Services
Measurement first, then the work it justifies
Every engagement starts with a baseline, because without one there is no way to tell you whether anything that follows worked. What comes after depends entirely on what the baseline found.
Find out whether AI assistants currently recommend your business, who they recommend instead, and which sources they relied on to decide.
Includes
- A benchmark prompt library built from your services, cities and buyer language
- Prompts run across the benchmark platforms, each repeated so a single odd answer cannot distort the result
- Every response captured verbatim and stored as evidence
- Competitor identification — who is being recommended when you are not
Improve the signals that affect how AI systems find, read and understand your company — then re-measure to see whether it worked.
Includes
- Site and entity optimisation — consistent name, location, services and credentials
- Service page and location page structure
- Structured data that matches what the page actually says
- Crawlability and indexability review, including AI crawler access
Discover which third-party sources support the competitors AI recommends, and which of them have no record of your business at all.
Includes
- Citation and domain extraction from the measured answers
- Frequency analysis — which sources influence recommendations repeatedly, not once
- Competitor presence mapping per source
- Your presence, or absence, on each
Make your business consistently understood across the web — one name, one location, one set of services, everywhere a system might look.
Includes
- Entity audit across your site, profiles, directories and third-party references
- Name, address, phone and service-area consistency
- Services, specialisms and credentials recorded consistently
- Leadership and expertise attribution
Content built from the questions buyers actually ask and the gaps the measurement actually found — not generic posts produced by the dozen.
Includes
- Content priorities derived from lost prompts and observed source gaps
- Buyer-question mapping across the full intent range, not just discovery
- Briefs built around demonstrable expertise and original proof
- Evidence assets — project documentation, before-and-after records, real specifics
Re-run the identical benchmark on a fixed schedule so movement is attributable and drift is caught while it is still small.
Includes
- The frozen benchmark re-run each cycle, with full evidence capture
- Score movement against baseline and the trailing 90 days
- Platform-by-platform comparison
- Competitor movement and share of voice
How these fit together
They are not a menu of equivalent options. There is an order, and it follows the evidence.
- Always first: the baseline. It tells you where you actually stand and produces the diagnosis everything else is prioritised from. We do not sell the rest without it.
- Then whichever constraint the baseline exposed. A business invisible because its site cannot be parsed needs optimisation. One that is legible but has no third-party corroboration needs source gap work and entity authority. One that is present but described wrongly needs accuracy work.
- Content, once there is something to aim it at. Content strategy is built against the specific prompts you lost, which means it comes after the measurement that identified them.
- Monitoring, continuously. The benchmark re-runs on a schedule so movement stays attributable and drift is caught while it is still small.
Start where every engagement starts
A baseline measurement of how often AI names your business, who it names instead, and which sources it trusted.