Be the firm AI engines recommend when a buyer asks who to hire.
Consultancies, IT services firms, accounting and advisory practices. ZivRank measures whether AI engines name your firm on the questions your clients ask, and builds the proof that gets you there.
Buyers of services ask AI engines for names: “best firm for…”, “who can help with…”. The answer usually lists a handful of firms. Expertise that is not documented where the engines look does not count.
Built for how your buyers research
Expertise made verifiable
AI engines name firms whose expertise is documented on credible third-party sources, not only on their own site. We build that footprint, topic by topic.
Practice-level questions
A firm is not one question. We measure practice by practice (for example cloud migration, ERP implementation, transaction advisory) so you see where you are named and where you are not.
Partner-reviewed content
Every asset is reviewed by your experts before it is published. Your reputation is the product; nothing goes out that your partners would not sign.
What your buyers ask AI engines
Examples of the questions we measure for professional services firms. Your own list is built and validated with you before the first measurement.
Where the answers come from
For services, the engines draw on directories, industry publications, partner listings, case studies and expert profiles. We measure which of them the engines reuse on your practice questions before deciding where to publish.
Measured before and after
Every program starts with a locked baseline measurement and is re-measured with the same protocol. You keep the raw answers, so every number in your report can be checked. See a measured case study →
From baseline to re-measurement
Measure
The buying questions of your market (50 from the Core plan upward), asked 5 times on each of 5 AI engines. You see where you are named, where you are only a source, and where you are absent.
Diagnose
Who is named instead of you, on which questions, and which sources the engines reuse. Plus a technical audit of what stops AI engines from reading your site.
Produce
Assets the engines can cite, placed on the sources they already trust in professional services. Reviewed by an expert before anything goes live.
Re-measure
The same protocol, replayed identically. A change only counts if it rises above the noise floor set before comparison.