AI Intake & Revenue Capture.

Design an intake system that answers, qualifies, schedules, updates the CRM, and escalates with human judgment intact.

AccessFree · public

Structure6 modules · 3.5 hours

LevelOperator

Fieldwork6 operating artifacts

Who this is for

Operators evaluating AI reception, voice agents, digital intake, scheduling automation, or after-hours coverage.

Course outcome

Leave with an intake map, business case, knowledge boundary, escalation policy, acceptance tests, and launch scorecard.

Model the revenue leak

Quantify missed demand, staff load, after-hours exposure, and the value of a successful recovery.

Lessons

↳Call volume, miss rate, lead value, and conversion
↳Triage labor and interruption cost
↳A conservative recoverable-value model

Field assignment

Model low, expected, and high cases using actual call and appointment data—not vendor assumptions.

Map the conversation system

Document intents, questions, decisions, tools, handoffs, and failure states before choosing technology.

Lessons

↳Intent and journey mapping
↳Qualification rules and required disclosures
↳Human handoff and emergency escalation

Field assignment

Map the ten highest-volume conversation paths and identify the point where automation must stop.

Build the knowledge boundary

Decide what the system may answer, what it must verify, and what belongs with a person.

Lessons

↳Source hierarchy and approved knowledge
↳Uncertainty, refusal, and sensitive topics
↳Content ownership and update cadence

Field assignment

Create the first approved-answer set with source, owner, review date, and escalation rule.

Connect the operation

Design reliable calendar, CRM, messaging, payment, and internal handoffs around the real workflow.

Lessons

↳APIs, webhooks, identity, and permissions
↳Retries, duplicate prevention, and audit logs
↳Operator notifications and manual override

Field assignment

Publish a system-of-record map and acceptance criteria for every write the intake layer may perform.

Test production behavior

Evaluate the difficult conversations demos avoid before exposing the system to customers.

Lessons

↳Scenario suites and red-team conversations
↳Latency, transcription, interruption, and noise
↳Accuracy, containment, escalation, and recovery

Field assignment

Run a 50-scenario acceptance suite spanning routine, ambiguous, adversarial, emotional, and emergency cases.

Launch and operate

Release in controlled stages, review real interactions, and measure the system as revenue infrastructure.

Lessons

↳Shadow, after-hours, overflow, and full-production phases
↳Conversation review and change control
↳Recovered revenue, resolution, and human workload

Field assignment

Create a staged launch plan with rollback conditions, daily review owners, and a 30-day operating scorecard.

Next course

Vibe Coding, Properly

Turn natural-language direction into working software without surrendering architecture, security, testing, or ownership to the model.

Continue learning

— Turn the fieldwork into a system

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