Media & publishing

The right story now, the next story after.

Readers search for events, people and topics that changed yesterday. AACSearch balances relevance with freshness, answers “what happened with…” from your archive, and recommends the next read.

Queries plain keyword search gets wrong

выборы итогиfreshness mattersчто известно о …summary questionинтервью Ивановperson + formatкурс доллара прогнозrecurring topic

Where search loses customers today

Old stories on top

A detailed 2019 article outranks this morning's update.

One article per visit

Readers leave after the article they came for.

Archive without context

Readers want the story so far, not a list of 40 links.

How AACSearch solves it, layer by layer

Each card names the capability behind it. Items marked “in development” are designed and scheduled, not live yet.

Finds the right item from the first letters — typos, synonyms, filters, meaning.

Freshness-aware ranking

Sort and boost by publication date alongside text relevance.

Topic and author facets

Filter by section, topic, author and date range.

Widens the basket: similar, frequently together, personal.

Next read

Related and trending articles, diversified so the row isn't five versions of the same story.

Personal front page

Topic affinity from reading history with decay.

Answers questions a filter can't, citing your own catalogue and docs.

The story so far

Cited summaries from the archive for “what is known about …” questions.

Learns how items, queries and topics connect — and leads the visitor to the goal.

Entities and storylines

Will link people, organisations and events across articles into storylines readers can follow.

One visitor, from first query to goal

What the visitor does on the left, what AACSearch does on the right.

  1. 1

    Arrive

    Searches for a breaking topic.

    Today's updates rank above older background pieces.

    Instant search
  2. 2

    Catch up

    Asks what is known so far.

    A short cited summary from the archive.

    AI answers with citations
  3. 3

    Stay

    Finishes the article.

    Recommends the next related read.

    Recommendations

Your first two weeks

A typical rollout for this industry. Most teams are live on day one and spend the rest of the time measuring and tuning.

  1. Day 1

    Import the archive via RSS, sitemap or WordPress.

  2. Day 2

    Boost by publication date for news queries.

  3. Week 1

    Add related-article rows under each story.

  4. Week 2

    Measure pages per session.

What to measure

The numbers to judge a rollout by. Compare them with an A/B split or a before/after window on the same traffic.

Pages per session
The effect of recommendations.
Search exit rate
Searches that end the visit.
Subscriber conversion from search
For paywalled publishers.

Connects to

  • Sitemap crawl
  • RSS / JSON
  • WordPress
  • REST API

Capabilities used

Coming next

Search for Media & publishing, live this week

Connect a feed or a site, embed one script, and measure the difference on your own traffic.