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Retainers · done-for-you · monthly
We run the full-stack engine for you, every month
AI engines and search results re-rank continuously (~40–60% of AI-cited sources change month over month (Profound)), so visibility isn't a one-time fix. Each month we run measurement, the SEO foundation, content, and off-site authority — AI-accelerated, and you review and approve every piece before it publishes.
A Full or Comprehensive Audit is the baseline a retainer reports against.
Your time required: almost none. You approve early drafts; once we’ve earned a track record on a content type, sign-off becomes yours by default — so it publishes sooner without waiting on you. How we work →
Compare the retainers
| Feature | Growth $2,950/mo | Recommended Scale $6,500/mo | Authority $12,000/mo |
|---|---|---|---|
| Measurement & reporting | |||
Share-of-modeliReproducible share-of-model measurement (adaptive per-engine sampling — at least 10 runs/engine, never below a 5-run floor — with metric-matched 95% CIs: presence → Wilson, share → bootstrap) plus SEO baselines and ongoing tracking. The proof layer. | ✓ | ✓ | ✓ |
Google AI surfaces (measured separately)iWe report Google AI Overviews as its own surface, never blended into a cross-engine score — it only fires on some searches, and a click lands in your analytics as ordinary organic traffic, so we treat measured AI referral as a floor, not a ceiling. | ✓ | ✓ | ✓ |
Weekly trackingiReproducible share-of-model measurement (adaptive per-engine sampling — at least 10 runs/engine, never below a 5-run floor — with metric-matched 95% CIs: presence → Wilson, share → bootstrap) plus SEO baselines and ongoing tracking. The proof layer. | ✓ | ✓ | ✓ |
Reporting cadenceiEvery monthly report includes the content scorecard — the AI citations each piece earned, with receipts (engine, prompt, date), the mention-vs-citation split, and refresh wins. Win alerts and engine-drift advisories (with before/after data) arrive as they happen, between reports. | monthly | monthly + mid-month alert | biweekly |
AI engines trackediEvery AI engine we measure — on every tier, no engine gated. We never cut Claude on the cheaper tier the way engine-gating competitors do (Claude is ~18.5% of measured B2B referral traffic; leaving it out gives you a wrong answer, not a smaller one). We measure the engines that move B2B buying and tell you plainly which ones are wasting your attention (e.g. Grok's measured B2B referral share is ~0%). | All | All | All |
Buyer prompts trackediThe AI MEASUREMENT — how many real buyer prompts (the questions your buyers actually ask AI) we measure your share-of-model against every cycle, across every engine. This is the AEO/GEO half and the real tier lever, DISTINCT from classic keyword tracking (rank_tracker separates them in code, and so does this page). | 30 | 75 | 150 |
Buyer categories ownediHow many buyer categories / use-cases we work to get you recommended in. Growth keeps one focused; Authority owns six — a defined, buyable scope, never an unbounded 'full category ownership' promise. | 1 | 3 | 6 |
Markets trackediHow many distinct markets or locales (geography / language) we track and optimize for. Growth focuses one; higher tiers widen coverage. | 1 | 3 | 5 |
| SEO foundation | |||
Optimizations / moione scoped, measured AEO/SEO change we make for you: e.g. an answer-first rewrite of a key section, a schema / entity fix, an internal-linking or metadata pass, or a page consolidated for a buyer prompt. We also refresh published content on researched decay cadences — substantive updates, not date bumps. | 2–4 | 2–4 | 2–4 |
Managed on-pageihands-on on-page work on your priority pages: titles, meta, headings, answer-first structure, internal links, and schema / entity markup - done for you, not just recommended. | — | ✓ | ✓ |
Schema & entity upkeepiwe keep your structured data and entity signals current: Organization / Product / FAQ / Article schema, and the sameAs identity links (site, profiles, directories) the engines use to resolve who you are. | ✓ | ✓ | ✓ |
Keywords trackediCLASSIC Google/Bing SERP tracking — how many keywords we track weekly organic positions for (striking-distance + drop intelligence + brand-SERP watch), folded into the retainer at no extra charge (W238-adopted, W212 engine). This is the SEO half, a DIFFERENT product from AI buyer-prompt tracking: volumes/difficulty are third-party estimates (DataForSEO); positions are dated observations, reported alongside and never blended into your AI share-of-model. | 200 | 600 | 1,500 |
| Content & production | |||
