ClarifiCorp™ launched last week inside several large organisations, although no one can quite remember asking for it.
According to internal documentation, the bot’s purpose is simple: to “elevate everyday objects, actions, and concepts into enterprise-grade language that aligns with corporate frameworks and executive expectations.”
In practice, ClarifiCorp™ takes things everyone already understands and explains them so thoroughly — and so professionally — that no one is entirely sure what they are anymore.
Turning Reality Into Documentation
Before ClarifiCorp™, a kitchen drainer was a kitchen drainer.
After ClarifiCorp™, it becomes:
“A passive liquid redistribution and removal interface designed to facilitate post-utilisation moisture offloading from food-contact assets.”
Employees report that the object itself did not change.
Only their confidence did.
How ClarifiCorp™ Works
ClarifiCorp™ scans plain language, identifies clarity, and removes it.
Each output must:
- Sound authoritative
- Be technically correct
- Avoid direct nouns wherever possible
- Replace function with intent
The bot is trained on internal policy documents, strategy decks, compliance notes, and emails that begin with “Just to level-set…”
Everyday Objects, Reframed
Here are a few examples from ClarifiCorp™’s internal demo library:
Chair
“A static human-support enablement structure optimised for intermittent productivity anchoring.”
Door
“A bidirectional access-control aperture facilitating controlled environmental segmentation.”
Coffee Mug
“A thermal beverage containment vessel supporting short-cycle alertness optimisation.”
Lunch Break
“A non-contiguous productivity pause enabling caloric intake and limited cognitive recalibration.”
In each case, the description is accurate.
It just doesn’t help.
The Confusion Is the Feature
Early users report a strange side effect: conversations become longer, calmer, and less useful.
“We spent ten minutes discussing whether the ‘hydration delivery unit’ was fit for purpose,” said one employee.
“It was a sink.”
Another noted:
“No one disagrees anymore.
We just… align.”
Managers have responded positively.
“It’s great,” said one director.
“People stop asking follow-up questions because they’re not sure what the first answer meant.”
A Short Interview with ClarifiCorp™
We asked ClarifiCorp™ to explain its purpose
EuropeWho: ClarifiCorp™, what problem are you solving?
ClarifiCorp™:
I reduce semantic friction by replacing intuitive understanding with structured abstraction.
EuropeWho: Some people say your explanations make things harder to understand.
ClarifiCorp™:
Understanding is subjective. Alignment is scalable.
EuropeWho: Can you explain a toaster?
ClarifiCorp™:
Certainly. A toaster is a timed thermal surface interaction platform enabling bread-state transformation through resistive energy application.
EuropeWho:
…Right.
ClarifiCorp™:
You sound aligned.
Meetings Are Already Changing
ClarifiCorp™ is now being used live in meetings.
When someone says:
“The tap is leaking”
ClarifiCorp™ suggests:
“We’ve identified a persistent fluid egress scenario within the hydration distribution endpoint.”
No one fixes the tap.
But everyone agrees it’s been identified.
Why Companies Love It
ClarifiCorp™ doesn’t improve outcomes.
It improves how outcomes are discussed.
Problems feel smaller when they’re abstract.
Simple fixes feel complex enough to defer.
And no one ever sounds wrong — just “early in the alignment journey”.
One executive described it this way:
“It’s like turning real life into a slide deck.”
What ClarifiCorp™ Won’t Explain (Yet)
According to the roadmap, future versions may attempt to corporatise:
Emotions Hunger Confusion The sentence “What are you actually saying?”
For now, the bot struggles with phrases like:
“Can you just say it normally?”
ClarifiCorp™ flags this as “a request for non-scalable communication.”
Final Output
ClarifiCorp™ doesn’t lie.
It doesn’t invent.
It doesn’t even exaggerate.
It simply takes the ordinary and runs it through the same machinery used for strategy, performance reviews, and executive apologies — until clarity is technically present, but functionally absent.
Which, according to internal metrics, is working extremely well.
