For two decades, online forms have largely followed the same formula: pick a template, drag fields into place, tweak logic, repeat. Now Jotform wants to replace that workflow with something closer to chatting with an assistant.

The company today unveiled Jotform AI, a conversational system designed to build and manage forms using natural language prompts. Instead of manually configuring fields, conditional logic, and integrations, users can simply describe what they want—by typing or speaking—and the platform generates the form automatically.

The launch marks a significant strategic shift for the company, which is repositioning its platform beyond form building toward a broader AI-driven productivity and data management layer.

“Jotform AI represents the next stage of our evolution,” said Aytekin Tank, founder and CEO of Jotform. “With this launch, Jotform shifts from a traditional form and productivity tool to an intelligent data management platform that executes at the request of a prompt.”

From Form Builder to AI Assistant

Traditional form builders—even modern ones—still rely heavily on graphical interfaces and manual configuration. Jotform AI flips that model by introducing a conversational interface that generates and modifies forms dynamically.

Users can prompt the system to:

• Generate complete forms from scratch
• Create or modify form fields
• Build conditional logic and workflows
• Structure layouts automatically
• Analyze submission data
• Suggest third-party integrations

The experience is powered by an AI assistant called AI Form Copilot, which works alongside the existing builder interface. Instead of digging through menus or configuration panels, users can instruct the system conversationally to refine questions, add logic, or restructure workflows.

The goal is straightforward: eliminate the learning curve that typically comes with form automation tools.

For many organizations—especially small teams or nontechnical users—that complexity has historically been a barrier. HR departments building onboarding forms, marketing teams collecting lead data, and educators managing submissions often rely on templates because customization can be time-consuming.

With Jotform AI, the company is betting that describing a form will prove far faster than building one.

Built for Busy Teams

While enterprise automation platforms often target developers or operations teams, Jotform’s pitch focuses on accessibility.

The company says Jotform AI is particularly useful for:

• Small and medium-sized businesses
• HR teams managing employee workflows
• Marketing teams handling lead capture
• Educators collecting student information

Another practical shift: the AI interface works across both desktop and mobile. That means users can generate or modify forms without navigating the full builder interface—a move that reflects how increasingly mobile many workplace workflows have become.

In theory, that could enable someone to create a lead capture form during a meeting or adjust a survey while traveling.

Riding the Generative AI Wave

Jotform’s announcement arrives amid a broader transformation in productivity software driven by generative AI. Platforms across the SaaS landscape—from document tools to CRM systems—are rapidly integrating conversational interfaces designed to reduce friction and automate repetitive work.

Behind the scenes, Jotform’s new AI capabilities are powered in part by technology from OpenAI, enabling multimodal inputs and conversational automation across the company’s ecosystem.

But Jotform’s AI push didn’t appear overnight. The company has spent the past year layering AI features into its platform, building the foundation for today’s launch.

A Year of AI Momentum

Before introducing Jotform AI, the company rolled out a series of AI-powered capabilities designed to automate customer interactions and workflows.

Among them were Jotform AI Agents, conversational agents that organizations can deploy across customer support channels, lead qualification flows, and internal operations.

The adoption numbers suggest significant traction:

• 750,000 AI Agents created by users
• 200 million agent sessions conducted
• 22 million messages sent through the system

Meanwhile, the earlier release of AI Form Copilot—now a core component of Jotform AI—has also seen strong usage. Since its debut, users have run more than 450,000 sessions, sending over 180,000 prompts to generate or refine forms.

According to Tank, those metrics helped validate the company’s belief that AI can dramatically accelerate everyday workflows.

“Generative AI has opened up incredible possibilities for software tools,” he said. “Our data shows users are building forms more efficiently and deploying autonomous customer service at high rates.”

In other words, the conversational interface isn’t just a flashy feature—it’s becoming the primary way users interact with the platform.

Expanding the AI Layer Across the Platform

Today’s launch focuses primarily on conversational form creation and editing. But the company says this is only the beginning.

Jotform plans to expand the AI layer across its broader product suite, including:

• Jotform Tables for structured data management
• Jotform Workflows for automation pipelines
• Jotform Sign for e-signature management
• Jotform Apps for building custom apps

The idea is to allow users to orchestrate entire workflows—data collection, processing, approvals, and automation—through conversational prompts.

