Cycles
Every capture cycle as a receipt — what it covered, expected vs actual answers, surface mix, and cycle-to-cycle diffs so you always compare like with like.
Overview
A cycle is one pass over your questions: the receipt for one batch of captures. Every manual run and every scheduled capture records exactly which questions, engines and locations it covered, how many answers were expected and how many landed, how long it took, and whether it completed. (The API and the MCP tools call the same thing a capture run.)
Why this matters: AI-visibility metrics only mean something when you know what was actually captured. If a metric moved between Tuesday and Friday, the first question is whether the cycle covered the same things — different questions, different engines, a surface that failed to capture. Cycles make that answerable instead of guessable.
Where cycles live
Cycles are a tab of Run schedule & cost: /dashboard/schedules?tab=cycles. The old /dashboard/runs address redirects there.
The table shows, per cycle: its name and whether it was scheduled or manual · when it started · its scope (full project or one group) · its status in plain words, with the reason under it when it stopped short (credits, a provider outage, a policy pause) · answers captured of expected · duration · and, for organization owners and admins, cost. Filter by scope, status and period; Load more widens the view 50 at a time.
Cycles are not numbered on this tab. The product numbers cycles from the project's first capture on the answer and trend surfaces, and that numbering is computed per answer; a cycle row is identified by its start time and its label.
Starting a cycle
- Open Run schedule & cost and choose the Cycles tab.
- Click Start a cycle — a full cycle captures all active prompts against all active engines.
- If the project has prompt groups, the button opens a menu: a full-project cycle or a single group's cycle.
- The cycle appears at the top of the list; scheduled captures create cycles the same way automatically.
To ask a subset once — one prompt, or a hand-picked set — use Run once now at the top of the page. It opens the capture form, priced as a single run.
What a cycle records
| Field | What it tells you |
|---|---|
| What it covered | The exact set of questions, engines and locations the cycle executed |
| Expected vs actual | How many answers were implied vs how many actually landed — a shortfall is visible, never silent |
| Duration & status | How long the batch took and whether it completed, partially completed, or failed — with the reason in plain words |
| Surface mix | How the cycle's answers split across AI surfaces — chat LLMs vs Google AI Overview vs AI Mode vs organic SERP |
| Cost split | The cycle's cost by source (LLM, proxy, SERP) — visible to organization owners and admins only; other members see the cycle without cost fields |
Cycle detail & diffs
Open any cycle to see its page. When two cycles covered exactly the same questions, engines and locations, the page links the comparable one and offers a side-by-side diff — per-prompt, per-engine changes between the two, covering SERP engines as well as chat LLMs.
Diffs deliberately require the same coverage: comparing a 35-prompt cycle against a 10-prompt cycle would attribute the coverage change to your brand. If no comparable cycle exists, the page says so instead of offering a misleading comparison.
Comparing periods
Period-over-period comparison lives on the home page's cycle charts: each question type's line runs on the project's own cycles, a model change cuts the line rather than being averaged through, and a change to the tracked set is marked on the cycle it landed in.
Capture provenance
Each answer inside a cycle carries a disclosure of how it was collected:
- Native browser capture — Google AI Overview and AI Mode are captured by AEO Optima's own browser over a residential connection, with the capture country named.
- Provider API — chat LLM answers come from the provider's own API.
- SERP vendor API — connector-based SERP captures name the vendor.
MCP parity
AI assistants get the same receipts through three MCP tools: list_runs, get_run (including the per-cycle surface composition), and diff_runs. Cost fields follow the same visibility rule as the UI — they are omitted for member and viewer tokens.
Related
- Snapshots — what each captured answer contains
- Run schedule & cost — putting captures on a cadence, and what one cycle costs
- SERP & AI-Surface Tracking — native Google AI Overview / AI Mode capture