Customers ask. Nobody answers fast enough.
Calls, messages, support, sales and intake.
AI systems for real work
The work is already there. The system is what is missing.
Calls that go unanswered. Customers nobody follows up. Documents copied between tools. Decisions made from stale reports. We design and build the AI layer that connects the work you already have.
Built into your existing stackWe start with the systems that already hold the truth.
Scope before buildProblem, integrations, permissions and success criteria are defined first.
Built to hand overArchitecture, rules and runbook stay with your team.
Start with the problem, not the tool
Which of these is happening inside your company?
People keep doing work software should already know how to do.
Documents, routing, approvals, reporting and handoffs.
The data exists. The decision still arrives late.
Alerts, recommendations, forecasts and planning.
The product you need does not exist off the shelf.
Custom agents, internal tools and customer-facing AI products.
What we build
We build around the workflow, not around a demo.
Customer operations
AI voice, messaging, support, intake, sales qualification, booking and follow-up tied to real customer context and business rules.
Internal operations
Document handling, routing, approvals, reporting, knowledge retrieval and repetitive cross-system work.
Data and decisions
Alerts, recommendations, forecasts, utilization views and decision support built on data the business already has.
Custom AI products
Internal tools and customer-facing products that do not exist off the shelf and need to fit a specific workflow.
The commercial difference
Not another tool you rent forever.
We scope it, build it into your environment, document it and hand it over. Your architecture, data schema, rules and runbook stay with your team. If you never call us again, the system should keep working.
- No seat count
- No GETMAI license key
- No forced migration
- No hidden dependency on our team after handover
Before architecture
The questions we usually hear first.
Can AI answer our calls without sounding ridiculous?
Yes, when the conversation is constrained by real business rules, live system context and a clear handoff path for judgment calls.
Will we have to replace our CRM or booking system?
Usually no. The point is to build around the stack you already run unless that stack is the actual blocker.
What happens when the AI does not know?
It should route, not improvise. Uncertainty and escalation rules are designed into the workflow.
Can it update the CRM instead of just answering questions?
Yes. A useful system closes the loop: request, context, decision, action.
Do we have to keep paying you forever?
No. GETMAI is built around a scoped build and handover, not permanent dependency on a license key.
How do we know it actually improved anything?
Define the baseline first, then compare operational metrics already present in the business.
Where we already know the workflow deeply
Vertical depth, not a company boundary.
Appointment businesses share a hidden economics: an enquiry that is not answered, a client who does not return, a conversation nobody reviews, a capacity gap nobody sees in time. We have modeled those workflows deeply, so we do not start from a blank page.
How we work
Problem first. Architecture second. Build third.
Diagnose the workflow
Map where work breaks, which systems hold the truth and which decisions need human judgment.
Define the system
Specify inputs, rules, integrations, escalation paths, outputs and success criteria.
Build into the real stack
Connect the actual tools and data the business runs, not a parallel demo.
Test edge cases
Unknown intent, incomplete data, conflicting records, failures, permissions and handoffs.
Hand over
Documentation, architecture, rules and runbook stay with the client team.
Measure the leak before claiming the fix.
Before a system gets credit for more bookings, faster handling or better utilization, we need the before-state. Where traffic allows, use holdouts. Use the client's own operational metrics. Do not publish invented percentages.
GETMAI was founded by Ekaterina Shalel after years of working across medicine, cosmetology and AI products. The company started with appointment businesses because those workflows made the losses unusually visible. The engineering model now extends beyond that category.
What keeps falling through the gaps?
Send us the workflow, not a wishlist of AI features. We will tell you whether the problem needs AI, ordinary automation, a better integration, or nothing new at all.