuses on agentic execution across enterprise marketing operations.
• Its platform targets content, localization, campaigns, assets, search and quality assurance.
• The company says it serves Global 2000 marketing organizations.
• Gradial has raised more than $110 million following its $65 million Series C.

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Keller Williams Realty, LLC (KW) is collaborating with Rejig.AI to bring an AI-powered marketing assistant to KW-affiliated real estate agents, extending the company's broader strategy of opening its technology ecosystem to third-party integrations.

The collaboration connects Rejig.AI's marketing capabilities with KW's Command® platform strategy, which the real estate franchise expanded earlier this year with a more integration-ready approach. The objective is to help agents automate routine marketing activities while retaining human control over content before publication.

For real estate professionals, maintaining a consistent digital presence can require substantial effort across social media, listings, market updates and local content. Rejig.AI is designed to automate portions of that workflow by generating agent-branded posts, videos and market reports and responding to listing activity.

Market Landscape

AI adoption in marketing is increasingly moving from content generation toward workflow automation. Instead of using generative AI only to create individual posts or copy, marketers are increasingly evaluating systems that can monitor events, recommend content and execute repetitive activities.

That model is particularly relevant to real estate, where marketing requirements change throughout a property's lifecycle. Rejig.AI says its integration can automatically generate marketing assets for stages including coming soon, just listed, open house, under contract and just sold.

The platform also monitors local news, events, trends and community information to generate hyperlocal content. Agents can produce market reports using housing data at county, city and neighborhood levels, while its 7-Day Planner recommends content based on an agent's activity and publishing history.

The approach reflects a wider shift toward AI-assisted personalization, where relevance is determined not only by audience characteristics but also by location, timing and current events.

Strategic Outlook

The partnership could strengthen KW's open-technology strategy by giving agents access to specialized marketing capabilities without requiring them to build individual AI workflows themselves.

An important element is the human approval layer. Agents can review and approve generated material before it is scheduled or published, preserving control over brand messaging while allowing AI to handle more of the preparation work.

Rejig.AI also positions social content as part of an agent's broader digital footprint. The company claims that one in five prospective buyers and current homeowners now use AI for homebuying research. That figure is a Rejig.AI claim rather than independently verified research in the announcement, but it highlights an emerging marketing consideration: brands may need to create consistent, locally relevant digital information for both human audiences and AI-assisted discovery.

The competitive opportunity for platforms serving real estate professionals is therefore shifting beyond CRM and listing management toward integrated marketing automation, personalization and AI visibility.

Top Insights

• KW is expanding its technology ecosystem through a collaboration with Rejig.AI.
• AI can automate recurring real estate marketing activities tied to listing milestones.
• Hyperlocal content generation is a central component of the offering.
• Human approval remains part of the publishing workflow.
• The partnership connects AI content creation with social media, market intelligence and agent branding.

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F&F Ventures, the venture capital arm of the Founders & Funders community, has launched a $10 million ecommerce-focused fund in partnership with SHOPLINE, targeting early-stage consumer brands and the technology supporting their growth.

The fund will invest from pre-seed through Series A and combines venture capital with access to SHOPLINE's commerce infrastructure. SHOPLINE says its platform serves more than 700,000 merchants globally, offering tools spanning storefronts, payments, marketing automation, loyalty and multichannel commerce.

The structure reflects a broader trend in ecommerce financing: early-stage consumer companies increasingly need access to technology and operational capabilities alongside capital. Rather than treating commerce infrastructure as a later-stage technology investment, the partnership intends to make it part of the portfolio companies' operating foundation from the beginning.

Market Landscape

The early-stage consumer market remains highly dependent on efficient customer acquisition, digital storefront performance and the ability to manage multiple commerce channels. For emerging brands, fragmented technology stacks can create additional costs as companies expand from direct-to-consumer operations into broader distribution.

