Home / Services / LLM & AI Search Visibility
SHIELD TIER · Service 03 · AI Visibility Layer

Get cited by AI search.

A continuous engagement to engineer your site for retrieval by ChatGPT, Perplexity, Claude, Gemini, and Google's AI Overviews. Entity-clean infrastructure, E-A-V triple production, schema deployment, citation diagnostics, and structural reinforcement — designed to make your entity the one AI systems retrieve from on your category's queries.

TierShield
DurationContinuous
DeliverableCitation infrastructure
PricingCustom scoped
01 / The Deliverable

What's actually inside the engagement.

Six continuous workstreams run across the engagement. Each one operates on a defined cadence, with measurable AI retrieval outcomes. Not "we'll write FAQs" — entity-level engineering of the structural properties that make AI systems retrieve from your site over competitors.

Deliverable 01
E

E-A-V Triple Production

Structured Entity-Attribute-Value triples deployed across priority pages. The relational format LLMs retrieve from. Each triple validated against schema and tied to a buyer-stage query.

Deliverable 02
S

Schema Deployment

Article, FAQ, Breadcrumb, HowTo, Organization, and Product schema types deployed at scale. Tied to entity recognition, not generic SEO hygiene. Validated against Google's structured data testing tool monthly.

Deliverable 03
Q

Query Diagnostics

Monthly testing across 4 LLM surfaces (GPT, Perplexity, Claude, Gemini) on 30–60 representative queries. Citation-readiness scored per cluster. Failures root-caused.

Deliverable 04
R

Retrieval Reinforcement

Iterative content adjustments based on citation diagnostic findings. Pages that fail retrieval get restructured for E-A-V clarity. Closes the AI visibility gap systematically.

Deliverable 05
I

Information Gain Engineering

Ensures published content contributes net-new attributes beyond the SERP agreement area. Pages with novel attribute coverage get retrieved disproportionately by LLMs.

Deliverable 06
M

Monthly Visibility Reporting

Cluster-by-cluster AI retrieval scorecard. Which queries earned citations, which didn't, why — with specific remediation actions for the next cycle.

03 / The Fit

Who this fits.

This engagement produces real outcomes for businesses ready for the methodology — and frustrates businesses looking for quick fixes. The fit map below is honest. If your business is in the right column, an audit-first approach or a different service is the better starting point.

Where it fits

This engagement works if you...

  • Operate in a category where buyers increasingly use AI search for discovery
  • Have an existing topical architecture (or are building one alongside)
  • Want measurable AI citation outcomes per cluster, per cycle
  • Recognize AI visibility as structural, not a one-time fix
  • Want to be the entity AI retrieves from on your category's queries
  • Are willing to commit to multi-month cycles for compounding results
  • Need monthly visibility reporting your team or stakeholders can act on
Where it doesn't

This isn't right if you...

  • Have no Central Entity defined yet — start with architecture first
  • Want a one-time "AI optimization" fix — this is continuous engineering
  • Believe AI citations don't matter for your category — fair, but we're wrong fit
  • Need traditional Google ranking improvements as the primary outcome
  • Don't have publishing capacity to support cluster reinforcement cycles
  • Are pre-launch with no published content — architecture before visibility
04 / The Timeline

How the engagement runs.

The engagement runs in continuous monthly cycles after a 4-week onboarding phase. Most clients see measurable AI citation gains by month 03 and category-defining retrieval by month 06–09.

Phase
Activity
Phase 01
Weeks 1–4
Onboarding & Baseline
Citation diagnostic across 30–60 representative queries · 4 LLM surfaces · root cause analysis · E-A-V triple production specification · schema deployment plan · cluster prioritization. Foundation for all monthly cycles.
Phase 02
Month 02+
Continuous Production
E-A-V triples deployed in priority order · schema rolled out · failed-retrieval pages restructured · Information Gain engineering across new content · monthly query testing cycle.
Phase 03
Continuous
Reporting & Reinforcement
Monthly visibility scorecard · cluster-by-cluster citation tracking · remediation specifications for next cycle · trending analysis. Cycle compounds quarter over quarter.
Phase 04
Quarterly
Strategic Review
Quarterly review of visibility progression · priorities re-sequenced based on category shifts · LLM model changes integrated into testing protocol · cycle continues.
05 / Pricing

Pricing & engagement model.

Continuous engagement priced as monthly retainer + onboarding fee. Final pricing depends on cluster count, query test surface, vertical regulatory layer, and reporting cadence. All pricing locked at kickoff with monthly cycles billed in advance.

LLM & AI Search Visibility

Pricing Custom

Onboarding fee at kickoff (Phase 01) · monthly retainer thereafter (Phase 02+). Minimum 6-month engagement to allow citation outcomes to compound. Cycle review after month 06 for scope continuation.

Book Strategy Call
What's Included
  • All 6 continuous workstreams
  • Onboarding citation diagnostic (30–60 queries)
  • E-A-V triple production at scale
  • Schema deployment across 6 types
  • Monthly query testing across 4 LLM surfaces
  • Retrieval reinforcement on failed pages
  • Information Gain engineering on new content
  • Monthly visibility scorecard report
  • Quarterly strategic review
  • Slack/email channel for cycle questions
06 / Questions

What buyers ask before committing.

The questions below come up most often during scoping calls for this engagement.

Measurable improvements typically appear by month 03 as the first round of E-A-V triples and schema deploy. Category-defining retrieval (where your entity becomes the one AI systems prefer to cite) typically takes 6–9 months of continuous engagement.

This is structural work, not switch-flipping. Authority compounds.

Standard test panel: ChatGPT (GPT-4 and beyond), Perplexity, Claude, and Gemini. We also spot-check Google's AI Overviews where they appear for category queries. We expand the panel as new retrieval surfaces emerge.

Strongly recommended. AI visibility engineering operates on top of architectural foundation. If your Central Entity is fragmented or your topical map is incomplete, citation engineering compounds slowly.

If you don't have architecture yet, we typically scope architecture + visibility together as a sequenced engagement.

No. Schema is one of 6 workstreams. The bigger leverage is in E-A-V triple production, Information Gain engineering, and retrieval reinforcement — which are content-layer disciplines, not technical-layer ones. Schema alone gets you partway; the rest is structural content engineering.

Cluster-by-cluster AI citation tracking. Each cluster has a representative query set tested monthly across 4 surfaces. Success = sustained citation rates increasing quarter over quarter. Vanity metrics like impressions or rankings are not the success measure — verified retrievals are.

6 months. AI citation outcomes are structural and compound over cycles. A 1–3 month engagement produces work without producing the outcomes the work is engineered for. We've declined shorter engagements where the math didn't favor the client.

08 / The Next Step

Become the entity AI retrieves from.

AI search is reshaping how buyers discover. The agencies that engineered for it early are compounding citations. The ones that didn't are watching their organic traffic decline. Get on the right side of the shift.