Content pieces / moiblog post / website page — one answer-first, schema-marked article or page published on your own domain (text + schema, not video) | 1 | 4 | 6-8 |
Managed social packi12 original posts/mo, each written from scratch and platform-tuned (text + a generated image where the platform rewards one), posted to your chosen social accounts. Carousels and video are NOT included. Typical platforms: LinkedIn, X, Reddit, Dev.to, Hashnode, Threads, Bluesky, Mastodon, Pinterest, Quora, Facebook, Instagram (feed posts). | — | — | 12 posts/mo |
VideoiVideo is never part of the social pack or a content piece — it is its own Video pack add-on ($2,500/mo), or standalone medium/long/short video SKUs. | add-on | add-on | add-on |
| Off-site authority | |||
Managed communityia managed, on-brand presence in the communities your buyers and the engines read - steady, you-approved participation on the platforms that matter for your category. | — | ✓ | ✓ |
Digital PRiManaged journalist/publication pitching for earned coverage, expert quotes, and authority links. Placements never guaranteed. | — | best-effort monthly | managed |
Active backlinksiYour link/domain-authority profile vs named competitors + a winnable-link target list. | — | — | ✓ |
Directory listingsiLayer 1: entity consistency across 30+ directories (identical NAP + sameAs) for discovery, brand-SERP coverage and referral — never as a ranking or AI-citation promise (no evidence links directories to AI citation). Layer 2: agency-side registries, claimed review profiles, your Index entry, a drift re-check, and the before/after. Directories are a one-time submission, not an ongoing service: every retainer includes a one-time core submission at onboarding, and the paid tiers (Boost 30+, Growth 60+, Authority 100+) are a bigger one-time push on top. | core (onboarding) | boost (30+) | growth (60+) |
| Logistics | |||
OnboardingiWe begin within 1 business day of onboarding + payment. Kickoff delivers a measured baseline snapshot within 72 hours — value from your own data on day one. | within 2 business days | within 2 business days | within 2 business days |
| Content delivery | ~3 business days | ~3 business days | ~3 business days |
Audit fee creditediStart a Growth, Scale or Authority retainer within 30 days of your audit and we credit the full audit fee to your first retainer invoice. | credited | credited | credited |
ContractiEither party may cancel a retainer at any time, effective at the end of the current paid month. | no lock-in | no lock-in | no lock-in |
| Get started | Choose Growth | Choose Scale | Choose Authority |
Not sure which tier? The 60-second fit-finder recommends one by your goal and budget — Scale is the common pick for multi-product or competitive categories. Hover any in the table for exactly what a row means. All delivery timelines →
What each tier covers — the scope, at a glance
Coverage is scope — the layers and tiers a plan actively works, not an outcome we promise. Every plan reports the full measurement stack; higher tiers execute across more of your citable footprint.
| Scope | Growth | Scale | Authority |
|---|---|---|---|
| Service layers engaged of the 5 — measurement, AEO, SEO, content, off-site authority | 4 of 5 | 5 of 5 | 5 of 5 |
| Citation-control tiers worked of the 4 AI cites from — owned, listings, reviews & social, earned | 1 of 4 | 3 of 4 | 4 of 4 |
| Monthly measurement report never gated by tier | all 6 layers | all 6 layers | all 6 layers |
Scope only — see the full row-by-row tier comparison above. The measurement report is the same six layers on every plan; the tiers differ in how much we execute, not in what we measure.
Included in every retainer — already in scope
These aren't add-ons. Every retainer runs the full intelligence loop (how we analyze): we measure, attribute every piece to the AI citations it earns, refresh on cadence, flag wins and drift as they happen, and prove it back to you — with your approval on everything before it publishes.
We deliver a measured baseline snapshot within 72 hours of kickoff, and reply to client requests rapidly and asynchronously.
Every piece we publish is attributed to the AI citations it earns — with receipts: the engine, the prompt, and the date it was cited.
We refresh published content on researched decay cadences — a systematic, substantive-update schedule, not a publish-and-forget.
We flag citation wins the same cycle they happen and advise on engine drift with before/after data — proactively, not on request.
Every piece runs through calibrated quality gates and lands in your approval queue before anything publishes — AI speed, human-approved.