F&F Ventures says its deal flow comes through the Founders & Funders community, which has held more than 300 events in the United States and internationally over eight years. Its VC ReversePitch series allows investors to present their investment thesis and check sizes directly to founders.

The fund's current portfolio includes 13 consumer brands, including Protein Waffles, NoLimit, See Sea, Link Luggage, OnGo Energy, Board Budder and G Box.

Strategic Outlook

The partnership illustrates how commerce platforms are increasingly becoming part of the startup ecosystem rather than functioning solely as software vendors.

For SHOPLINE, providing infrastructure alongside an investment relationship potentially creates deeper engagement with emerging brands at an early stage. For F&F Ventures, the arrangement gives portfolio companies access to a commerce platform while they are still establishing their customer acquisition, retention and multichannel strategies.

The model also places greater emphasis on the connection between funding and execution. F&F Ventures' three general partners bring experience across consumer products, ecommerce, supply chains and private equity, while SHOPLINE contributes commerce technology.

However, the long-term value of the model will depend on whether portfolio companies can translate integrated infrastructure and operating support into sustainable customer growth. Early-stage brands still face the fundamental challenges of product-market fit, acquisition costs, retention and differentiation.

Top Insights

• F&F Ventures has launched a $10 million fund focused on early-stage ecommerce.
• The fund targets consumer brands and commerce technology from pre-seed through Series A.
• SHOPLINE will provide portfolio companies with commerce infrastructure alongside investment.
• The partnership combines venture funding with ecommerce and operating expertise.
• Integrated storefront, payments, automation and loyalty capabilities could reduce technology fragmentation for young brands.

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Optimizely is moving its marketing AI strategy toward persistent, role-based digital coworkers with the launch of Virtual Teammates, a capability designed to perform recurring marketing work rather than simply respond to individual prompts.

Announced August 31 at Opticon, the offering extends Optimizely's AI platform with digital teammates that can retain organizational context, operate under defined permissions and take action across workflows. The company says the objective is to address a growing problem in enterprise marketing: AI adoption is increasing while the number of tools marketers must coordinate is also expanding.

Optimizely's own survey of 2,003 B2B marketing leaders found that 81% work with two or more disconnected AI tools each week, while only 19.2% reported using a single integrated AI platform. The study was conducted by Savanta across seven markets.

Market Landscape

The launch comes as marketing technology vendors increasingly shift from generative AI assistants toward agentic AI, where software can execute multi-step tasks with less direct human intervention.

Optimizely's differentiation is its emphasis on persistent roles and identity rather than standalone task execution. Its Virtual Teammates can be assigned recurring responsibilities, connected to required systems and configured through natural-language instructions. The company says each teammate operates with its own identity and permissions, allowing organizations to establish oversight and trace actions.

The initial roster includes a Chief of Staff, SEO & AI Search Analyst, Marketing Analyst, Personalization Strategist and CRO Manager. These roles span planning, analytics, search visibility, personalization and experimentation, positioning the technology across multiple marketing functions rather than as a single-purpose AI assistant.

Strategic Outlook

The bigger implication is a potential change in how enterprises organize marketing technology. Instead of adding another interface for employees to operate, AI agents can increasingly become operational participants inside existing workflows.

That model could help address AI fragmentation, but it also raises questions around governance, permissions, quality control and accountability. Optimizely's use of individual identities, scoped access and auditability indicates that these controls are becoming part of the product architecture rather than an afterthought.

The timing is significant. Optimizely's research found that 76% of marketers spend at least three hours a week editing, fact-checking or correcting AI-generated output, suggesting that automation alone does not necessarily eliminate operational work.

For marketing leaders, the competitive question will therefore be whether persistent AI coworkers can deliver measurable business outcomes while reducing—not merely relocating—the workload associated with managing AI.

Top Insights

• Optimizely is shifting AI from prompt-based assistance toward persistent marketing roles.
• Virtual Teammates retain context and can perform recurring work on schedules or triggers.
• Identity-based permissions provide an accountability layer for autonomous AI activity.
• The launch targets fragmentation across increasingly complex marketing AI stacks.
• AI-agent adoption is moving closer to execution across SEO, analytics, personalization and CRO.

