Know exactly which questions deserve a page, which deserve a refresh, and which deserve neither.

Search volume alone doesn't tell you what to write. Keyword & Topic Research maps query demand to the audience behind it, so every topic decision comes with intent, business relevance, and a clear next step attached.

You walk away with clusters that carry an intent, a business-fit score, and a next action, checked against what you have already published.

Map demand into topics

Some of the 500+ brands we've worked with

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Each step turns raw query data into a decision a planning meeting can use.

Pull the raw demand signal
Query datasets, result-page patterns, and whatever first-party search and site data you already have, gathered before any judgment gets applied.
A raw demand dataset with sources attached.
AI assist
Agents can pull demand data from several sources and normalise it into one comparable set. Which sources are reliable for your market, and where the data thins out, is stated rather than assumed.
Human gate
A strategist checks that the pulled dataset's sources are reliable before anyone builds on top of it.
Check it against what already exists
Every query gets checked against your current URL inventory, so we aren't recommending a page for something you already rank for.
A demand set filtered against existing coverage.
AI assist
Large keyword sets can be matched against the URL inventory quickly. A strategist steps in where wording overlaps but intent may not.
Human gate
A strategist resolves any ambiguous overlap between a query and an existing page, since a surface-level match can still be a false one.
Cluster by real intent
Queries that look similar but mean different things get split apart. Queries that mean the same thing get grouped, even if the wording differs.
Intent-based clusters.
AI assist
Agents can match queries against your URL inventory and flag where a page already competes for the term. Whether the existing page should be improved or replaced is a strategist's call.
Human gate
A strategist decides where an ambiguous query actually splits, since automatic clustering can still miss the difference.
Score for business fit and confidence
We score each cluster for business relevance and evidence confidence alongside demand. Volume alone would rank the list badly.
A prioritized topic map with confidence and business-fit scores.
AI assist
Agents can score each cluster against the agreed business-fit criteria and show its confidence. Volume says nothing on its own about whether a topic deserves a page, so the score is an input to a decision.
Human gate
A strategist weighs the business relevance the data can't see on its own before a cluster earns a high score.
Assign the next action
Every cluster gets a clear call: new page, section addition, refresh, or leave alone. That turns raw data into a decision the roadmap can use.
A demand map with an explicit next action per cluster.
AI assist
Agents can attach a proposed next action to each cluster and list the ones with weak evidence. Ambiguous clusters stay marked for review rather than being pushed onto the roadmap.
Human gate
Before a cluster reaches the roadmap, a strategist approves its final call: new page, refresh, or leave alone.

Agents can sort the query set. A strategist sets the priority.

Agents are useful for the heavy sorting: matching thousands of queries to your URL inventory and drafting an initial set of clusters. Search volume still says nothing on its own about whether a topic deserves a page. A Zeo strategist reads the intent, weighs the business relevance, and makes the final call. Weak or ambiguous evidence remains marked for review on the roadmap.

A ranked list only helps if the reasoning behind it survives contact with a skeptical stakeholder.

Prioritized keyword and topic demand map
Clusters, intent, audience job, and a next action for each, organized so a planning meeting can use it directly.
Evidence and source register
Where every number came from, so a skeptical stakeholder can check it themselves.
Page-boundary notes
Which queries share a page and which need their own, so the next brief does not accidentally create two pages competing for the same result.

We call it done when: every cluster has an intent, a business-fit score, and a next action, and the map has been checked against what is already published.

This decides what's worth writing about. It doesn't write the brief or the page itself.

A good fit when

A large keyword export exists, but nobody can tell which queries deserve a new page, a refresh, or no action at all.
SEO and content keep disagreeing about priority because nobody has checked demand against what already exists.
Every roadmap topic needs a reason a skeptical stakeholder can inspect, but the current priority is backed only by search-volume rows.

Better handled as other work when

Search volume is being treated as proof of audience demand, so high-volume queries move forward without anyone checking the intent behind them.
The plan gives every keyword its own page. Several phrases share one intent while ambiguous queries split into different needs, so the URL boundaries will be wrong.
A specific ranking has to be promised before the topic is approved, although the page, site, and competition still determine the eventual position.

Some of these consultants write the pages themselves. Others plan distribution or check how a piece performs once it's live, so the team below covers more than one stage of the work.

Content we've produced on this topic

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Ahrefs
the primary volume pull: thousands of keyword ideas, clustered, in one query
Google Keyword Planner
a second, Google-sourced volume number to check the primary pull against
Keyword Cupid
clusters by real search intent, using which URL Google already ranks
Airtable
the demand map itself: cluster, score, and assigned action in one table
Google Search Console
checks a candidate topic against what the site already ranks for