Research evidence
We publish what we measured about whether each tier works, including when it didn't — see the full research programme.
| Study | Status | Finding |
|---|---|---|
| AI Answer Volatility Index | running | measured: 5 of 5 engines have a no-intervention band (anthropic +/-14.0pt, gemini +/-13.0pt, grok +/-8.0pt, openai +/-13.0pt, perplexity +/-27.0pt) |
| Cross-Engine Disagreement Index | proposed | not yet measured — proposed (pre-registration pending) |
| Newsletter/email presence vs AI citation | proposed | not yet measured — proposed (pre-registration pending) |
| Schema markup on NEVER-cited pages | proposed | not yet measured — proposed (pre-registration pending) |
| Content-freshness refresh test (causal, matched control) | proposed | not yet measured — proposed (pre-registration pending) |
| Developer-doc / technical-content citation study | proposed | not yet measured — proposed (pre-registration pending) |
| The negative-results ledger (what didn't work) | running | 2 pre-registered intervention(s): 0 moved, 0 no measurable change, 0 moved the wrong way, 2 unverified |
| AI citation -> funnel/revenue | proposed | not yet measured — proposed (pre-registration pending) |
| Own-brand Reddit test (disclosed participation only) | proposed | not yet measured — proposed (pre-registration pending) |
| Multi-baseline SCED across >=3 clients | proposed | not yet measured — proposed (pre-registration pending) |
| State of AI Visibility (the annual flagship) | proposed | not yet measured — proposed (pre-registration pending) |
Disconfirming evidence: at least one study above found this SKU's premise does not hold. See the study for the full method and limitations before you decide how we act on it.
| Study | Status | Finding |
|---|---|---|
| AI Answer Volatility Index | running | measured: 5 of 5 engines have a no-intervention band (anthropic +/-14.0pt, gemini +/-13.0pt, grok +/-8.0pt, openai +/-13.0pt, perplexity +/-27.0pt) |
| Cross-Engine Disagreement Index | proposed | not yet measured — proposed (pre-registration pending) |
| Newsletter/email presence vs AI citation | proposed | not yet measured — proposed (pre-registration pending) |
| Content-freshness refresh test (causal, matched control) | proposed | not yet measured — proposed (pre-registration pending) |
| Developer-doc / technical-content citation study | proposed | not yet measured — proposed (pre-registration pending) |
| The negative-results ledger (what didn't work) | running | 2 pre-registered intervention(s): 0 moved, 0 no measurable change, 0 moved the wrong way, 2 unverified |
| AI citation -> funnel/revenue | proposed | not yet measured — proposed (pre-registration pending) |
| Own-brand Reddit test (disclosed participation only) | proposed | not yet measured — proposed (pre-registration pending) |
| PR placement -> citation lift | proposed | not yet measured — proposed (pre-registration pending) |
| Multi-baseline SCED across >=3 clients | proposed | not yet measured — proposed (pre-registration pending) |
| State of AI Visibility (the annual flagship) | proposed | not yet measured — proposed (pre-registration pending) |
Disconfirming evidence: at least one study above found this SKU's premise does not hold. See the study for the full method and limitations before you decide how we act on it.
| Study | Status | Finding |
|---|---|---|
| AI Answer Volatility Index | running | measured: 5 of 5 engines have a no-intervention band (anthropic +/-14.0pt, gemini +/-13.0pt, grok +/-8.0pt, openai +/-13.0pt, perplexity +/-27.0pt) |
| Cross-Engine Disagreement Index | proposed | not yet measured — proposed (pre-registration pending) |
| Newsletter/email presence vs AI citation | proposed | not yet measured — proposed (pre-registration pending) |
| Content-freshness refresh test (causal, matched control) | proposed | not yet measured — proposed (pre-registration pending) |
| Developer-doc / technical-content citation study | proposed | not yet measured — proposed (pre-registration pending) |
| The negative-results ledger (what didn't work) | running | 2 pre-registered intervention(s): 0 moved, 0 no measurable change, 0 moved the wrong way, 2 unverified |
| AI citation -> funnel/revenue | proposed | not yet measured — proposed (pre-registration pending) |
| Own-brand Reddit test (disclosed participation only) | proposed | not yet measured — proposed (pre-registration pending) |
| PR placement -> citation lift | proposed | not yet measured — proposed (pre-registration pending) |
| Multi-baseline SCED across >=3 clients | proposed | not yet measured — proposed (pre-registration pending) |
| State of AI Visibility (the annual flagship) | proposed | not yet measured — proposed (pre-registration pending) |
Disconfirming evidence: at least one study above found this SKU's premise does not hold. See the study for the full method and limitations before you decide how we act on it.