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A Boynton Beach, Florida-based marketing agency is positioning earned media as an increasingly important customer-acquisition channel, arguing that independent editorial coverage can provide a form of credibility that paid advertising cannot easily replicate.

Good At Marketing, a Google Partner agency, says it has developed proprietary software designed to improve the odds of securing earned-media placements for its clients. The company has not disclosed how the technology works, saying the system is exclusive to its client base.

The strategy reflects a broader shift in how marketers evaluate visibility. Paid advertising provides predictable access to audiences, but earned coverage can create third-party validation that brands cannot directly purchase. Nielsen's global advertising research has repeatedly found strong consumer trust in recommendations and other forms of earned influence. Its 2021 study reported that 88% of respondents trusted recommendations from people they knew, while earlier Nielsen research found earned recommendations significantly ahead of many paid advertising formats.

Market Landscape

The distinction between paid and earned visibility is becoming more relevant as consumers use search engines, online publications and AI-powered assistants during buying research. Academic research published in Marketing Science describes large language model referrals as an emerging digital channel, although it notes that the channel remains relatively small and concentrated in informational use cases.

That creates an additional incentive for brands to build credible third-party references around their businesses. However, editorial coverage is fundamentally different from advertising: marketers can purchase placement in paid media but cannot guarantee that an independent journalist will consider a company sufficiently relevant to include in a story.

Good At Marketing says its software is designed around that challenge. According to the agency, clients can receive opportunities to contribute expertise to media stories, while the agency manages the underlying process. The company says its clients include law firms, home-service businesses and ecommerce brands, with placements extending into national business, marketing and trade publications.

Strategic Outlook

The agency's approach represents a convergence of public relations, content strategy and customer acquisition rather than a replacement for conventional advertising.

Its more significant proposition is the attempt to make earned-media activity measurable and repeatable through software. If the system can consistently identify relevant opportunities and increase successful placements, it could address one of the persistent limitations of traditional PR: significant effort with uncertain outcomes.

There is also a competitive question. Good At Marketing says the technology is proprietary and that the service is deliberately limited to avoid representing competing businesses in the same market. Those claims remain company assertions because the underlying technology and performance data have not been publicly disclosed.

For marketers, the strategic takeaway is less about abandoning advertising and more about combining paid reach with independent credibility. Earned coverage can strengthen brand authority, while advertising continues to provide control over audience, timing and message.

Top Insights

• Earned media offers third-party credibility that paid advertising cannot directly purchase.
• Proprietary technology could make media outreach more systematic, if performance claims are validated.
• AI-assisted shopping and discovery are creating new reasons for brands to build authoritative digital footprints.
• Customer acquisition strategies are increasingly combining paid, owned and earned channels.
• Exclusive agency models may reduce conflicts when multiple competitors target the same market.

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Global Communication Works (GCW) has launched SignlLabs™, a proprietary AI Signaling practice designed to help brands earn visibility and recommendations across artificial intelligence platforms. The initiative extends the public relations agency's earned-media approach into AI-driven discovery and recommendation environments.

GCW positions SignlLabs around the changing way consumers find information. Rather than relying exclusively on conventional search engines, users increasingly ask AI platforms for recommendations and direct answers. The agency argues that this creates a distinct visibility challenge for brands because AI-generated responses can draw on information from multiple sources rather than simply ranking a company's website.

According to GCW, SignlLabs combines earned-media strategy with an AI recommendation framework focused on five platforms: ChatGPT, Google Gemini, Claude, Microsoft Copilot and Perplexity AI.

From SEO Rankings to AI Recommendations

GCW differentiates its approach from traditional search engine optimization and Answer Engine Optimization (AEO). The agency describes SEO as focused on rankings, while AEO focuses on content being surfaced as an answer. SignlLabs instead targets the recommendation layer, seeking to influence which brands AI platforms identify when users ask which product, service or company is best.