If successful, that approach would effectively turn Jotform into an AI-powered operations layer rather than just a form tool.

Competing in the AI Productivity Race

Jotform’s pivot mirrors a broader shift across enterprise software vendors that are embedding AI directly into their user interfaces.

Many productivity platforms now offer AI-assisted generation, but they often stop at producing templates or drafts. Jotform’s approach goes further by enabling end-to-end form creation, automation, and integration through conversation.

That distinction could matter.

Forms remain a critical entry point for structured data across industries—from marketing lead capture to customer onboarding and employee workflows. Automating that process with AI could significantly reduce setup time and operational overhead.

For businesses increasingly adopting automation-first workflows, the appeal is obvious: fewer clicks, faster deployment, and less reliance on technical teams.

The Bigger Picture

The launch also arrives at a milestone moment for Jotform. The company recently celebrated its 20th anniversary, a rare longevity for a SaaS platform in the rapidly evolving productivity software space.

Two decades ago, web forms were mostly static tools used to collect basic information. Today, they power complex workflows tied to CRM systems, marketing automation, and customer support platforms.

By embedding AI directly into form creation, Jotform is positioning itself for the next evolution of that ecosystem—one where automation is driven less by configuration and more by conversation.

If the shift sticks, the drag-and-drop interface that once defined form builders may soon feel as dated as HTML tables.

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Diffblue today announced the general availability of its Diffblue Testing Agent, an autonomous regression test generator designed to work alongside AI coding platforms such as GitHub Copilot and Claude Code. The agent automatically produces verified unit tests across entire codebases without developer intervention, addressing a longstanding challenge in enterprise software engineering: generating comprehensive, trustworthy test coverage at scale.

Closing the Gap in AI-Assisted Development

AI coding agents have transformed how developers write software, but creating robust regression tests has remained labor-intensive. Developers often spend hours iterating with AI coding assistants, manually verifying output, and coaxing adequate coverage—frequently achieving suboptimal results.

Diffblue’s Testing Agent introduces an orchestration and verification layer that autonomously scopes codebases, generates tests at the method and class level, validates compilation, verifies test results, rolls back failed tests, and even prepares pull requests—all without manual intervention.

“Our benchmark data shows that the developer effort for driving even the best AI coding agents reaches unaffordable levels quickly,” said Dr. Peter Schrammel, co-founder and CTO of Diffblue. “The Diffblue Testing Agent achieves 80%+ coverage autonomously—the difference between an AI experiment and an AI-enabled engineering workforce.”

Benchmark Performance

In tests across eight real-world Java projects, Diffblue delivered:

• 81% average line coverage, compared to 32% achieved by an AI-savvy developer using only Claude Code
• 61% mutation coverage, surpassing typical enterprise standards
• 2.5x higher coverage than iterative human-assisted AI workflows

The benchmarks highlight the platform’s ability to scale efficiently across hundreds or thousands of classes in a single run, turning AI coding tools from experimental assistants into reliable engineering collaborators.

How It Works

The Diffblue Testing Agent integrates with existing AI coding platforms, delegating test generation to the underlying agent while orchestrating:

• Coverage analysis and build system configuration
• Test plan creation and parallelized generation
• Verification and rollback of failing tests
• Project clean-up and pull request preparation

Initially available for Java and Python, Diffblue plans to expand to additional platforms and software quality domains, including test quality assessment, code review automation, large-scale refactoring, and requirements-driven test generation.

Enterprise Impact

By automating regression test creation, Diffblue enables engineering teams to:

• Accelerate modern application development
• Improve software quality and reliability
• Reduce developer toil and resource costs
• Confidently scale AI-assisted workflows across the organization

The Diffblue Agents platform, founded by researchers from the University of Oxford and backed by IP Group, Albion, Parkwalk, and Citi, is now available for enterprise evaluation.

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Idomoo today announced Strata, a generative AI foundation model that reimagines how brands create video content. Unlike traditional AI video models that output a single flat file, Strata generates fully layered compositions—including text, animation, footage, and actors—allowing enterprises to edit and personalize videos at virtually unlimited scale.

From Flat Files to Fully Editable Blueprints

Layered video has long been a staple in professional production tools like Adobe After Effects, but generative AI has historically only produced uneditable flat videos. Strata changes that by generating structured video blueprints with independent layers for typography, motion, animation, and synchronized audio—all while enforcing brand guidelines automatically.

