Project snapshot: AI Receptionist for Call Routing + Scheduling (anonymized)

Purpose: Illustrate a front-desk style voice agent that routes calls, answers basics, and schedules—without over-promising.

Primary CTA: Let's talk

TL;DR

An AI receptionist handled common inbound requests, routed to the right person/queue, and scheduled appointments when possible—while making it easy to reach a human.

The workflow

Problem: Front desk overload, missed calls, inconsistent call routing, and friction in scheduling.

What we automated:

Identify caller intent (new inquiry, existing customer, billing, scheduling)
Route calls based on intent and rules (hours, location, priority)
Answer high-frequency FAQs (hours, directions, required documents)
Offer scheduling/confirmation for supported appointment types

What remained human-led:

Complex customer situations
High-stakes exceptions and policy decisions

Constraints

Must respect business hours and escalation rules
Must not “hallucinate” pricing/policy; use approved sources
Keep caller experience friendly and fast (avoid long back-and-forth)
Clear fallback: a human option must always be reachable

Approach

Conversation design

Quick intent selection + one clarifying question
Always provide a "talk to a person" path
Confirm appointment details (time, location, contact) before booking

Integrations

Calendar/scheduling system (create, reschedule, cancel where allowed)
CRM/contact database (lookup/create contact)
Knowledge base for FAQs

Guardrails + reliability

Only book within allowed windows; confirm time zone when relevant
If any ambiguity → route to staff with a concise summary
Keep an explicit “allowed answers” surface for sensitive topics (policy/pricing)

High-level architecture

Telephony + voice pipeline
Agent with tool access (calendar, CRM, knowledge retrieval)
Rules engine for routing + hours
Logging and QA review loop for improving flows

Launch + iteration

Rollout pattern (typical):

Start with a small intent set (top 3–5) and business-hours coverage
Ship with a strict escalation path and conservative confidence thresholds
Review call transcripts weekly and expand scope deliberately (new intents, new actions)

Operational readiness:

Monitoring: intent distribution, escalation reasons, failed tool calls, latency
QA loop: sample calls → label outcomes → adjust prompts/guardrails/routing rules

Measurement

Success metrics (examples):

Missed-call rate (baseline → after launch)
Successful routing rate (right queue/person on first attempt)
Appointment booking completion rate (for supported appointment types)
Average call handle time (for supported intents)

Metric placeholders we’ll define with you:

Target missed-call reduction: set after a 1–2 week baseline review
Target booking uplift: set after baseline (based on supported appointment types)
Target reduction in transfers: set after baseline (based on routing rules + staffing)

Outcome (MVP-safe)

The receptionist pattern improved call handling consistency and reduced front-desk load for the supported intents—while preserving a clear human fallback.

If you want this pattern

Ideal for teams with repeatable inbound call categories.
Works best when routing rules are explicit and staff ownership is clear.

CTA: Let's talk