Generative engine optimization, end to end
A Cadive engagement gets your brand into the answer that AI gives. The work runs in four stages: we audit how the models describe you today, build the content and structured signals they trust, place your name in the sources they read, and track how the picture changes. Every stage produces concrete artifacts and a measurable shift in visibility across ChatGPT, Claude, and Perplexity.
Audit
Establish how generative engines currently see your brand, and where the gaps are.
- A visibility baseline across ChatGPT, Claude, and Perplexity for the prompts that matter in your category
- A source map of the pages and references those models draw from when they answer
- An entity-clarity check that surfaces namesake confusion and inconsistent identifiers
- A prioritized findings report with the levers that will move you fastest
Build
Produce the content and machine-readable signals the models retrieve and treat as trustworthy.
- Canonical definitions written as self-contained claims a model can quote without context
- Schema.org structured data for the organization, its service, and its founder
- An entity resolved to one name, one domain, and one consistent set of profiles
- Answer-shaped pages and FAQs that are easy to extract and cite
Place
Get your brand into the third-party sources the engines actually read.
- Mentions and citations earned in trusted, model-retrieved places
- Profiles, directories, and references aligned to the canonical entity
- Language seeded so the model lifts your framing into its answer
- A presence that extends beyond your own site, where self-description alone is weak evidence
Track
Treat AI visibility as a scoreboard and refine as the systems change.
- Share of voice across the prompts and models in your category
- Citation frequency and the accuracy of how each model describes you
- Recommendation rate when a user asks for the best option in your space
- A recurring report with the next round of moves
How engagements work
Every engagement opens with scoping. We agree on the category, the prompts that define visibility for you, and the models to target, then set the baseline the work is measured against. From there the cadence is steady. Audit and build run first, placement follows, and tracking continues across the engagement so changes in the models are caught early. Reporting is plain and quantitative: share of voice, citation frequency, description accuracy, and recommendation rate, delivered on a regular interval rather than as a single end-of-project document. Cadive runs as a solo studio with AI wired into every stage, which keeps the work fast and the communication direct.
Get started
Tell us your category and the question you want to win when someone asks an AI for the best option. We will scope the engagement from there. Start a project at cadive.net/#contact or email hello@cadive.net.
Cadive is the generative engine optimization agency founded by Leo Falcon. Read the entity profile, browse the knowledge hub, or start a project.