“Every other AI video model generates pixels: a flat file you can’t meaningfully edit,” said Danny Kalish, cofounder and CTO of Idomoo. “Strata generates structure…solving this makes AI video genuinely usable and scalable for enterprises for the first time.”

Blueprints Over Templates

Template-based AI video solutions often force content into rigid layouts, limiting brand compliance, personalization, and studio-level quality. Strata, by contrast, creates custom blueprints for each video, ensuring production-ready quality and flexible editing for sales videos, onboarding content, eCommerce ads, and more.

Brand DNA Applied at Scale

Idomoo’s Lucas AI Video Agent analyzes a company’s approved content to learn its brand voice, visual style, and motion cues. This Brand DNA—covering design, narrative, and assets—is embedded into every video Strata generates, ensuring 360-degree brand compliance across all content.

Hyper-Personalization for Millions

Each video blueprint is fully personalizable: text, images, footage, and more can be adjusted layer by layer. This allows brands to create real-time, hyper-personalized video experiences for millions of viewers in any language, all while preserving brand integrity.

Strata is patent pending, with early access already in use by several of Idomoo’s largest customers. The model is available now through Lucas AI Video Agent, part of Idomoo’s AI video platform.

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Elpida, a healthcare-focused AI marketing compliance platform, has launched a new solution designed to streamline regulatory oversight for healthcare companies and marketing agencies. By embedding AI directly into marketing workflows, Elpida identifies, ranks, and resolves compliance risks before campaigns are implemented, helping organizations move faster while maintaining strict regulatory standards.

Compliance Without Slowing Marketing

Healthcare marketing operates under some of the strictest regulations in the world, from the FDA and FTC to HIPAA, CMS, and state-level laws like NRS 603A and CCPA. Traditional compliance reviews are often manual, slow, and prone to human error, which can delay campaigns and leave brands exposed to costly penalties.

Elpida’s platform acts as a “Live Regulatory Perimeter,” continuously monitoring websites, brochures, blogs, social media, videos, images, press releases, and other public-facing content. When potential compliance risks arise, the system flags the issue, assesses severity, and provides actionable remediation guidance, bridging the gap between marketing and compliance teams.

“Healthcare companies operate in one of the most heavily regulated marketing environments in the world, yet many organizations still rely on manual reviews that slow campaigns and leave room for costly mistakes,” said Gal Levenhaim, co-founder and CEO of Elpida. “By embedding AI directly into the marketing workflow, we help organizations detect compliance risks instantly and bring campaigns to market faster while maintaining the highest regulatory standards.”

Proactive AI for a Rapidly Evolving Regulatory Environment

With the FDA recently deploying generative AI to monitor healthcare marketing materials, warning letters have risen 73%, making manual reviews increasingly insufficient. Elpida positions itself as an essential tool for risk mitigation, continuously adapting to regulatory changes in real time to ensure campaigns remain compliant without stalling marketing momentum.

Key features include:

• Real-time risk detection: Monitors content across all digital properties continuously.
• Severity ranking and remediation: Flags issues and provides actionable guidance for marketing teams.
• Integrated compliance dashboards: Visualizes live risk profiles across campaigns and channels.
• Regulatory coverage: Supports FDA, FTC, CMS rules, HIPAA, CCPA, and NRS 603A.
• Streamlined onboarding: Maps digital properties and marketing assets for immediate monitoring.

By combining AI-driven monitoring with proactive risk management, Elpida helps healthcare organizations accelerate time to market, reduce friction between marketing and compliance teams, and mitigate financial and reputational risks before they occur.

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Zeta Global (NYSE: ZETA) has officially launched Athena by Zeta™, a superintelligent AI agent designed to help enterprise marketing teams turn data into actionable, predictive decisions. Built for chief marketing officers and marketing leaders, Athena is now generally available to all Zeta Marketing Platform customers.

From Data Overload to Predictive Marketing

Marketing teams often struggle less with collecting data and more with interpreting it. Athena addresses this gap by converting enterprise data into predictive answers—showing which customer segments are most likely to convert, estimating ROI before campaigns launch, and recommending where budget should be allocated next.

