1. Start with a representative workflow
Write down a task your team performs repeatedly, the systems it touches, the evidence required, the people involved, and the cost of an error. Use the same scenario with every vendor.
A workflow test reveals more than an open-ended prompt. It shows whether the product understands transaction state, requests missing information, and produces an output that can actually enter the team’s process.
2. Inspect how the product gets context
Ask which systems the tool can access, how it matches information to records, how recently those connections refresh, and what happens when two records look similar.
For MLS or provider data, verify that the proposed use is allowed under the applicable license and sharing agreement. A technical connection does not by itself create the right to use data for every AI workflow.
- Which source is authoritative for each fact?
- Can the system cite or link back to the evidence?
- How does it detect stale, conflicting, or incomplete information?
- Can administrators limit access by workspace, role, record, and action?
- Does the product use standard real estate structures where appropriate?
3. Evaluate control and failure behavior
A product should explain more than what the AI can do. It should make clear what it cannot do, when it asks for review, and how an administrator can change the policy.
Ask to see a failed or ambiguous case. A trustworthy system should stop, surface the conflict, preserve the evidence, and route the issue to the right person.
4. Review data handling and security
Document what data enters the system, where it is stored, which subprocessors or models receive it, how long it is retained, and whether customer data is used to train models. Confirm authentication, access controls, export, deletion, incident response, and audit capabilities.
Do not rely on a generic security badge. Compare the vendor’s written terms and controls to the sensitivity of the actual workflow.
5. Measure quality before expansion
Time saved can be useful, but it is easy to overstate and hard to compare. Begin with measures that reveal reliability: correct record matching, extraction corrections, approval rate, exception rate, completion rate, and the share of outputs users discard.
If a vendor presents an annualized projection, ask for the observation period, sample size, denominator, calculation, excluded cases, and whether the figure describes typical, median, or best-case performance.
Common questions
What is the most important feature in a real estate AI tool?
Workflow fit is more important than a single model feature. The tool should obtain the right context, respect permissions, handle ambiguity, and produce an auditable next step.
Should a brokerage create an AI use policy?
Yes. A policy can define approved tools and data, prohibited uses, required review, disclosure expectations, retention rules, and accountable owners.
How long should a pilot run?
Run it long enough to observe normal work and exceptions across a representative set of transactions. Define the sample and success criteria before the pilot begins.
Sources and further reading
This page is written by Flatre Editorial for a U.S. real estate audience. It is educational product content, not legal, tax, financial, brokerage, or real estate advice.
- AI Risk Management FrameworkNIST
- Generative Artificial Intelligence ProfileNIST
- Artificial Intelligence in Real EstateNational Association of REALTORS®
- AI, data licensing, and sharing agreementsReal Estate Standards Organization
- Guidance on Applications of the Fair Housing Act to Digital AdvertisingU.S. Department of Housing and Urban Development