We lay the method out in full.
The barrier is not knowing it — it is doing it.
Citely is a GKM Research product. Everything you ask in the chat box runs on the nine sections below. Pick one on the left.
Buyers look for you at three stages
"How do I solve this problem"
Measured by: informational interception rate
"What are the options, and who is better"
Measured by: comparative interception rate
"Specifications, price, how to buy"
Measured by: transactional interception rate
Most company sites are empty at all three stages and do not know it, because nobody has ever measured it for them. We measure first, fill whichever stage is missing, and every term we go after can be traced back to a reason.
From the inside out — the outermost layer is what an AI wants to cite
- L1 Brand defensive position
- L2 Product the baseline
- L3 Need where growth comes from
- L4 Context the long-tail goldmine
- L5 Identity who is searching
- L6 AI citation intent what an AI wants to cite
Most services get as far as L1 and L2. L6 can only be built from week-by-week engine measurements, which is why almost nobody in the field does it.
A topic pillar plus subtopic clusters, so you become the one source
- Topic pillar One long, complete piece answering the topic on one page
- Subtopic clusters One piece per follow-up question, catching the long tail
- Linked to each other Weight concentrated, structure an AI can read
- Kept current A freshness signal
- Cross-engine variants One version per engine's preference
An AI prefers a single authority it can build the whole answer from. A semantic network is exactly what makes you that source. Scattered articles fight alone; a network is what gets recognised as authority.
An AI cites passages, not articles
Sample size and time range written into the passage, giving the AI credible wording it can borrow
Resolving the conflict on the AI's behalf
"As mentioned above" is poison to an AI
What, why and how, finished in one passage
E-E-A-T: named authors, first-hand data, an institutional fact base. These experience and authority signals are material engineered into each passage, not page decoration.
Every passage has to pass a machine check before it publishes. This is not a writing style — it is engineering discipline applied passage by passage.
However good the content, if an AI cannot get in it does not exist
Internal linking and weight structure steer crawler attention to the pages that should be seen; important pages are always within three clicks.
Each major AI engine's crawler allowed and verified one by one, with new content pushed actively so it is read on the day it publishes.
Structured markup gives every page its own identity card.
So every return visit tells the engine this site is alive.
To an AI most websites are fog. We build yours into a city with gravity and signposts.
Every one of them has a quantified threshold
Each front is held by a dedicated agent, monitored continuously from launch day, with anything abnormal handled within 72 hours. This is the foundation content performance sits on, and most of the field skips it.
More than 100 AI agents, each doing exactly one thing
| Function | Agents | What it is responsible for |
|---|---|---|
| Onboarding and situation assessment | 8 | Clarifying the commercial goal and identifying which of six market situations applies |
| Content production line | 17 | Data preparation, intent analysis, strategy, drafting, multiple reviews, deployment and after-action review — seventeen stations |
| Technical foundations | 10 | Each holding one quantified threshold |
| External data intake | 8 | Support conversations, CRM, e-commerce, social sentiment and AI visibility — cleaned and de-identified before storage |
| Quality and governance | 12 | Quality gates, data audits, adversarial stress-testing |
| Intelligence and learning | 27 | Thirteen knowledge institutes, cross-client intelligence distribution, hypothesis validation and forecast calibration |
The table lists the standing establishment of 82; further sub-modules are assigned by industry and situation, bringing the whole system to more than 100 AI agents working together.
One responsibility per role, one quantified metric, one handover contract. Tools can be copied; this structure cannot.
The system writes its own textbook daily, and gets more accurate the longer it serves you
- Evidence grading Every piece of market intelligence is graded on source reliability × credibility; weak evidence is not allowed to become a rule
- Hypothesis validation Every strategy is a falsifiable hypothesis card, checked against reality at six and twelve weeks
- Forecast ledger and calibration Every forecast carries a probability and is recorded; calibrated quarterly
- Cross-client knowledge abstraction A lesson from one client becomes a general rule only after at least two clients confirm it; client data stays isolated from client data
- Convergence measurement Client edits per round keep falling until there is nothing left to change, with a ledger to show it
This is compounding you cannot buy with time: every article's error becomes the next article's precision.
Forecast, measure, reconcile, calibrate — then back to forecast
- Engine preferences on file For one topic, each engine receives the version it likes to cite
- The domain trust flywheel A domain that has been cited is cited more easily next time; the earlier you start, the greater the compounding
Elsewhere in the field the hundredth article is as accurate as the first. In this system the hundredth carries the reconciled results of the ninety-nine before it.
What we promise every client
In a field full of inflated numbers and lock-in tactics, we choose to be plain. These four are written into how Citely behaves, too.
Every number in a report can be checked. We would rather give you a conservative but true estimate than a handsome promise we cannot keep.
The Trial plan carries no measurement data, so no performance figure appears on that tier. The system would rather tell you "measurement is being set up" than invent one.
Clients who have not authorised publication stay anonymous, and results not yet measured stay blank. We do not use fake data or borrowed names to vouch for ourselves.
Add-ons are always quoted first and only registered as a request once you agree; submitting one costs nothing at that moment. Every charge traces back to a request you have seen.
You have read the method. Now look at your own numbers.
Thirty minutes, measuring word by word whether AI cites you today.