The agency says AI systems can break a single user prompt into multiple research queries before composing an answer. GCW argues that third-party endorsements and earned media can therefore play an important role in the information AI systems encounter when evaluating brands.

That distinction reflects a developing area of marketing strategy as generative AI becomes another discovery interface. For marketers, the challenge is shifting from optimizing only for pages and snippets toward understanding how brand reputation and external sources can influence AI-generated recommendations.

Market Landscape

AI-powered discovery is creating a new layer in the digital visibility ecosystem alongside search, social media and traditional media. Brands now have to consider how their information is represented not only on their own websites but also across external sources that AI platforms may use when generating responses.

GCW's approach is notable because it connects public relations and earned media with AI visibility rather than treating AI discovery exclusively as an SEO problem.

The agency says SignlLabs measures conventional campaign metrics such as impressions, reach and engagement alongside AI recommendation lift. This creates a combined measurement model intended to show how campaigns perform across both traditional media channels and AI-based discovery.

Strategic Outlook

SignlLabs is already being used within GCW campaigns across its women's health, consumer health and lifestyle client roster. The practice will also become part of the agency's future new-business efforts.

The launch suggests that PR agencies may increasingly expand into AI visibility as brands seek ways to influence how they are represented in conversational search and recommendation environments.

However, AI recommendations can vary by platform, query and context. As this marketing discipline develops, measurement methodology, source quality and the ability to distinguish durable visibility from temporary model responses will remain important considerations for marketers.

Top Insights

• GCW has created SignlLabs to address brand visibility across AI platforms.
• The practice combines earned-media strategy with an AI recommendation framework.
• GCW is measuring activity across ChatGPT, Google Gemini, Claude, Microsoft Copilot and Perplexity AI.
• The agency distinguishes AI Signaling from traditional SEO and AEO.
• SignlLabs is already being incorporated into selected GCW client campaigns.

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Hikino AI has announced RAV for iOS, extending its Revenue Control Plane to customer-facing sales workflows through verbally processed, agentic AI orders. The company says the capability allows sales representatives to describe customer orders naturally from an iPhone, with governed AI agents handling configuration, pricing validation and order submission.

The company highlighted an initial production order placed by Amza Safi, Sales Director West at Clutch Solutions. According to Hikino AI, Safi described the order verbally from his iPhone, after which RAV validated the configuration and pricing within a single deal thread before submitting the order.

Hikino AI says the order moved from spoken request to submission in less than 60 seconds, compared with approximately 30 minutes for the same process using multiple portals, spreadsheets, internal teams and a CRM. The company also states that the transaction required zero keyboard input.

From Voice Interfaces to Autonomous Sales Workflows

Hikino AI's positioning differs from conventional voice assistants and chatbot interfaces by focusing on completing an operational sales process. RAV's governed agents operate within the company's Revenue Control Plane, which Hikino AI says manages the VAR business from quote through cash.

The company describes the system around a governance model in which users approve AI actions and can undo them. According to Hikino AI, the same deal thread provides the context for the spoken order, approvals and audit trail.

That architecture points to a broader shift in enterprise AI from conversational interfaces toward systems capable of executing business processes. In this model, voice becomes an interface for initiating work rather than the end product itself.

Market Landscape

Sales organizations across complex B2B environments often work across CRM platforms, quoting systems, spreadsheets, partner portals and internal approval processes. Moving information between those systems can introduce operational friction and require sales representatives to spend time on administrative tasks rather than customer interactions.

Hikino AI is positioning RAV against that fragmented workflow by keeping order activity within a governed platform. Its argument is that adding AI to existing processes does not necessarily remove the underlying operational complexity if employees still have to re-enter information across systems.

The production order highlighted by the company provides a practical example of how voice-driven agentic AI could reduce manual interaction in sales operations. However, broader adoption will depend on factors including governance, accuracy, integration and the ability to safely authorize AI-driven actions.

