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Tailsearch for coverage tracking

A story rarely stays in the outlet that ran it. It gets picked up, rewritten, and reposted under changed headlines, sometimes only for a few days. The engine follows one article's trace across six search engines, tells a real republication from a coincidence, and screenshots the proof before the page can disappear.

How it works

You giveA direct link to the published article and its publication date. Articles go in one at a time or grouped into a campaign.
The system doesThe tracing. It extracts the article's title, description, and date, queries six search engines across several pages of results, compares every candidate page against the original, and captures a screenshot of each confirmed match.
You stay in controlOver what counts as a match. A relevance threshold sets how close a candidate has to be, and analysts move it per campaign, because a long or loosely worded headline changes what a fair match looks like. Every result carries its own evidence, so nothing rests on the tool's word.
You getA ranked list of republications with a screenshot each, campaign-level statistics, and a CSV plus an archive of the proof.

Inside the system

08 components
01

Campaign batching

Articles group into a campaign with a name and a category, and a whole list loads as a table for batch processing.

02

Article extraction

You paste the link and set the publication date. The system reads the article and pulls out its title, a short description, and the date it uses for verification later.

03

Queued multi-engine search

Every submitted article enters a job queue that guarantees execution, retries failures, and respects each engine's rate limits. Underneath it queries six search engines and reads several pages of results from each, with a separate title-and-quote pass for exact matches.

04

Semantic matching

Each candidate page is compared against the original by title, snippet, and description, which is what separates a real republication from a story that merely covers the same event on the same day.

05

Verification and dedup

The same matching core runs initial filtering, date verification, and duplicate protection before anything reaches the results view, so each match counts exactly once.

06

Screenshot proof

Every match ships with a screenshot of the page as it appeared. Temporary placements and removed content stop being unprovable.

07

Campaign analytics

Each campaign rolls up into live figures: domains reached, the spread of similarity scores, and the top domains across every article in the group.

08

Ranked export

Results rank by match percentage against a threshold you set, then export as CSV with every screenshot in one archive, or individually straight from a row.

Who it's for

Good fit
PR agencies reporting campaign reach to clients
In-house communications teams measuring earned coverage
Brands tracking where an announcement actually landed
Publishers following syndication of their own material
Teams building media rankings from real distribution data
Anyone currently checking republications by hand
Not a fit
Tracking brand mentions in general rather than one story's trace
A handful of placements a year, where manual checking is quicker
Coverage that lives entirely behind logins search engines never see

What you can configure

01
What counts as a matchThe relevance threshold per campaign, because a long headline and a short one do not behave the same way.
02
Where it searchesWhich engines run, how deep into the results, and which languages and regions are covered.
03
What evidence you keepScreenshot capture, retention, and the export format your reports are built from.
04
How it reaches your reportingCSV, an archive of screenshots, or a direct feed into the analytics you already produce.

What we need from you

Your published articlesThe links and publication dates you already track. That is the entire input.
Your match criteriaWhat your analysts count as a genuine republication, which is the judgment the system has to learn.
One analystSomeone who can say whether a match is real, a few hours a week during the build.

Example use cases

Uncovering the 60% of republications manual search missed

An international PR agency tracked each story's pickups by hand, spending up to an hour per article and still missing about 60% of them. An AI parser now searches six engines, verifies every match, and screenshots the proof in about five minutes.

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See the solution →

Show a client every republication of their story.

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