Two privacy-focused technology companies are joining forces to challenge the dominance of Big Tech in search and web browsing.

MASQ Network and Timpi have announced a full product merger that combines decentralized connectivity, private browsing, and independent search infrastructure into a single integrated platform.

The unified product will launch under the MASQ brand, bringing together MASQ’s privacy browser and distributed VPN technology with Timpi’s independent search index—an alternative to the centralized search infrastructure dominated by major technology companies.

The companies say the result is a browsing experience designed to function without user tracking, ad profiling, or centralized data collection.

A New Attempt to Break Big Tech’s Search Monopoly

The modern internet largely runs on infrastructure controlled by a handful of technology giants. Most web searches and browsing activity ultimately flow through ecosystems dominated by companies like Google, Microsoft, and Apple, whose services are deeply tied to advertising-driven data collection.

MASQ and Timpi are positioning their merger as an alternative to that model.

Rather than routing user activity through centralized data systems, the combined platform relies on decentralized networking and an independent search index that operates outside traditional Big Tech infrastructure.

The aim is to offer private browsing and search capabilities without relying on surveillance-based advertising systems that track user behavior.

“Consumers don’t adopt infrastructure—they adopt products,” said Aaron Friedlander, founder of MASQ.

“This merger lets us package private browsing, independent search, and secure connectivity into one experience that everyday users can actually use.”

How the Combined Platform Works

The integrated MASQ platform merges three core components:

Private Browser
MASQ’s browser provides privacy-focused web access designed to minimize data collection and tracking.

Decentralized Network and VPN
The platform routes internet traffic through a distributed peer-to-peer network rather than centralized servers, adding an extra layer of privacy and resilience.

Independent Search Index
Timpi contributes the search engine infrastructure, which has been under development for three years and operates independently of the major search providers.

Together, these elements create a browsing environment where users can search and navigate the web without feeding data into the advertising ecosystems that dominate most online services.

Timpi Search Enters Public Beta

Alongside the merger announcement, Timpi Search is now available to the public in open beta.

Users can access the search engine directly through the Timpi website without creating an account, marking the first time the company’s independent search index has been broadly accessible.

Previously, the platform had been limited to community testing.

The beta version can currently be used through:

• The Timpi website
• A standalone browser extension
• The MASQ Browser environment

The release signals Timpi’s shift from experimental infrastructure toward a publicly available search product.

Building Infrastructure Outside Centralized Platforms

The merger also reflects a growing movement toward decentralized internet infrastructure.

Advocates argue that much of today’s digital ecosystem is controlled by centralized platforms that influence search visibility, advertising economics, and data ownership.

By contrast, Timpi’s architecture aims to create a community-driven index that can operate outside those centralized control points.

“The internet today runs through a handful of chokepoints,” said Gareth Evans, co-CEO of Timpi.

“We’re building the infrastructure that sits outside that—inspectable, decentralized, and owned by its community.”

A Different Business Model for Search

One of the biggest challenges facing alternative search platforms is revenue.

Most large search engines generate billions of dollars through targeted advertising fueled by user data collection.

MASQ and Timpi say their approach avoids selling user data while still supporting monetization through other channels.

The combined platform plans to generate revenue through:

• Search advertising that does not rely on user profiling
• Enterprise data and API services
• Consumer subscriptions for MASQ’s privacy services

Whether that model can compete with the massive scale of existing search ecosystems remains to be seen, but privacy-focused alternatives have steadily gained traction as users grow more concerned about online tracking.

What Comes Next

The fully integrated MASQ Browser with native Timpi Search is expected to launch in the coming months.

The companies also plan to expand distribution through MASQ’s partnership network while continuing to develop the underlying decentralized search infrastructure.

If successful, the combined platform could represent a new category of web tools—one that merges search, privacy, and connectivity into a decentralized alternative to the dominant Big Tech ecosystem.

For users increasingly wary of how their data fuels the modern internet, that proposition may be gaining relevance.

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AI assistants are quickly becoming the new interface for enterprise decision-making. Now FiscalNote wants to ensure policy and regulatory intelligence is part of that workflow.

The company announced that its PolicyNote MCP has been approved and listed in the OpenAI App Store, allowing developers, analysts, and enterprise teams to access structured policy and regulatory data directly inside ChatGPT.

The move effectively embeds FiscalNote’s policy intelligence infrastructure into one of the fastest-growing AI platforms. According to OpenAI, ChatGPT surpassed 700 million weekly active users earlier this year, making it a powerful distribution channel for enterprise data services.

With the listing, users can connect to the PolicyNote MCP server inside ChatGPT without additional integrations, allowing GPT-powered workflows to query real-time policy developments through natural language prompts.

Turning ChatGPT Into a Policy Intelligence Interface

Traditionally, policy monitoring tools have lived inside specialized enterprise platforms used by government affairs teams, legal departments, and regulatory analysts.

FiscalNote’s new integration shifts that model.

Instead of requiring users to access a separate application, PolicyNote MCP brings legislative and regulatory intelligence directly into conversational AI workflows.

