Five Applications. One Agentic Intelligence Layer.
Purpose-built applications that work together — or independently — to deliver measurable business outcomes. Each application is configured to your organization's institutional knowledge, proprietary data sources, and existing tech stack, so Kana works with what you've already built rather than replacing it.
Give your media sales team the AI-powered intelligence they need to win more deals, faster, with audience insights, competitive context, and automated proposal creation built in.
Streamline campaign operations and give your ops team real-time visibility to optimize performance across every campaign in flight, simultaneously, at scale.
Stop subscriber churn and maximize viewer or reader lifetime value with agentic personalization, monitoring behavioral signals in real time and executing tailored interventions at scale.
Unify first-party audience data across every touchpoint, resolve identity across devices, and activate precision audience segments, enabling premium packaging without data engineering overhead.
Turn AI-driven traffic into a managed strategic asset. Monitor how your content and your advertisers' brands appear in AI-generated responses, and optimize for AI-era discovery.
Built for Every Corner of the Media World
Whether you operate a retail media network, a digital publication, or a streaming service, Kana's agentic platform addresses the unique revenue, operations, and personalization challenges of your segment.
Media Networks Are Undermonetizing Their First-Party Advantage
Retail and commerce media networks sit on rich, purchase-linked first-party data, but fragmented audience infrastructure, slow sales cycles, and manual ops are preventing them from competing at the scale of walled gardens.
Brand advertisers demand proof that media spend drives actual sales, but linking impression exposure to purchase transactions across systems requires significant manual work, and results arrive too late to influence budget decisions.
Building a compelling media package, pulling audience data, assembling category benchmarks, and formatting a proposal, takes sellers days. That timeline limits deal volume and disadvantages sellers against faster-moving competitors.
Customer signals from e-commerce, in-store, app, and loyalty programs sit in separate systems, making it difficult to build the verified, high-fidelity audience packages that brand advertisers increasingly demand.
Retailer media planning, co-op budget tracking, and shopper program performance are managed in Excel — disconnected from brand and performance marketing systems.
As brand advertisers scrutinize contextual adjacency and how their brands appear in AI answer environments, media networks need proactive governance infrastructure, not a reactive response after an incident.
Publishers Are Navigating a Structural Revenue Shift
Between cookie deprecation, declining display CPMs, AI-powered content discovery eroding search traffic, and subscription headwinds, publishers face simultaneous pressure on both advertising and subscription revenue, with legacy infrastructure that wasn't built for what comes next.
Publishers sitting on rich first-party behavioral data have no efficient way to segment, package, or activate it for advertisers or their own subscriber marketing, leaving the most durable post-cookie asset underutilized.
AI-powered search tools and assistants answer user queries directly, without sending traffic to publisher sites. Content that built audiences through search discovery is losing reach without publishers knowing how, where, or how to respond.
Building a differentiated, audience-backed media package requires pulling data from multiple systems and crafting category context, a process that bottlenecks sellers and limits the number of opportunities a team can pursue at once.
Churn spikes at renewal windows, after content gaps, or when competitive alternatives launch. Most publishers lack the infrastructure to identify at-risk subscribers early and intervene with personalized, timely offers.
Managing delivery, pacing, and performance reporting across display, native, newsletter, audio, and podcast inventory simultaneously creates significant ops overhead, and manual errors damage advertiser relationships and renewal rates.
Streaming Platforms Are Caught Between Revenue and Experience
Streaming and video platforms face competing pressures: maximizing ad revenue and subscriber LTV while protecting the viewer experience that keeps subscribers engaged. Neither goal is achievable with the batch workflows and manual ops models most platforms are still running on.
Churn models exist, but by the time risk scores reach the marketing layer, at-risk subscribers have often already cancelled. Intervention needs to happen at the behavioral signal, not days later through a batch campaign.
Ad break frequency, pod length, and interruption timing are set as static rules, not adjusted dynamically based on viewer engagement, content type, session depth, or real-time yield outcomes.
Greenlight and renewal decisions rely on incomplete visibility into how content titles actually drive subscriber acquisition, retention uplift, and long-term engagement, forcing reliance on instinct over real-time evidence.
A single viewer appearing as four different users across mobile, smart TV, laptop, and tablet fragments behavioral signals, making content personalization less relevant and ad targeting less precise and valuable.
As consumers increasingly use AI assistants to find shows and content, streaming platforms that don't actively manage their presence in AI-generated recommendations risk losing organic discovery to competitors who do.