Insights

Reduce content noise. Recover context.

The goal of xtnd.tv is to make it easier for agencies and advertisers to extract the essence of their audience: who they are, what they watch, and whether delivered media matches the marketing story you intended to tell.

The problem

Titles are messy - and often missing

In many cases, publishers do not want to pass content titles to advertisers. The motives are understandable: reduce cherry-picking, protect premium shows, and keep programmatic buying broad. But it leaves buyers blind in reporting.

  • Content titles can be empty, truncated, or inconsistent
  • Series vs episode vs promo fields get blended or lost
  • Different supply paths produce different shapes of metadata
The beta

Directionally strong enrichment

We have spent years deconstructing this problem and building practical solutions. This first beta module reduces title noise and fills gaps with AI-assisted inference. It will never be a perfect mirror of reality - but it is strong enough to provide meaningful context.

Normalize Infer Explain

What the module outputs

Outputs are designed to drop into your existing reporting workflow.

Output What it helps you answer
Clean Title + Clean Series What did we actually run against - at a human readable level?
Content Category Path Are we showing up in the right programming environments for the brand story?
Genre and Language cues What kind of viewing mindset was the audience in - and in what language context?
Confidence signals How sure are we - and where should a buyer be cautious in interpretation?
Narrative exports Can I explain this to an advertiser without a 40-tab spreadsheet?
In beta, the goal is usefulness: reduce ambiguity, increase explainability, and make next actions obvious.

How it works (self-serve)

xtnd.tv operates in a self-serve capacity. You keep control of your data.

1) Request logs

Ask your DSP of choice for delivery logs. We provide specs - and can adapt to your data shape if needed.

2) Enrich and unify

AI-assisted normalization and inference (Claude, ChatGPT, Gemini) turns raw rows into structured context.

3) Export narratives

Dashboards and exports that can be inserted into your agency reports - and explained in one slide.

Practical notes
  • Beta outputs are directionally strong - they should support decisions, not replace ground truth.
  • Some publishers will always withhold details. We focus on the useful signal that remains.
  • We aim to make the content layer legible without enabling cherry-picking.

If you want a placeholder call-to-action form, add it later - this template keeps things lightweight.