Ongoing Legibility
Check again when the answer matters or the conditions change
A first audit gives you a starting point. A follow-up tells you what happened after the work. Further checks make sense when those decisions continue to matter to your business.
Your offer may change. A site migration can affect access to important pages. A source may disappear, a competitor may introduce a stronger offer, or an AI product may change how it searches and answers. Any of these can be a reason to revisit the relevant scenarios.
The response should be proportionate. One unusual answer calls for another look before it triggers a rewrite. Repeated changes deserve investigation. Stable, lower-stakes scenarios can be checked less often or after a specific event.
Ongoing work connects each meaningful change to a decision: investigate, correct, test again or leave things alone. The cadence follows the business and the evidence. There should be a clear reason for each check and each piece of work.
Choosing the right scope
When is a one-time audit enough?
The scope should follow the decision at hand. A focused audit can help clarify a specific problem, prepare for a launch or establish whether further work is worthwhile. Implementation and a follow-up test need an agreed scope. Further checks make sense when important scenarios continue to matter and the team is ready to act on the findings.
What does ongoing work involve?
Checking whether important changes have held, investigating meaningful shifts and deciding what to do next. That can include correcting outdated facts, reviewing a changed source or competitor, and testing an approved intervention. The frequency should reflect the stakes and pace of change. Rewriting content every month is not the goal.
Can you guarantee that AI will recommend us?
No. We can define relevant tests, document what appears, improve evidence and access, and check the result. We cannot control every model, source or personalized answer. A recommendation in a test is also different from an inquiry or a sale; business outcomes need their own evidence.
How will we know whether a change helped?
We compare repeated observations before and after the change using the same core scenarios and as closely matched conditions as possible. We keep the change log, failed tests and limitations. Where feasible, unchanged comparison scenarios help us interpret broader shifts. A before-and-after difference is an observation; a causal claim needs stronger evidence.
Start with a measurement
Tell me what you want to understand, which people or businesses to review, and the markets and languages that matter. We’ll agree on the scope before work begins.