Start your AI leasing assistant evaluation with the workflow
An AI leasing assistant should be evaluated against a specific leasing workflow, not a long feature list. Write down where inquiries arrive, what information you need before a showing, who approves exceptions, how calendars are managed, and where records must end up.
This exposes the difference between a useful capability and a demo. If your main problem is inconsistent pre-screening, a polished voice interface does not solve it. If your team loses context during handoff, response speed alone is not enough.
Score the capabilities that affect daily work
Give each category a weight before viewing vendor demos. A simple five-point score is enough if the criteria are specific and everyone uses the same evidence. Require the vendor to show your workflow with realistic properties and exceptions.
- Inquiry capture: supported entry points and property context
- Qualification: structured questions, branching, and consistent records
- Scheduling: availability rules, confirmations, and calendar behavior
- Human handoff: clear ownership, history, and exception handling
- Data quality: duplicate prevention, corrections, exports, and audit history
- Administration: setup effort, permissions, support, and reporting
Ask risk and control questions before feature questions
Ask whether the system makes eligibility decisions or only assists staff. Confirm how protected or sensitive information is handled, how long data is retained, and whether administrators can review what happened. For compliance-sensitive workflows, consistent human oversight is more important than a claim that the system is fully autonomous.
Security review should cover authentication, encryption, access controls, incident handling, subprocessors, and data export or deletion. Technical integrations also need retry behavior, rate limits, and a documented source of truth.
Compare total cost and implementation effort
Normalize every quote into a twelve-month total. Include setup, training, usage charges, integrations, support tiers, and the internal time needed to clean property data. Then compare that cost with a conservative estimate of work removed or revenue protected.
Avoid paying for channels or automation you cannot operationally support. A smaller system with clear handoff and accurate records can outperform a broader tool that creates silent exceptions.
Use a real pilot scorecard
Pilot the finalists with the same properties, questions, calendars, and success criteria. Review failed and ambiguous interactions, not just completed ones. Ask staff whether they trust the record and know what to do next.
Rentalot supports structured pre-screening, property and contact organization, conversation history, showing coordination, management chat, and plan-based API access. Evaluate those released workflows directly. Do not assume that every channel or autonomous follow-up shown in the broader AI market is available in every product.
- Accuracy of property and availability information
- Completion rate for the intended workflow
- Number and type of human exceptions
- Time required to review and correct records
- Staff confidence in the next action
- Annualized cost at expected volume