LiveRamp: additional device graph matches & 1P audience targeting. Podscribe has partnered with LiveRamp, an enterprise data collaboration and identity resolution platform, to strengthen audio campaigns in three ways: 1. Another device graph for more deterministic attribution. Adding LiveRamp as a fourth device graph means more matches and higher-confidence attribution, resolving noisy signals back to households by matching MAIDs (mobile advertising IDs) on impression IPs and MAIDs/HEMs (hashed emails) on conversion IPs to household IPs. Note: In the coming weeks, Podscribe will announce separately how Podscribe utilizes all device graphs, and what new toggles will be available for marketers looking to customize their results. 2. Plan, report and activate on 1P audience segments. Advertisers with first-party audiences in LiveRamp can now: * Target those audiences directly within Podscribe SmartServe * Verify delivery against them * Understand what shows' audiences most overlap with them Advertisers can also import first-party segments directly through the Podscribe tag or through other partners such as TransUnion, Experian and Segment. 3. LiveRamp 3P audience segments. Everything above i.e. targeting, delivery verification, and show overlap, also applies to the 500K+ premium third-party segments available in LiveRamp's Data Marketplace. These segments will soon be available in Podscribe's Overview and Showpage > Audience tabs (alongside TransUnion segments) and targetable through SmartServe. No more guessing whether your podcast buys reached the audiences that matter most to your brand. Get started today! If you have a first-party audience in LiveRamp or want to put third-party segments to work across your audio campaigns, reach out to its team at [email protected] to get set up or send Podscribe a note using the form below.

Koddi accelerates Commerce Media evolution via strategic GIPHY platform and LiveRamp identity collaboration. Media technologies company Koddi has made a groundbreaking move and partnered with GIPHY. Thanks to this partnership, global companies now can run targeted advertisements using digital stickers, GIFs, reactions in conversation, and in-app search engines. Combining the power of commerce media infrastructure of Koddi, the scale of GIPHY, and the first-party identity solution of LiveRamp, the multi-stakeholder collaboration empowers digital marketers to catch the eye of consumers during natural everyday micro-moments. The project marks the beginning of the new era of advertising monetization for GIPHY and provides omnichannel brands with an opportunity to scale their reach in a privacy-minded way. Self-learning ad tech Koddi's role as an enabler of technology for the digital media companies is made clear by the collaborative approach adopted. Through the provision of a platform that can deliver ads, manage budgets, and offer analytics to enhance performance, Koddi can allow high-volume players like GIPHY to set up advertising networks that are sustainable and scalable into the future. "Consumers don't just interact with brands when they're shopping. They connect with them while they're searching and sharing," said Eric Brackmann, VP of Commerce Media at Koddi. "This partnership brings commerce media capabilities to those moments, helping advertisers activate campaigns more effectively while giving GIPHY the foundation to scale and monetize its advertising business." Expanding non-endemic retail media beyond traditional product pages. Given the rising dependency on visual-based messages in digital communications, programmatic advertisers are actively looking for other means through which they can connect in a more authentic manner than is possible with traditional desktop banners and pre-roll videos. Through the integration, there will be enhanced demographics, campaign coordination, and measurement in terms of ROAS for brands within GIPHY's ad unit inventory. "As we continue to grow GIPHY Ads, we're focused on delivering more value to advertisers while creating a scalable foundation for long-term growth," said Kevin Hein, Chief Growth Officer at GIPHY. "Koddi's technology helps us streamline campaign activation, improve performance and build the infrastructure needed to support the next phase of our advertising business." As the commerce media market rapidly branches out from traditional retail websites, this deployment demonstrates how organizations can systematically leverage an owned digital footprint. By combining data privacy standards, predictive campaign analytics, and high-engagement short-form media, Koddi provides the essential tools to deploy commerce media networks for any enterprise with an active, owned digital audience. Platform key deliverables for enterprise advertisers: * Conversational Activation: Contextual placement within the messaging flows, reactions, and daily expressions used by billions of mobile device users. * Privacy-First Identity Integration: Leveraging LiveRamp infrastructure to enable highly secure, first-party audience synchronization without reliance on signal degradation or third-party cookies. * Unified Performance Analytics: Streamlined reporting pipelines that help media buying teams quantify the impact of short-form conversational campaigns.

