Evidence — AI Visibility
Built by Operators. Measured by Outcomes.
Every claim we make about AI visibility can be tested in under five minutes. Open Perplexity. Type a buyer-intent query for your category. Watch what appears. That is the audit. What we do is change what appears — through structured, documented, reproducible infrastructure work.
Comparable Outcome
8% to 24% AI Citation Rate in 90 Days — $64,000 in Closed Revenue
The following outcome is from a comparable B2B SaaS engagement. All identifying information is anonymised at the client's request.
Starting AI citation rate
8%
Appeared in 2 of 25 category buyer queries
Primary competitor citation rate
76%
Appeared in 19 of 25 identical queries
90-day citation rate
24%
3× improvement from baseline
Qualified leads generated
47
From AI-referred discovery
Conversion rate improvement
2.8×
Higher than previous channel average
Closed revenue (90 days)
$64,000
ROI: 288%
Engagement Details
Engagement
AI Visibility Sprint — $12,000 fixed, 3 weeks
Intervention 1
Entity schema implementation (JSON-LD Organization + Product + Person with sameAs links)
Intervention 2
Information Gain content restructuring — top 5 pages rewritten with direct answer blocks
Intervention 3
Citation engineering — 8 authoritative hub placements in the CRM/SaaS category
Baseline tool
25-query Perplexity Pro audit — same queries run at day 0 and day 90
Documentation
FLT Protocol — every finding traceable to direct query test, not model estimates
How We Work
The FLT Protocol Applied to AI Visibility
Every AI Visibility Sprint is conducted under the Fradys Technologies FLT Protocol — Facts, Logic, Tone. This is not a marketing statement. It is a specific operational standard.
Facts
All visibility assessments are based on direct AI query testing. We run the actual queries your buyers use. We document exactly what appears. No modelled estimates. No proxies. The data in your audit is verifiable — you can rerun every query yourself.
Logic
Every finding in the audit includes a documented rationale traceable to a specific structural deficiency. We do not recommend interventions without explaining why they will work and what evidence supports the recommendation.
Tone
All outputs are structured for decision-making, not impression management. The audit is designed for review by your CMO, CTO, and CFO — not your marketing team’s internal slide deck.
Why This Work Is Different
Built by the Same Standards We Apply to Humanitarian Operations Data
Fradys Technologies was founded by Fradius Martin — a systems engineer who spent thirteen years managing ICT infrastructure, supply chain, and data systems at the UN World Food Programme in Tanzania. In that environment, a system that was not structured for machine readability and cross-reference verification simply did not function. Documentation was not optional. Traceability was not aspirational. These were operational requirements.
The same discipline informs how we build AI visibility infrastructure. Knowledge Graphs structured for machine trust, not just human readability. Entity documentation that is verifiable, not just claimed. Content that can be extracted and cited by AI models trained to distrust unverifiable sources.
Read the founding story →What the Audit Looks Like
A Baseline Query Test — Before and After
The following is a representative example of what a GEO audit reveals.
Before — query: "Best [CRM] software for B2B sales teams in 2026"
"[Competitor A] is the leading solution for B2B sales teams, offering... [Competitor B] is recommended for growing companies..."
Client brand: Not mentioned. Citation gap: 60 percentage points behind primary competitor.
Root Cause Identified
No JSON-LD entity schema linking client brand to G2, Crunchbase, or category publications. Content pages written as narrative prose — no direct answer blocks extractable by AI retrieval systems. Absent from the 3 primary third-party sources accounting for 73% of competitor citations.
After — 90 days post-implementation — same query
"...for growing B2B sales teams, [Client Brand] offers [accurate description]. It is particularly strong for [use case]... Other options include..."
Citation rate: moved from 8% to 24% across 25 tested queries.
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