Apollo by EliseAI

Apollo is EliseAI's agentic property-management layer. It can send prospect messages, update knowledge, reassign tasks and tours, change settings and generate custom dashboards, subject to the permissions of the signed-in user.¹ It is accessible through EliseCRM, the Agent by EliseAI mobile app and the Maintenance App.¹

The core proposition is execution rather than another reporting interface: Apollo can use the settings surface available inside EliseAI and act against live property-management data. Commercial Observer independently reported the September 3, 2026 launch as EliseAI's first cross-platform agentic AI tool and described it as a single agent for property-operations tasks.²

Apollo is a paid product with multiple usage tiers. EliseAI does not publish numeric Apollo pricing on the current product page and directs buyers to their account executive.¹ This makes direct value-for-money comparison difficult before a sales process.

Access is strongest for existing EliseAI customers because Apollo is native to the platform and inherits the information exposed through EliseAI's PMS integrations.¹ Admin-set usage limits by user or role are described as coming soon, so they should not be treated as a current control.¹

The most concrete governance signal is permission inheritance: Apollo's read and write access follows the existing user's permissions.¹ The company also says it has run 7,000 evals and uses a semantic layer intended to improve data accuracy, but those are vendor claims and are not independent proof of production reliability.¹

Commercial Observer corroborates the product launch and intended operational scope, but it is launch reporting rather than a hands-on reliability test.² The canonical 50-source panel produced no eligible Apollo-specific numeric external observation. No hands-on use is claimed here.

Apollo is best suited to multifamily operators, leasing teams and property managers already invested in EliseAI who want to replace repetitive CRM navigation with natural-language execution. Useful examples include bulk prospect messaging, tour reassignment, knowledge maintenance and custom operational dashboards.¹

It is less compelling for organizations outside the EliseAI ecosystem, buyers that require transparent self-serve pricing, or operators that need extensive independent evidence before allowing an AI agent to make operational changes.