VERIFIED OUTCOMES · LIVE DEPLOYMENT CASE LAB

What AI presence management produces in practice.

These case studies are drawn from live client engagements. Client names are anonymised by agreement. Outcomes are verified against our monthly simulation data and client-reported pipeline metrics.

E-E-A-T INTEGRITY RECORD
MethodologiesEntity & RAG Mapping
VerificationMonthly Run Protocol
Anonymity LimitNDA Strict-Compliant
Quantifiable proof, validated timeline structures, transparent metrics.
DEPLOYMENT TIME: 14 DAYS
IDENTIFIERCASE STUDY 01 / PROFESSIONAL SERVICES
SERVICE REGIONUK Midlands and North
PRE-ENGAGEMENT0 / 8 Citation Frequency
POST-ENGAGEMENT75%+ Cite Index
Entity Graph Diagnosis

“Zero schema markup blocks meant every retrieval machine queried client as completely absent entity.”

CORE OBJECTIVE

From AI-invisible to cited authority in 14 days.

CLIENT CONTEXT & PROFILE

Client Profile: A highly regarded mid-market management consultancy with 12 years of practice, an active thought leadership programme, and a consistent SEO investment. Strong human-facing brand presence.

THE RETRIEVAL DEFICIT (THE PROBLEM)

A routine AI Visibility Audit revealed the firm was achieving a citation score of 0 out of 8 across high-intent regional procurement queries on ChatGPT Search, Gemini, and Perplexity.

Google's Schema Validator returned a completely blank entity panel — no Organisation data, no service schema, no LocalBusiness mapping. Despite 12 years of operation, the firm was an unverified entity to every AI retrieval system querying their category.

Three direct competitors — all smaller by revenue — were appearing as named citations in every simulated query.

SYSTEMS INTERVENTION SEPARATED BY PHASE

Phase 1 (Days 1–5): Schema deployment
  • ✓Constructed and validated a complete Organisation JSON-LD graph mapping the firm's service lines, founding date, key personnel, geographic service area, and industry credentials
  • ✓Resolved 14 critical schema errors identified in the audit
  • ✓Deployed LocalBusiness entity with sameAs chains to Google Business Profile, Companies House, and LinkedIn
Phase 2 (Days 6–14): Content correction
  • ✓Restructured three primary service pages into atomic definition blocks with extraction-eligible formatting
  • ✓Deployed six FAQ modules across core service pages using FAQPage schema markup
  • ✓Implemented structured comparison table on primary capabilities page

VERIFIED MEASURABLE OUTCOMES

DAY 14 POST-DEPLOYMENT:
  • First citation appeared in Perplexity in response to a regional management consultancy query
  • ChatGPT Search footnote citation confirmed within 17 days
  • Gemini entity recognition confirmed within 21 days
DAY 60 POST-DEPLOYMENT:
  • Firm cited in 6 of 8 monitored query variants across all platforms
  • Two of three previously dominant competitor citations displaced in Perplexity's regional ranking
The Key Efficiency Takeaway: No content redesign. No new service pages. No paid promotion. Infrastructure correction alone produced category citation.
DEFLECTION: ACTIVE COMPOUND
IDENTIFIERCASE STUDY 02 / DESIGN & ENGINEERING
PRIMARY RISKCompetitor SEO Displacement
PRE-RETAINER6 / 8 Citation frequency (fragile)
POST-PROGRAMME8 / 8 Perfect Citation Match
Enterprise Locker Moat

“Competitors failed to displace positions due to aggressive weekly programmatic updates mapping alternative services.”

CORE OBJECTIVE

Defending a citation position against systematic competitor displacement.

CLIENT CONTEXT & PROFILE

Client Profile: A top-tier design and manufacturing consultancy with established presence across ChatGPT Search due to a historically clean technical backend. Recognised as a leading voice in their specialist engineering category.

Primary Risk: Three well-funded competitors were actively investing in structured content programmes clearly targeting the same AI citation positions.

THE STRATEGIC RISK (THE PROBLEM)

Existing citation share was strong but fragile. The client's AI presence was built on historical entity recognition — not active content infrastructure. Competitors were publishing structured, schema-annotated case studies and comparison content at pace.

Our competitive simulation modelling projected the client would lose their primary category citation to a competitor within 4–6 months without structural reinforcement.

SYSTEMATIC PROGRAMME (THE INTERVENTION)

We activated the Enterprise Market Lockout Retainer to execute a systematic moat-building programme:

Month 1–2: Foundation deepening
  • ✓Expanded existing schema to include detailed ServiceOffering entities for each specialist practice area
  • ✓Deployed structured case study schema across historical project portfolio
  • ✓Built comprehensive Markdown comparison tables for key service categories
Month 3–6: Citation consolidation
  • ✓8 Framework Articles per month covering specialist engineering topics where competitor content was gaining traction
  • ✓4 structured case briefs per month with quantitative project outcomes formatted for Perplexity extraction
  • ✓Monthly competitive displacement monitoring with tactical content pivots based on simulation data

LONG-TERM VERIFIED COMPOUND STATS

6 MONTHS POST-ACTIVATION:
  • Client maintained primary category citation across all 4 monitored platforms
  • Identified leading competitor failed to displace any of the 6 monitored query positions despite consistent publishing activity
  • Secondary competitor fell from citation in 4 of 6 monitored queries within 90 days of the client's comparison table deployment
12-MONTH COMPOUND POSITION:
  • Client's citation frequency increased from 6/8 to 8/8 monitored queries
  • Share of Voice across monitored category queries: 73%
  • No competitor currently holds citation in more than 2 of 8 queries where client is cited