Once installed, users can:

• Query legislative and regulatory developments worldwide
• Access verified information from government sources
• Monitor policy changes across jurisdictions
• Integrate structured policy data into research workflows

The integration also enables developers to build custom GPT-powered assistants focused on specific policy tasks using PolicyNote MCP tools.

For organizations managing regulatory compliance or government relations, that could mean faster analysis of policy developments without leaving their AI work environment.

From Software Platform to AI Infrastructure

The OpenAI App Store listing signals a broader strategic shift for FiscalNote.

Rather than operating solely as a destination software platform, the company is increasingly positioning its policy data as an intelligence infrastructure layer that powers AI-driven workflows.

“As AI agents become an increasingly important interface for how organizations operate and make decisions, the opportunity is expanding for trusted intelligence to be embedded directly into those workflows,” said Josh Resnik, CEO and president of FiscalNote.

By embedding its data inside AI environments, the company can potentially reach millions of users who may never purchase a full enterprise platform but still need reliable policy insights.

A New Distribution Channel for Enterprise Data

The OpenAI App Store serves as a global discovery layer for AI-integrated applications, allowing software vendors to distribute specialized tools directly inside ChatGPT.

For FiscalNote, that creates a new path to customer acquisition.

Instead of relying entirely on enterprise sales cycles, the company can reach developers, distributed teams, and analysts already building AI-driven workflows.

This approach could significantly expand FiscalNote’s addressable market by enabling product-led growth across new geographies and customer segments.

The model also introduces consumption-based revenue opportunities, where organizations pay for access to policy intelligence within AI workflows rather than licensing full software platforms.

Governance and Data Control

Despite the integration with ChatGPT, FiscalNote retains full control over its proprietary policy data.

Access to PolicyNote MCP remains a commercial offering, with users required to transact directly with FiscalNote to obtain the data.

The company emphasized that its legislative and regulatory datasets are only available within authorized user workflows and are not used to train AI models or for other platform-level purposes.

That distinction is likely to matter for government agencies, enterprises, and legal teams concerned about data governance when using AI-powered tools.

AI Agents and the Future of Enterprise Workflows

The integration also reflects a larger trend in enterprise software: the rise of AI agents as primary interfaces for accessing business intelligence.

Instead of navigating dashboards or complex data platforms, users are increasingly asking AI systems to retrieve and analyze information through conversational queries.

For companies like FiscalNote, embedding domain-specific intelligence into these environments could become a powerful distribution strategy.

By turning ChatGPT into a gateway for policy data, FiscalNote is effectively positioning its platform as part of the emerging infrastructure powering AI-native workflows.

If that strategy gains traction, the company may evolve from a policy software provider into a critical intelligence layer within the growing ecosystem of enterprise AI agents.

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In the evolving world of enterprise AI, knowing what you don’t know is half the battle. Appier, an AI-native Agentic AI-as-a-Service (AaaS) company, is tackling that challenge head-on with its latest research paper, On Calibration of Large Language Models: From Response to Capability. The study introduces Capability Calibration, a framework designed to help AI systems gauge their own problem-solving abilities—before generating answers.

From Answer Confidence to Real-World Capability

Traditional large language model (LLM) calibration focuses on a single response: is it right or wrong? But LLMs are inherently stochastic—ask the same question twice, and the answers may differ. For businesses, the real question isn’t whether one answer is correct; it’s whether the AI can reliably solve the task at hand.

Appier’s capability calibration framework shifts the focus from one-off responses to overall task-solving probability. Essentially, AI agents learn to “know their limits” and decide whether to handle a problem immediately or tap additional resources. As Chih-Han Yu, Appier’s CEO and co-founder, puts it:

“With capability calibration, an agent can estimate its probability of success before responding and allocate resources intelligently. Simple queries can be handled quickly, while complex tasks leverage stronger models or additional compute.”

The implications for enterprises are clear: smarter, more efficient AI that reduces wasted compute and delivers more reliable outcomes.

How It Works

The research evaluates multiple confidence-estimation techniques across three LLMs and seven datasets, ranging from knowledge-intensive to reasoning-heavy tasks:

• Verbalized confidence: The AI explicitly states its confidence in text or as a percentage.
• P(True): Estimates answer correctness probability based on generation signals.
• Linear probes: Analyze internal model signals to determine whether the AI truly “understands.”

Linear probes emerged as the best compromise between cost and accuracy—so lightweight they can run for less compute than generating a single token, yet robust enough for enterprise use.

Practical Applications: Smarter Resource Allocation

Capability calibration opens two key doors for enterprise AI:

1. Pass@k prediction: Estimate the probability that an LLM will get at least one correct answer after k attempts—without actually generating multiple responses.
2. Dynamic inference allocation: Assign more computational power to tougher tasks and less to simpler ones, squeezing more value from existing AI infrastructure.

This approach doesn’t just make AI faster or cheaper; it gives businesses a reliable metric to trust the AI’s decisions, including when to involve humans or external tools.