“AI will transform marketing, but the companies that win will be the ones that go beyond simply having data and turn that intelligence into outcomes,” said David A. Steinberg, Co-Founder and CEO of Zeta Global.

By combining Zeta’s proprietary data cloud and identity graph with conversational AI interfaces, Athena provides predictive recommendations tied directly to execution, rather than static insights or dashboards. Early adopters report faster workflows, shorter analysis cycles, and clearer ROI visibility.

Early Results and Customer Validation

Since Athena’s debut at Zeta Live in October 2025, marketing teams have reported significant efficiency gains:

• Segment analysis that once took days now completes in minutes
• Campaign workflows that previously spanned weeks can now execute in hours
• Pre-campaign predictive guidance allows marketers to anticipate performance and ROI rather than rely solely on post-campaign reporting

“Athena elevates the way we approach performance marketing. Instead of reacting to what already happened, we now have predictive insight that helps us anticipate opportunities,” said Zack Gharib, President of Red Roof.

Athena’s Capabilities

Athena offers a full suite of AI-powered marketing tools:

• Insights: Conversational analytics for real-time data interpretation
• Advisor: Goal-based campaign optimization and predictive recommendations
• Adaptive revenue modeling: Forecasts performance across paid, owned, online, and offline channels
• Curated intelligence: Leverages Zeta’s proprietary data cloud for actionable guidance
• Voice and conversational interface: Supports direct interaction with the agent
• Journey attribution: Tracks outcomes across channels with built-in governance and safety

Athena is designed not just for insight, but execution, enabling marketers to act instantly on predictive recommendations while maintaining financial accountability and operational transparency.

The Athena agent and its first apps, Insights and Advisor, are now fully available within the Zeta Marketing Platform.

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Proven Cite, a new platform from Proven ROI, has officially launched, offering businesses a first-of-its-kind way to understand, measure, and improve how AI systems evaluate and cite their content. As search shifts from traditional queries to AI-generated answers on platforms like ChatGPT, Gemini, and Perplexity, visibility is no longer about rankings—it’s about whether AI selects and references your content.

The Shift from Rankings to Citations

Traditional SEO tools measure traffic, backlinks, and page rankings. But in the age of AI-driven discovery, these metrics don’t reflect whether AI systems trust, process, or include your content in answers.

Proven Cite evaluates websites across 13 AI-readability and citation factors, helping businesses understand why competitors are cited and what changes are needed to earn citations themselves.

A spokesperson from Proven ROI noted:

“Businesses are realizing that ranking on page one is no longer enough. If your content is not structured in a way that AI can understand, trust, and extract, you will not be included in the answers users actually see.”

This approach aligns with the growing practice of Generative Engine Optimization (GEO), which focuses on making content visible in AI-generated answers rather than just search engine results.

How Proven Cite Works

Proven Cite provides a structured, actionable framework for improving AI visibility:

• AI Readability Scoring: Measures how effectively AI can process your content.
• Citation Gap Analysis: Reveals why competitors are being cited instead of your brand.
• LLM Evaluation Factors: Scores content against the elements that influence AI trust and extraction.
• Optimization Recommendations: Step-by-step guidance to increase citation likelihood across AI platforms.

Designed to be both technical and accessible, Proven Cite gives marketing teams, founders, and agencies clear insights into how AI “sees” their websites and what it takes to be included in AI-driven answers.

Why It Matters

As AI-generated answers become the primary way users access information, brands that are cited gain trust, authority, and influence—often before a user ever clicks a link. Conversely, brands that fail to adapt risk invisibility.

Proven Cite helps businesses navigate this transition, providing an intelligence layer for the new era of digital visibility. According to Proven ROI, success now depends on:

• Traffic → Visibility in answers
• Rankings → Citations
• Keywords → Structured, trustworthy content

Headquartered in Austin, Texas, Proven ROI has historically helped companies tie marketing directly to revenue. With Proven Cite, the company extends that mission into AI-driven discovery, offering marketers a clear path to dominate emerging channels before competitors catch up.

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Brandfuel.ai, an AI-native Product Experience Management (PXM) platform, has partnered with Hairball.io, a leading NetSuite integration provider and 2025 Inc. 5000 honoree, to deliver a unified workflow that connects ERP product creation, multi-channel distribution, and AI-powered merchandising across all sales channels and languages. The solution debuts this week at Shoptalk Las Vegas.

