Managed AI workers for real business operations

Put an AI employee on the work your team keeps absorbing.

Guad Squad builds and runs practical AI employees for intake, follow-up, scheduling, reporting, document chasing, CRM cleanup, and back-office admin — with approvals, logs, and human handoffs from day one.

Starts with one painful workflow, not a vague AI wish list. Runs in approval mode before sensitive actions go live. Documented rules, logs, escalation paths, and monthly tuning.

The hook

Your business is already paying an invisible admin tax.

The cost is not just payroll. It is delayed replies, forgotten follow-ups, stale records, duplicated updates, and managers becoming human routers.

Someone checks the same inbox six times a day.That is a workflow begging for triage, summary, and routing.
Customers wait because the next step lives in someone’s head.That is where an AI employee can draft, remind, and escalate.
Your CRM, spreadsheet, or scheduler is always almost updated.That is operational drag — not a discipline problem.

AI employees

Start with the job that leaks the most money.

Each AI employee is scoped around a real workflow, connected only to approved tools, and launched with clear rules for what it can do, draft, log, and escalate.

01

AI Dispatcher

Captures job requests, gathers details, confirms next steps, and keeps after-hours leads from going cold.

  • Call/text/web lead intake
  • Urgency and job detail capture
  • Scheduling or queue handoff
02

AI Intake Coordinator

Turns forms, emails, and call notes into clean intake records with missing details and next steps surfaced.

  • Structured intake summaries
  • Missing document requests
  • Routing by rules and priority
03

AI Follow-Up Rep

Keeps quotes, estimates, renewals, and stale opportunities moving without making your team remember every touch.

  • Quote follow-up queues
  • Customer check-ins
  • Human handoff flags
04

AI Reporting Assistant

Collects approved inputs and turns messy updates into weekly summaries, exceptions, and owner-ready action lists.

  • Weekly operating summaries
  • Exception reporting
  • Data gap detection
05

AI Admin Coordinator

Chases documents, sends reminders, updates lists, and prevents routine internal work from clogging the day.

  • Document chasing
  • Reminder queues
  • Approval tracking
06

AI CRM Cleaner

Finds stale records, missing fields, next-step gaps, and account notes that keep sales teams babysitting databases.

  • Record cleanup queues
  • Account activity summaries
  • Next-step preparation

Proof without theater

Built around evidence, not AI hype.

For a faceless launch, trust has to come from the operating system: narrow scopes, visible logs, approval rules, and a working first workflow before expansion.

  • Workflow map before build. The first deliverable is a plain-English map of the job, tools, exceptions, and handoffs.
  • Approval mode first. Customer-facing or sensitive actions start as drafts until the rules are proven.
  • Evidence after launch. You see what was handled, what stalled, what needed a human, and what should be tuned next.

Example first build

After-hours lead intake for an owner-led service business.

No fake case study. This is the kind of narrow workflow Guad Squad looks for first: recurring, rules-based, expensive when missed, and easy to review.

1. Capture call, form, text, or email details. 2. Ask for missing basics and classify urgency. 3. Draft confirmation or route to scheduler. 4. Escalate pricing, angry customers, and edge cases. 5. Summarize the week for the owner.

Process

Not a chatbot. A managed operator.

The first build should prove value quickly, stay narrow enough to trust, and create the playbook for every AI employee after it.

Map the work

We identify the repetitive workflow, the systems involved, the edge cases, and the cost of doing nothing.

Build the worker

We connect approved tools, define permissions, write the operating rules, and build the first AI employee.

Launch safely

The workflow starts with approvals, logs, test cases, and explicit human handoffs where judgment matters.

Tune and expand

We review performance, tighten the runbook, and move to the next highest-value workflow when the first is trusted.

Guardrails

Built for operators who cannot afford AI chaos.

Useful automation needs boundaries. Every AI employee is designed around scope, visibility, and escalation — not magic.

Human approval

Sensitive, customer-facing, unusual, or judgment-heavy work starts in draft/review mode before autonomy increases.

Logs and runbooks

Every workflow gets documented rules, action logs, escalation paths, and a clear way to understand what happened.

Limited permissions

AI employees only get the access they need for the scoped job. No broad, mystery access to your business.

Simple pricing

One workflow, fully managed.

$2,500 / month

Includes the initial setup, first AI employee workflow, ongoing maintenance, tuning, support, logs, and human handoff rules. No separate setup fee for the first workflow.

  • Workflow review and build plan
  • First AI employee scoped around one workflow
  • Initial setup included in the first month
  • Approval mode for sensitive actions
  • Runbook, logs, maintenance, and support
  • Add-on workflows typically $1,000–$1,500/month each

Why this works

Start narrow. Expand only when it works.

Most AI projects fail because they start too broad. Guad Squad starts with one workflow already costing time, money, or customer trust. Once the first AI employee is useful, additional workflows can be added for $1,000–$1,500/month depending on complexity.

Book workflow review →

AI workflow guide

Ask questions or hand us the workflow.

Use the AI guide for deeper questions, fit checks, pricing context, or to send enough detail for a workflow review. When you submit the form, it notifies Guad Squad directly.

Next step

Bring the workflow your team keeps doing manually.

Send the inbox pile, intake queue, follow-up gap, reporting grind, document chase, CRM mess, or scheduling problem. We will tell you whether it is a fit for an AI employee — and what the first safe version should do.