When I launched Pixelhatch, Google kept autocorrecting it to “Pixel Watch.”
Not occasionally. Consistently. People searching for my business were being quietly redirected toward a Google hardware product, because the signal I had put into the world was not strong enough yet to anchor the name to anything real.
The name was not the problem. The signal was.
There was nothing for the system to grab onto — no consistent description, no clear category, no pattern of references it could trust. So it did what systems do when the signal is weak: it reached for the nearest familiar thing and filled the gap.
That is not an algorithm failure. That is exactly how it is supposed to work.
And it is fixable. We even added a line to our FAQ — “not a watch, and considerably smarter than a smartwatch” — because sometimes you repair the signal and have a little fun with it.
I share that story because it is a clean example of something I see constantly — in businesses, in careers, in products that should be thriving but are not being found.
For years we have been taught to optimise for the platform.
Optimise for Google. For LinkedIn. For the algorithm. For engagement. For AI.
Every new platform brings new rules, new tactics, new practitioners selling the latest playbook. The advice changes. The channels shift. The tools evolve.
But a pattern keeps repeating.
The businesses struggling to be found are often not lacking expertise. The professionals struggling to be discovered are often not lacking experience. The products struggling to gain trust are often not lacking capability.
The problem, more often than not, is simpler.
The signal is broken.
The visibility problem is not always a visibility problem
When people talk about visibility, they reach for distribution.
More traffic. More impressions. More reach. More content.
But there is a step that comes before all of that — one most visibility strategies skip entirely.
Before something can be found, it must first be understood. Before it can be recommended, it must be trusted. Before it can be chosen, it must make sense.
This is where most strategies fall apart. The focus is placed on influencing the system rather than improving the signals the system is reading.
As AI assistants become part of how people search, research and make decisions, this gap is more consequential than it has ever been.
What changes in the agentic era
Here is what is different now.
When someone searched for you five years ago, they landed on your page and formed their own interpretation. They brought judgment. They read between the lines. They gave you the benefit of the doubt.
Increasingly, that is not how it works.
Today, an AI assistant may form the first interpretation on your behalf — synthesising what it knows about you from across the web, your documentation, your content and your mentions — and surface a summary, a recommendation or a shortlist.
You never get to explain yourself.
The AI is working from signals. And if those signals are weak, incomplete or inconsistent, the outcome is not confusion.
It is absence.
Or worse — the system confidently misrepresents you to someone who was ready to say yes.
A business owner asks an AI for a recommended service provider. A recruiter uses AI to help review candidates. A team researches software through conversational search. A customer asks for recommendations before they buy.
These systems do not know what is valuable. They infer it from evidence.
The challenge is no longer simply being present. It is being represented accurately.
What is Agentic Signal Repair?
Agentic Signal Repair is the practice of improving the signals that help people, businesses and products become understood, trusted and chosen — by both AI-assisted systems and human decision makers.
It focuses on the information layer that exists before optimisation. The layer that determines whether you can be correctly interpreted at all.
It asks different questions.
Not “How do we rank higher?” — but “How are we actually being understood?”
Not “How do we get more visibility?” — but “What signals are preventing us from being chosen?”
Not “How do we influence the algorithm?” — but “What evidence exists for the algorithm to trust?”
These are different problems. They require a different kind of fix.