Research

Clippable Research is a marketing intelligence lab on top of frontier models. Those models get better at execution and optimization. People keep taste, brand judgment, and what actually feels culturally right.

The loop we want is AI proposes, humans approve and steer, the system learns that taste, then it executes at scale. We would rather not let a fully autonomous stack decide what a brand should sound like.

We are not trying to train a general foundation model. Let those labs spend the billions. Clippable should get smarter when a new one ships, while the marketing knowledge, customer history, taste data, and execution stay here.

Research Note #002 · July 24, 2026

Atoms Research and the Marketing Measurement Moat

Why proprietary physical-attention data beats hardware, and what to measure next

Atoms ResearchPhysical AttentionEvaluation & MeasurementSocial & Marketing AI

If the goal is a durable moat, hardware is the wrong object. Devices get copied. Proprietary datasets compound. This note argues that Clippable Research should pursue atoms research, measurement of real-world attention and conversion that the ad industry still treats as a black box, and connect those signals to the same learning loops we already run for online creatives. Online attribution is a solved-ish problem. Offline is not. Rough foot-traffic panels and billboard cameras exist; almost nobody has closed the loop from physical dwell and engagement back to campaign outcomes at the resolution marketing agents need. We outline a concrete direction: privacy-safe, on-device computer vision for passersby, stops, looks, and dwell time, linked to creative and location metadata so agents can learn which work actually holds attention in the physical world.

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Research Note #001 · June 4, 2026

Architecting Domain-Embedded AI Agents

Memory, Tools, Governance, and Multi-Surface Orchestration

Agent ArchitectureLLM SystemsHuman-in-the-Loop

Most production “agents” today are still chat sessions with extra API calls. That is enough for drafting copy; it is not enough when work has owners, deadlines, spend limits, and an audit log. This research note lays out a practical architecture for domain-embedded agents: the model reads and writes through product schemas, keeps memory outside the prompt, routes side effects through tools, and leaves humans on the hook for irreversible steps. The argument is grounded in published work on tool-augmented language models and in operational constraints from social-media programs, where coordination cost often exceeds model capability. The note is descriptive engineering guidance, not a benchmark study.

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