Built around the role
Each AI employee is designed around a profession, its workflows, its language, and its daily pressure points.
AI employees are powerful. They need to be built properly.
We build managed AI employees with the systems, supervision, memory, tools, approvals, and ongoing improvement needed to operate inside real businesses.
Why we exist
The AI gap is not intelligence. It is implementation.
Most businesses already know AI matters.
They have seen the demos, opened the tools, tested the prompts, and heard the promises.
But the actual work still lives across calls, emails, documents, CRMs, calendars, task boards, reports, and follow-up lists.
The opportunity is not another AI tool. It is a managed employee layer that turns AI into working business capacity.
We do not sell chatbots. We build managed workers.
Connected to real tools
Phone, email, documents, calendars, CRM, task boards, social platforms, and reporting systems.
Managed after launch
We continue monitoring, improving, adjusting, and expanding the employee after deployment.
Designed for control
Approvals, draft-first workflows, escalation paths, logs, and review points can be built into the system.
Built by operators, not AI tourists.
Wharq was created for businesses that want practical AI implementation, not theory.
We focus on workflows that already cost companies time, money, speed, and attention: missed follow-ups, slow admin, messy systems, untracked conversations, and overloaded operators.
The goal is simple: install AI capacity where it creates measurable business leverage.
Founded by Joshua Khoury, Wharq was built from a simple observation: most businesses do not need more AI tools. They need someone to turn AI into a working employee inside their actual business.
The standard
Every AI employee is built to a working standard.
Clear role
The employee has a defined job, outcomes, boundaries, and escalation points.
Business memory
It is trained around your workflows, documents, tone, tools, and operating preferences.
Tool access
It is connected only to the systems needed to do the work.
Approval flow
Sensitive or external actions can start as drafts before becoming automated.
Monitoring
Failures, missed actions, usage, and workflow issues are reviewed.
Improvement loop
The employee is refined weekly based on real usage and business feedback.
Controlled by design
Uncontrolled AI is not leverage. It is another system to manage.
A real AI employee needs boundaries, approval paths, monitoring, and someone responsible for keeping it useful.
We build for control first, then expand automation where it earns trust.
What we refuse to ship
Because fragile AI creates more work than it removes.
Loose automations
Disconnected workflows that fire actions without a clear owner, review path, or business reason.
Chatbot costumes
A chat widget dressed up as an employee, with no real tool access, memory, or follow-through.
Fragile handovers
A one-off setup that works on demo day, then breaks when tools, staff, or workflows change.
Complexity theater
Extra platforms, prompts, and moving parts added to look advanced instead of making work cleaner.
What you get instead
A controlled AI employee layer with a defined role, connected tools, draft-first controls where needed, logs, monitoring, and weekly improvement.
The promise is not magic. It is managed capacity.
Your AI employee will not replace every human decision overnight.
But when built properly, it can remove the repetitive work that slows your team down: calls, transcripts, follow-ups, documents, emails, CRM updates, tasks, reports, and open loops.
That is the opportunity.
Start with the first employee your business actually needs.
We will map your workflow, identify the highest-value use case, and show you what a managed AI employee could handle first.