Common questions
Is there a contract or lock-in?
Which retainer is right for me?
Do I need an audit before a retainer?
Can I add video, PR or directories?
Make it go further (opt-in)
Prepay & save
Prepay a retainer and save 10% for 3 months, 15% for 6, or 20% for 12. No lock-in beyond the term you choose.
Add a capability
Layer on a video pack, directory authority, digital PR or extra content only where it moves your share-of-model — never bundled in by default.
Scale is the common pick
Multi-product or competitive category? Scale runs the full engine every month — no lock-in, cancel anytime, and your audit fee is credited.
Choose Scale ($6,500/mo)Not sure which tier? Find your fit in 60 seconds → — 7 questions, a recommendation with the add-ons that fit and an estimated price.
Why Clear Cited
The five levers behind share of model
We get you recommended across the 5 AI engines — ChatGPT, Perplexity, Claude, Gemini, and Grok — plus Google's AI surfaces (AI Overviews & AI Mode), measured separately — and here is the honest system beneath it: what you are actually paying for, and the real limit on each.
Every engine and surface your buyers actually use
the AI engines your buyers use, plus Google's AI surfaces and Bing's Copilot answers — each measured separately, never summed into one score
The roster is five AI engines plus two answer surfaces — AI Overviews and Microsoft Copilot — reported separately and never summed into an engine count. The five engines are measured today. Both surfaces are positioned, not measured: an engine is one foundation-model family we query directly, a surface is a product that renders an answer, and a surface never becomes an engine by being added up. Outside the labelled separate-surface panel we name the Google surface plainly as AI Overviews, because naming it beside the engines would read as an engine claim.
A method you can reproduce
pre-registered prompt sets with published hashes, run counts and a confidence interval on every figure, and null results published on the same terms as positive ones - as standing practice, not a one-off study
Confidence intervals are reported honestly wide and named per product: presence uses a Wilson interval in both products; share-of-model uses a percentile bootstrap in the paid audit and a Wilson interval in the public Index, whose pooled denominator is clustered - so those Index intervals are narrower than a cluster-corrected estimate would give, and we say so. We never narrow a CI to look more certain than the data is.
All four tiers AI cites from
we work all four tiers AI cites from — not just the fraction of your presence you directly own
This is SCOPE — the tiers we work, not an outcome we promise. The per-tier percentages are illustrative (Yext-sourced) and the earned tier (news & forums) is best-effort, never guaranteed.
Almost none of your time
your time required is almost none — sign-off graduates to become yours by default once we've earned a track record, with continued sampling
A human approves every client deliverable until a clean track record is earned; graduation is opt-in, default-off, and reversible — client-facing and published content never reaches unattended auto (AI speed, human-approved).
A system that compounds
the system learns which of your pages get cited and compounds that signal every cycle
Honest-empty until measured — attribution shows the real citations a piece earns, with the engine, prompt and date, only once they are actually earned; no projected compounding curve.
The system compounds — see how it learns which of your pages get cited → · the four tiers of citations we work →
The transparency wedge
A method you can reproduce
pre-registered prompt sets with published hashes, run counts and a confidence interval on every figure, and null results published on the same terms as positive ones - as standing practice, not a one-off study
at least 10 runs per engine (12 by default), spent adaptively — more on high-variance engines, fewer on stable ones, never below a 5-run floor Every presence figure carries a Wilson 95% confidence interval. Share of model carries a percentile bootstrap 95% confidence interval in the paid audit, and a Wilson 95% confidence interval in the public AI Visibility Index — the method matched to the metric and named per product, both computed from the actual runs.
Confidence intervals are reported honestly wide and named per product: presence uses a Wilson interval in both products; share-of-model uses a percentile bootstrap in the paid audit and a Wilson interval in the public Index, whose pooled denominator is clustered - so those Index intervals are narrower than a cluster-corrected estimate would give, and we say so. We never narrow a CI to look more certain than the data is.
We publish our run counts, our uncertainty and our pre-registered prompt sets. Across the 8 providers we checked on 2026-08-02: run counts 4 of 8 verified; 4 not verified; uncertainty on published figures 2 of 8 verified; 6 not verified; the prompt set published with the results 0 of 8 verified; 8 not verified. The full comparison, with each vendor’s own wording →
That is the wedge: a number you can re-run and get back. See the full method →
Hold us to it, and hold the others to it too — the 12 questions to ask anyone selling AI visibility, each one answered here with a link.