Why It Matters

As companies increasingly rely on AI for marketing, sales, and operational decisions, overconfident or unreliable AI can be costly. Capability calibration provides a foundation for trustworthy, agentic AI—systems that actively manage tasks and resources instead of passively responding to prompts.

Looking ahead, Appier plans to expand this framework for model routing, human-AI collaboration, and more robust decision-making in enterprise contexts. For marketers and tech leaders, these innovations promise not only better performance but a clearer path to scaling AI across complex workflows.

In short, Appier is helping AI stop bluffing—and start delivering measurable business value.

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At Oracle AI World, Oracle unveiled major updates to Oracle AI Agent Studio for Fusion Applications, a development platform designed to build, connect, and run AI-powered automation and agentic applications across enterprises. The centerpiece of the update is the Agentic Applications Builder, a natural-language-based environment that lets users compose workflows and integrate Oracle, partner, and third-party agents—without writing traditional code.

From Dashboards to Autonomous AI Applications

Enterprise AI is moving beyond pilots and copilots to fully operational, outcome-driven systems. Oracle positions AI Agent Studio as the bridge to this autonomous enterprise, enabling AI agents to reason, act across business systems, and execute complex processes continuously. According to Chris Leone, EVP of Applications Development at Oracle, the platform allows organizations to tailor AI to their workflows, expertise, and operational priorities, while maintaining the governance, trust, and security critical for enterprise deployment.

Key Features in the Latest Release

• Agentic Applications Builder: Create outcome-focused AI applications using natural language. Reuse Oracle, partner, and external agents while connecting enterprise data seamlessly.
• Workflow Orchestration: Coordinate multi-step, multi-agent execution with rules, logic, and human oversight for enterprise-grade reliability.
• Content Intelligence: Combine unstructured data with transactional data to improve decision-making and automate more effectively.
• Contextual Memory: Enable AI agents to remember relevant context across interactions and workflows, reducing repetition and improving collaboration.
• LLM Multimodal Capabilities: Process and generate non-text inputs like images, audio, and video, unlocking insights across diverse enterprise datasets.
• Monitoring & Prompt Playground: Test, debug, and adjust agent behavior in real-time to scale deployments with confidence.
• Agent ROI Dashboard: Measure business impact, including time saved, cost reductions, and productivity gains per agent.

These updates reinforce Oracle’s vision for AI that actively runs the business, rather than passively supporting it. By integrating AI directly into existing workflows, organizations can accelerate automation while ensuring responsible governance and measurable ROI.

Enterprise Ecosystem and Partner Support

With over 63,000 Oracle-certified experts, customers can leverage AI Agent Studio to deploy and optimize agentic applications across their organization. Partners like Accenture, Deloitte, KPMG, and PwC are already helping clients tailor AI to specific use cases, emphasizing flexibility, controls, and accountability.

Oracle AI Agent Studio also seamlessly connects with Oracle Fusion Applications—including ERP, HCM, SCM, and CX—enabling agentic applications to automate finance, HR, supply chain, and customer experience processes.

As enterprises increasingly adopt AI at scale, Oracle’s platform offers the tools to orchestrate workflows, maintain context, and track business impact—turning AI from a helpful assistant into a strategic driver of enterprise performance.

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Arc XP, the content platform and operating system for ambitious media companies, has announced a new integration with TollBit, a platform that helps publishers track and monetize AI usage of their content. The partnership aims to give publishers transparency and control over AI bots scraping their websites, while opening new revenue opportunities.

Addressing the AI Content Scraping Challenge

Generative AI bots increasingly pull from publisher content to generate real-time answers, creating infrastructure strain, content commoditization, and limited visibility into intellectual property use. While some media companies have signed licensing deals with major AI providers, many lack the systems to detect and manage automated traffic effectively.

The Arc XP–TollBit integration tackles this problem head-on. According to Sharad Vivek, Global Head of Partnerships and Alliances at Arc XP,

“Publishers need control and transparency, not guesswork. This partnership gives media organizations the ability to manage AI access on their terms and participate in emerging AI licensing models.”

From Detection to Monetization

Unlike traditional bot-management tools that simply block traffic, this integration provides a commercial pathway for AI access. Once activated from the Arc XP dashboard, publishers can:

• Monitor AI bot traffic in real time with TollBit Analytics
• Identify and classify AI bots automatically
• Block unwanted access or redirect bots to TollBit Bot Paywall
• Configure licensing terms and pricing for AI agents and LLM developers

This allows media organizations to enforce access rules and monetize automated retrieval of content, with participation fully optional and configurable.

Building the Infrastructure for the AI Era

As AI transforms content consumption, publishers need infrastructure that supports both protection and participation. Arc XP provides the edge integration and policy-control layer, while TollBit handles agent authentication, licensing, and monetization.

Toshit Panigrahi, CEO and co-founder of TollBit, explained:

“By joining forces with Arc XP, we're making it effortless for publishers to set their own terms and monetize their content with ease.”

The integration strengthens Arc XP’s AI-readiness strategy, enhancing its offerings around content protection, monetization, and audience ownership.

The feature is available to Arc XP publishers using the Edge Integration Framework and requires a TollBit account.

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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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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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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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