From consultation to daily work
Understand the work
We walk through the current workflow: who starts it, what comes in, which tools are involved, where it stalls, and what a successful finish looks like.
Set the rules
We agree which actions are allowed, which data is needed, when approval is required, and when a person must take over.
Build and connect
We connect the approved systems, build the path, and test normal cases, exceptions, and failure states.
Launch, measure, improve
We put the AI employee to work, track completed outcomes, and improve the rules using data and team feedback.
How we choose the first workflow
We look for work that repeats often enough, moves between tools or people, and has a recognizable start and finish. It should matter enough to improve, but be contained enough to test without turning the whole business into one project.
A strong starting point might be a new lead that needs a reply and a CRM record, meeting coordination across WhatsApp and a calendar, handling a recurring document, or routing an exception to the right person with the right context. Our guide to choosing the first AI employee workflow includes a short test and worked examples.
During the consultation, we turn a plain description of the work into a path that can be built, tested, and measured.
AI agents by the work a business needs done
Custom AI agents are developed around a workflow, not a catalogue of ready-made roles. These are examples of uses designed around the business's data, permissions, and points for human handoff.
AI sales agents
A lead sends its details, the agent checks what is missing, and it passes approved information into the CRM. When the enquiry needs judgment, a salesperson receives it with the context already collected.
AI customer service agents
A customer explains an enquiry, the agent clarifies the details needed and updates the relevant system. An exception or sensitive decision goes to a person instead of receiving an answer based on a guess.
AI operations agents
Information arrives from one system, the agent brings it together and routes a task to the right team. If data is missing or an action is rejected, it flags the exception for follow-up.
For a small business or an enterprise
AI agents for small businesses often begin with one recurring task that weighs on a small team. When assessing enterprise AI agents, we first examine process owners, permissions, data quality, existing systems, and the treatment of exceptions; that assessment does not mean the solution suits every enterprise. The integrations page explains how systems remain the sources of truth, and about GIMMI explains who this approach fits.
Permissions, approvals, and boundaries
The AI employee connects only to accounts, fields, and actions approved in advance. Together, we decide which actions can run automatically, which need approval, and which must go to a person. The owner or an authorized administrator remains in control of connections and access.
A good workflow plans for uncertainty. If information is missing, access is revoked, or a system is unavailable, the action stops with a clear status and reaches the right person instead of continuing on a guess.
What the 48 hours include
GIMMI completes the bespoke build, integrations, testing, and launch within 48 hours of the consultation. To meet that schedule, authorized people need to be available to approve the workflow and provide access to the selected systems. If an external platform requires an approval or review outside GIMMI's control, we make that dependency clear.
AI employee implementation services do not end at launch. Managed AI agents remain connected only to approved tools and actions, and guidance and support are available 24/7. We keep checking outcomes, removing friction, and expand only once the first workflow is stable and useful.
How we measure the result
Before launch, we agree a baseline for the same workflow: completed volume, team time, corrections, and cost per completed task. We then compare the same work under the same definition.
GIMMI's public target is up to 10 times more completed work at 90% lower cost per task than the same manual workflow. It is a target measured against an agreed baseline, not a promise that every workflow will produce the same result. Actual results depend on the workflow, volume, data quality, and integrations. The guide to measuring AI employee ROI explains how to build that comparison.
What to prepare for the consultation
- One example of repetitive work and who handles it today.
- The systems where that work happens and who can authorize a connection.
- Two or three normal cases, plus one exception that needs judgment.
- A plain definition of improvement: time, volume, quality, cost, or a combination.
If you do not have every answer yet, that is fine. Start with one piece of work that feels heavy, slow, or split across too many tools.