Strategic Outlook

For B2B sales teams, the significance of RAV for iOS extends beyond voice input. The company's approach combines natural-language interaction with automated order processing, configuration and pricing validation.

Hikino AI says the capability is designed to help sales representatives remain focused on customers rather than keyboards and fragmented systems. If similar workflows can be reliably extended across the revenue cycle, agentic AI could increasingly become an execution layer for sales operations rather than simply an assistant.

The launch also highlights the importance of auditability and user control as AI agents move from generating recommendations to taking commercial actions.

Top Insights

• Hikino AI has launched RAV for iOS for verbally processed customer orders.
• The system uses governed AI agents to validate configuration and pricing before submission.
• Hikino AI reports an initial production order was completed in under 60 seconds.
• The company positions RAV as an alternative to fragmented, multi-system sales workflows.
• Voice is presented as an interface to the broader Revenue Control Plane rather than a standalone feature.

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Close O Matic is highlighting the final stage of its Six-Step Top-Producer Sales Psychology Framework, positioning autonomous AI sales agents as a way to automate the transition from a qualified sales conversation to transaction completion.

The company's Step Six, "Close the Sale," follows five earlier objectives: breaking a prospect's preoccupation, building rapport, establishing credibility, identifying the underlying problem and aligning a solution with that problem. Close O Matic says the framework is designed to address the primary reasons prospects do not buy before the closing stage.

According to the company, those barriers include affordability, lack of decision-making authority, insufficient trust in the salesperson or company, and a lack of understanding about the product or service. The first five steps are intended to identify and address those issues before a prospect reaches the closing stage.

AI Sales Conversations Move Beyond Qualification

Through Closora, described by Close O Matic as its AI Sales Closer, the company has incorporated this methodology into an autonomous sales conversation powered by Omni Rocket.

Rather than waiting for a prospect to explicitly announce an intention to purchase, Closora assumes the sale once the preceding objectives have been addressed and moves into the closing process. The approach reflects a broader development in conversational AI: moving from systems that answer questions or qualify leads toward agents designed to execute more of the sales workflow.

Close O Matic also describes an objection-handling process. The company characterizes an immediate first objection as a potential "smoke-screen objection" that may give a prospect time to consider the purchase. Closora is designed to address that response and attempt the close again.

If resistance continues, the system identifies what Close O Matic considers the underlying barrier, using information gathered during the conversation and its product or service knowledge to respond before attempting another close.

Market Landscape

AI sales technology is increasingly focused on automating activities that traditionally require human sales representatives, including prospect conversations, qualification, objection handling and transaction assistance. Close O Matic's approach places particular emphasis on the closing stage rather than stopping at lead qualification.

The model also illustrates an important distinction between conversational AI and autonomous sales agents. A conversational system may provide information or respond to questions, while an autonomous sales agent is designed to advance a defined business process and take actions toward an outcome.

For businesses evaluating such systems, the effectiveness of automated closing depends on the quality of qualification, objection handling, product knowledge and safeguards built into the sales workflow.

Strategic Outlook

Close O Matic's methodology creates a feedback loop between closing conversations and earlier stages of the sales presentation. The company says recurring genuine objections can be used to improve the earlier presentation so that the same concern is addressed before the close in future conversations.

Once a prospect is ready to transact, Closora can offer to send a payment link or agreement by text or email and guide the prospect through completion.

The resulting workflow can be summarized as: assume the sale, recognize the initial objection, close again, identify a genuine objection, resolve it, close again, and guide the prospect through the transaction.

Top Insights

• Close O Matic is applying its six-step sales framework to autonomous AI sales conversations.
• Closora is designed to continue toward a close rather than wait for an explicit buying statement.
• The system addresses objections using information gathered during the conversation.
• The approach extends AI sales automation into transaction assistance.
• Recurring objections can inform improvements to earlier stages of the sales process.

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