Level 2 taught you to use AI well on one task at a time. Level 3 is about operations: the repeatable workflows a team or brokerage runs hundreds of times a year, the standard operating procedures that make them consistent, the knowledge base of forms, rules, and MLS policy the AI draws on, the automations that move leads and listings without a person touching every step, and the quality assurance that catches the failures before a client, a commission, or a fair housing tester does.
The course follows a transaction from lead to closing and asks, at each step, what should be automated, what should be AI-drafted and reviewed, and what must never touch a model: disclosures, opinions of value, compensation terms, legal conclusions, and wiring instructions. It then builds the operating layer around that answer. You will write SOPs with review gates, classify every brokerage task by risk, build and test a retrieval knowledge base against stale forms and invented citations, design lead and listing automations that respect TCPA, CAN-SPAM, and HUD's guidance on algorithmic advertising, connect CRM, MLS, and transaction management systems with data minimisation, sample AI output with an error taxonomy, and measure performance without rewarding the wrong behaviour.
Two case studies close the course: a listing description automation that produced a fair housing complaint, and a brokerage whose property management arm adopted an algorithmic tenant screening tool without reading HUD's 2024 guidance. The capstone asks you to design a complete AI-assisted transaction workflow for a fictional brokerage, with SOPs, a risk matrix, a QA plan, escalation paths, and metrics, and it is graded against a published rubric.
This is an independent educational course offered by AI Coalition Network. It does not replace a real estate licence, broker supervision, MLS rules, or the laws of any state.