Integral Privacy Technologies raises $25M to build the privacy layer for AI's real-world data push. July 2, 2026 Integral Privacy Technologies has raised $25 million in total funding, with backing from Venrex, The General Partnership, Array Ventures, GreatPoint Ventures, LiveRamp Ventures, Haystack, Virtue Ventures, Also Capital, Caffeinated Capital, LifeX Ventures, Circle & Co, and WS Investments. The round is aimed at scaling what the company calls its Forward Deployed Privacy Services - a service layer meant to make sensitive real-world data usable for AI training without triggering the regulatory and re-identification risks that normally keep that data locked away. The problem: public data is running out. The framing here matters for anyone tracking where AI training data is headed next. The first generation of large models was trained largely on public web data and human-curated datasets. That well is drying up relative to demand, and AI builders are now pushing into health records, financial transactions, customer interaction logs, operational systems, and codebases - data that carries real decision-making complexity but also carries contractual and regulatory exposure that public data simply doesn't have. Enterprises sitting on this data increasingly see it as a monetizable asset, but raw real-world data is legally and technically difficult to hand over. Masking and synthetic data generation have become commodity capabilities available in most modern data stacks - that's no longer the differentiator. What Integral is betting is scarce, and therefore valuable, is the privacy engineering expertise to strip re-identification risk out of a dataset surgically, without destroying the statistical signal that makes it useful for AI in the first place, plus an independent risk assessment that a data buyer can't credibly produce on its own. How the service works. Integral's Forward Deployed Privacy Services model embeds a dedicated team - statisticians, privacy engineers, software engineers, and methodologists - directly into a customer's data pipeline, rather than delivering a one-time audit or a software tool. The work splits into two functions: * Entity-preserving remediation, which reduces re-identification risk while retaining the longitudinal relationships, rare cohorts, and behavioral signal that make a dataset valuable for AI applications rather than just anonymized noise. * Independent, continuous risk assessment, evaluated against the specific dataset, intended use case, and recipient - and re-run as the pipeline evolves, rather than treated as a static, point-in-time review. The output is a defensibility artifact matched to whatever regulatory framework applies. In healthcare contexts, that means an Expert Determination under HIPAA §164.514(b)(1), signed by a qualified statistical expert. In other regulatory contexts, it takes the form of a signed defensibility opinion or comparable instrument. Healthcare-Tested, now expanding. CEO and co-founder Shubh Sinha says the company spent four years working through this problem in healthcare - one of the most heavily regulated data environments that exists - before concluding that the real bottleneck was never data access. It was preserving the value-bearing signal in the data while managing the privacy, regulatory, and business risk that otherwise prevents its use. The company points to a pharmaceutical customer that used its service to unlock a multi-source training dataset previously considered too risky to touch, by building privacy engineering into the dataset assembly process itself rather than retrofitting controls at final review. Integral's methodology is grounded in peer-reviewed statistical disclosure limitation research, and the company is now extending beyond healthcare and life sciences into broader AI labs, data and annotation platforms, and enterprises looking to participate in what it's calling the real-world data economy. Why this matters for the AI data supply chain. As frontier labs exhaust easily-licensed public and synthetic data sources, deals for proprietary, regulated datasets are going to become a larger share of how models get trained on domain expertise - healthcare, finance, enterprise operations. That shifts the bottleneck from data availability to data defensibility: can a company prove, to a regulator or a counterparty, exactly how a dataset was cleared for use and what residual risk remains. Integral is positioning itself as infrastructure for that exact question, and the investor list - which includes LiveRamp Ventures, a firm with deep roots in data connectivity and identity resolution - suggests the privacy-layer thesis is drawing interest from data-industry incumbents as well as AI-native venture funds.