Discover
Map current brand understanding across the selected AI systems, prompts, markets and competitors.
Five stages connect diagnosis to sustained improvement.
Every engagement begins with a bounded question: which buyers, categories, markets, competitors and AI systems matter? That scope keeps the work commercially relevant and makes later measurement more meaningful.
Diagnose
Identify gaps in entity clarity, buyer-question coverage, source authority, accessibility and factual consistency.
Engineer
Create or improve first-party pages, structured information and content architecture around the evidence gaps.
Influence
Strengthen legitimate external corroboration through expert contribution, relevant profiles, references and digital PR opportunities.
Measure
Repeat controlled tests, log changes and distinguish directional progress from model variability.
Methodology and governance
The framework is designed for leadership teams that need useful decisions, traceable evidence and clear limitations.
How do you prevent cherry-picking prompts?
We agree the prompt themes, category boundaries, competitors and systems before the principal test. Material changes to the test set are logged rather than silently blended into trend reporting.
How do you handle changing AI answers?
We treat variability as a property of the system. Findings are time-stamped, important tests can be repeated, and reporting separates stable patterns from one-off outputs.
Do you use automated or human review?
Automation can support collection and comparison, while material conclusions and recommendations are human-reviewed for context, factual accuracy and commercial relevance.
What counts as progress?
Progress can include better factual accuracy, stronger presence in relevant shortlists, improved citation quality, greater category association and competitive gains across a controlled prompt set.