AI Discovery & Revenue Engineering
Be the company AI recommends.
Dgenius measures how buyers discover your company across ChatGPT, Gemini, Perplexity and AI search—then engineers the content, data, websites and AI systems that turn discovery into revenue.
10–14 days · $6,500 fixed
AI Discovery Field
Illustrative exampleBuyer query
“What are the best enterprise identity-security platforms for a 1,000-person company?”
Recommended
- 01Company A
- 02Company B
- 03Company C
Your company
Absent
Observed 2026-08-11 09:14 UTC
Why
- Source authority
- No high-authority third-party source describes the category position
- Entity clarity
- Company is not consistently resolved to one category entity
- Comparative evidence
- No structured comparison the model can quote
Dimensions shown are the observable factors a diagnostic measures. They are not a claim about how any specific system ranks or weights inputs.
The blind spot
Absence is invisible.
Analytics can measure the people who arrive. They cannot show you the buyers whose shortlist never included you.
The shift
Discovery moved from search results to machine recommendation.
The channel that decides whether you are considered is no longer one you can see in a rank report.
- 01
Buyers ask systems, not indexes
A shortlist is now formed inside an answer. The buyer never sees the ten links that produced it, and never sees the vendors that were left out.
- 02
Answers are synthesised, not ranked
Position one is not a position. Being present in an answer depends on what a system can find, verify and reconcile about you across sources.
- 03
Recommendation is a higher bar than presence
Being indexed is not the same as being understood. Being understood is not the same as being recommended. Each step is a separate condition, and each one can fail silently.
The instrument
We measure the answer, not the ranking.
Buyer intents against the systems that answer them. Every cell is a recorded observation: prompt, model, time, state, source, competitor.
Dgenius Discovery Matrix
Demonstration data| Buyer question | ChatGPT | Gemini | Perplexity | AI Search |
|---|---|---|---|---|
| Best enterprise identity-security platforms for a 1,000-person companyShortlist formation | ||||
| How does <vendor> compare with the market leader?Direct comparison | ||||
| What does enterprise identity security typically cost?Budget qualification | ||||
| Which platform integrates best with an existing IdP?Technical fit | ||||
| Which vendors satisfy SOC 2 and ISO 27001 evidence requirements?Risk / procurement | ||||
| How hard is migrating off a legacy identity provider?Switching cost |
Select a cell to inspect the prompt, intent, model, observation time, observed state, source and competitor behind it.
The objective
Visibility is not the objective. Revenue is.
Mentions, citations and prompt screenshots are not outcomes. The only measurement that matters is whether machine-mediated discovery produces qualified pipeline you can trace.
- Visibility
- Qualified discovery
- Referral
- Conversion
- Pipeline
- Revenue
The chain
Expertise → research → methodology → measurement → engineering → revenue.
Every claim on this site traces back through this chain. Nothing is asserted without a method behind it.
- Subject-matter expertise
- Technical research
- Methodology
- Measurement
- Data
- Engineering
- Revenue
Measurement
Repeated, structured observation of how AI systems answer your category's buying questions.
Diagnosis
Why you appear or fail to appear: sources, entities, factual consistency, technical accessibility.
Engineering
Content, data, websites, and AI systems changed at the level that actually moves discovery.
Revenue
Discovery joined to pipeline, so the work is judged on commercial outcome, not vanity presence.
Category position
Not SEO. Not a GEO tool. A measurement and engineering practice.
| Capability | Traditional SEO | Typical GEO practice | Dgenius |
|---|---|---|---|
| Search visibility | yes | yes | yes |
| AI recommendation measurement | no | yes | yes |
| Cross-model repeated measurement | no | sometimes | yes |
| Source influence analysis | no | sometimes | yes |
| Factual consistency | no | sometimes | yes |
| Competitive answer share | no | yes | yes |
| Referral attribution | sometimes | sometimes | yes |
| Conversion measurement | sometimes | no | yes |
| Research methodology | no | rare | yes |
| Engineering implementation | limited | limited | yes |
| AI transformation | no | no | yes |
Engagements
One entry point. A clear path after it.
Every client starts with the same measurement. What follows is decided by what the measurement finds — never assumed in advance.
- 01
AI Discovery Diagnostic
This engagementMeasure + diagnose + prioritize
- — Intent mapping and prompt families
- — Cross-model observations
- — Competitor and citation analysis
- — Prioritized roadmap and executive briefing
$6,500 fixed · 10–14 days
- 02
AI Discovery Revenue Sprint
Implement the highest-value changes
- — Technical changes
- — Content and entity architecture
- — Structured information and analytics
- — Source strategy and experimentation
Typically $20K–$30K
- 03
AI Discovery Intelligence
Continuously measure + improve
- — Longitudinal monitoring and testing
- — Competitor and source intelligence
- — Ongoing interventions
- — Attribution and reporting
Typically $8K–$15K / month
- 04
AI Revenue Transformation
Fix the larger digital, data and AI systems discovery exposes
- — Websites and product engineering
- — Data, analytics and CRM
- — AI agents and knowledge systems
- — Conversion infrastructure
Typically $100K–$500K+
Intellectual provenance
Dr. Anthony Bowen
Founder · Subject Matter Expert
Author — technical paper on KSM
Dr. Anthony Bowen provides the subject-matter expertise from which Dgenius approaches machine-mediated discovery, measurement and optimization. He is the author of a technical paper on KSM. Details of that paper are published here once verified — nothing about it is characterised before then.
- Paper
- Withheld until verified
- Abstract
- Withheld until verified
- Publication
- Withheld until verified
You cannot fix what you have never measured.
The diagnostic tells you exactly where you stand in machine-mediated discovery, why, and what to change first. Fixed price. Fixed scope. Ten to fourteen days.