AI Receptionist
Capture intent, qualify requests, answer approved questions, schedule and hand off when needed.
← MARTINEZ AI STUDIOS 2.0
AI DIGITAL WORKERS
Give repetitive work to systems built to do the work.
We design a bounded digital worker around one real business process, with clear tools, permissions, escalation rules and human checkpoints.
01 / WHO IT IS FOR
Service businesses, SMB operators, lean teams and founders with repetitive customer or back-office work.
02 / THE PROBLEM
Your team loses time moving information between calls, inboxes, calendars, documents and systems. Generic chatbots answer questions; the opportunity is to build a worker around the actual workflow.
03 / TARGET OUTCOMES
01Faster response and follow-up
02Less repetitive coordination work
03Consistent intake and documentation
04Human escalation for sensitive decisions
Concrete use cases.
We start with a bounded outcome. Architecture comes after.
Operations Worker
Read incoming requests, gather context, prepare updates, create structured records and route exceptions.
Research Worker
Collect current information from approved sources, compare options and produce decision-ready briefs.
Follow-up Worker
Track open loops, prepare reminders and move routine work forward without losing human ownership.
04 / WHAT WE DELIVER
A capability, not an empty demo.
- +Workflow map and risk boundaries
- +Agent instructions and tool permissions
- +Integrations with approved business systems
- +Escalation and approval logic
- +Testing scenarios and failure cases
- +Operator handoff documentation
05 / HOW WE WORK
Map
Choose one workflow with measurable friction.
Prototype
Build the smallest worker that can complete the useful part of that workflow.
Validate
Test with real edge cases, permissions and human escalation.
Scale
Expand only after the workflow is reliable enough to justify it.
ILLUSTRATIVE EXAMPLE / NOT A CLIENT CASE
After-hours requests for a local service company
A digital worker needs a job description: the queue it owns, information it may use and a point where responsibility returns to a person. A convincing answer is not enough if the appointment was never saved or the next shift receives no context.
The scenario
Imagine a repair company receiving requests while its coordinator is offline. The first pilot collects a request and prepares a scheduling suggestion. It does not diagnose equipment, invent availability or promise a technician.
Information needed
- Approved service descriptions and coverage areas.
- Read access to the scheduling system and an escalation contact.
- A short list of details genuinely needed to route the request.
Understand and complete
Identify the requested service, ask only for missing operational details and show the customer a summary to correct. Preserve uncertainty instead of guessing.
Prepare a proposed slot
Read current availability, attach the source and prepare an appointment draft. If the service or location falls outside the approved scope, route it to the coordinator.
Confirm the handoff
Only an authorized confirmation creates the booking. Return a reference and make unresolved requests visible to the next shift, with an owner and a reason.
What a useful handoff looks like
A reviewable request containing the service, missing details, proposed time, source of availability and assigned coordinator. The customer can distinguish a request received from an appointment confirmed.
FROM EXAMPLE TO PILOT
Test before expanding the scope.
One request, one record
Replay duplicate messages and delayed responses. Confirm they cannot create two appointments or lose the original request.
A useful exception
Test an unsupported service, unavailable calendar and missing address. The coordinator should receive the context already collected, not a blank notification.
What to measure
- Completeness of the handoff, reviewed by the coordinator.
- Duplicate bookings, missed exceptions and corrected classifications.
- Time from request received to human decision; compare like-for-like requests.
When to stop and review
Do not expand autonomy while the calendar is unreliable or nobody owns escalations. A reliable intake draft is a valid first release.
Start with a sample of the current work. Compare equivalent tasks, record human corrections and agree acceptance criteria before attributing an improvement to the system.
What we can learn from other companies.
Public references for studying design decisions. These companies and practitioners are not clients, partners or endorsers of Martinez AI Studios. The studio’s observations are interpretations, not promised results.
EXTERNAL REFERENCE / Intercom
Fin: a deliberate handoff to a person
Intercom documents a Fin procedure that pauses for a teammate’s decision, carries the review context, then resumes or hands over the conversation. Its configuration includes a timeout and an escalation owner. This is a documented product workflow, not an independently measured customer outcome.
Our reading: the handoff is part of the product. Define who receives it and what happens if nobody responds.
Read the source: Intercom Help ↗ · Reviewed 2026-09-21
FREE DIAGNOSTIC
MAP MY WORKFLOW →How to choose between a digital worker and a business system →Which workflow should you automate first?
Use the AI Workflow Opportunity Map: 15 points across frequency, structure, access, control and oversight to prioritize a better-shaped first pilot.
Questions before we build.
Is this just a chatbot?
No. A chatbot is an interface. A digital worker is designed around a bounded workflow, authorized tools, state, rules and escalation.
Does the AI make every decision?
No. Sensitive decisions, approvals and exceptions can remain human-controlled. We design the boundary explicitly.
Do we have to replace our existing software?
Usually not. The goal is to make your current systems easier to operate through an AI layer where it creates value.
07 / NEXT STEP
Describe the problem. We design the path.
You do not need to choose models, frameworks or protocols. Tell us the process, the friction and the outcome you want.