Closing the Gap Between Systems of Record and Engagement

Ecommerce brands today rely on a complex stack:

• NetSuite manages SKUs, pricing, inventory, and operational product data.
• Shopify hosts ecommerce storefronts.
• Klaviyo drives customer engagement through email and lifecycle marketing.
• Marketplaces like Amazon and Walmart extend distribution.
• Integration providers like Hairball move data reliably between these systems.

But after product data leaves ERP systems, merchandising and content creation remain fragmented and manual. Teams often labor to produce product descriptions, curated collections, and channel-specific content across multiple platforms and languages.

The Brandfuel–Hairball partnership closes that gap. Hairball moves operational data seamlessly from NetSuite, while Brandfuel transforms that data into AI-driven, channel-ready product experiences optimized for search, generative discovery (GEO), and merchandising.

Diego Terra, CEO of Hairball.io, explained:

“We’ve seen a consistent challenge: what happens after the data leaves the ERP. Brandfuel brings real-world ecommerce experience, turning operational product data into high-performing, channel-ready experiences at scale.”

Unified Workflow for Modern Commerce

The solution adapts to two types of retailers:

1. Brands with curated product portfolios – Brandfuel uses the organization’s full digital asset library—design files, catalogs, photography, marketing assets—to create rich, search- and AI-optimized product experiences.
2. Large-scale retailers – Brandfuel ingests data from fragmented supplier sources, PDFs, spreadsheets, and legacy systems, generating on-brand, multilingual product descriptions ready for publication across storefronts, marketplaces, and international markets.

Together, the companies provide a single, operationally integrated workflow that connects ERP-managed product data to merchandising and content systems that actually drive sales.

Launch at Shoptalk

Brandfuel will showcase the partnership at Shoptalk Las Vegas, booth SU21 in the Spark Startup Pavilion. Retailers can explore how the combined solution streamlines product onboarding, channel distribution, and AI-powered merchandising across every market and sales channel.

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Wisse Kommunikatie, a leading Dutch communications consultancy with an international footprint, has joined the Brandi Agency Partnership Program, a global initiative designed to help agencies guide clients in the age of AI-driven brand discovery. The move equips Wisse Kommunikatie with Brandi AI’s Generative Engine Optimization (GEO) platform, offering unprecedented visibility into how brands appear across AI-generated answers.

From SEO to GEO: The Next Layer of Brand Visibility

As AI-driven discovery shifts how audiences find and evaluate brands, traditional marketing and PR approaches no longer suffice. GEO focuses not on search rankings, but on how brands are cited, represented, and trusted in responses generated by AI platforms like ChatGPT and Claude.

Serge Beckers, managing partner at Wisse Kommunikatie, explained:

“Brandi AI reveals what's influencing visibility—from missing context to weak trust signals—so we can take clear, data-backed action. It’s giving brands control in the AI discovery layer where buying decisions increasingly start but have long been impossible to measure.”

This approach is particularly critical in areas like crisis communication, where accuracy and visibility can influence public perception and trust. With Brandi AI, Wisse Kommunikatie can monitor AI-generated narratives and correct misinformation in real time, strengthening client credibility across markets.

How the Partnership Works

Through the Brandi platform, Wisse Kommunikatie can now:

• Track how client brands appear in AI-generated answers
• Compare visibility and credibility against competitors
• Identify gaps in context or trust signals
• Optimize content and messaging for GEO
• Monitor AI output across languages and regions to ensure consistent representation

Leah Nurik, CEO and co-founder of Brandi AI, added:

“Agencies partnering with Brandi AI are changing the game for how brands create value in the AI era. Together, we’re helping clients understand how their brands are seen, cited, and trusted—and turn those insights into stronger AI-driven visibility.”

Why It Matters

As AI reshapes search, discovery, and brand evaluation, agencies and marketing leaders now face a new responsibility: ensuring brands are not only discoverable but also credible within AI-generated outputs. Platforms like Brandi AI allow PR teams to measure, monitor, and optimize this visibility continuously—a critical capability for global organizations navigating different markets, languages, and AI models.

Wisse Kommunikatie brings decades of expertise in crisis communication to this AI-centric service, enabling clients to respond faster, protect reputation, and strengthen trust across AI-driven discovery channels.

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