Rayse launched RAE, an AI chat engine embedded in its Agent Value Platform that allows real estate agents to log work, review customer journeys and manage client portals with natural voice or text instead of traditional portal interfaces, the company announced.
RAE (Rayse Assistant Engine) is designed to act as a personal assistant on the agent’s phone, giving users the ability to narrate their day – from shows and calls to lenders to offer preparation and follow-up – while the system captures, categorizes and time-stamps tasks in the background. The tool is distributed through multiple listing service (MLS) and agency partnerships across the country, according to the announcement.
Rayse’s basic pitch focuses on a pain point identified by many agents: clients often don’t see much of the work that goes on between showings and closings, which can fuel skepticism about agent value and commissions. The system tracks behind-the-scenes activities and presents them to consumers through a professional client portal, making the agent’s effort visible and measurable.
By adding RAE, Rays aims to remove a major barrier to the use of many proptech tools: the requirement for agents to log in and enter data manually.
“We stopped asking agents to work the way our platform works. Rayse now works the way agents work,” said Christian Dwiggins, co-CEO of Rayse. “They talk or text about what they’re doing, and RAE handles everything else. Their customers see every show, every phone call, every hour of preparation, automatically.”
For real estate professionals, the pitch is straightforward: if agents can capture their work in real-time through natural conversation, it’s easier to show value to buyers at a time when commission structures and agent compensation are widely considered. At the same time, usage-based data can help salespeople and teams understand productivity without forcing agents into rigid CRM workflows.
RAE is built natively within the Rayse system instead of wrapping a single third-party AI service, the company said. Rays describes the architecture as large-scale language model (LLM)-agnostic, allowing it to change or combine models as the state of AI changes while maintaining agent and client data within the platform.
The company has highlighted data privacy and security as a priority, especially for MLSs and organizations that distribute products to their members. Rayse said no agent or client data is sent outside of its environment for processing, a design choice that could appeal to organizations wary of exposing inventory or consumer data to outside AI providers.
Early usage metrics from Rayse’s partner network show increased engagement and adoption since RAE’s launch, according to the announcement, though the company did not disclose specific numbers.
Planned enhancements take RAE deeper into personal assistant territory. Chat journey summaries will provide agents with quick verbal or text feedback on any client, while active reminders will try to identify stagnant tasks, overlooked activities and recommend next steps. The goal is a context-aware assistant that tracks all the information in the background and helps agents prioritize actions.
The release also ties in with Rayse’s broader engagement strategy, which relies on best-in-class courses and education with industry voices rather than pure product demos. That approach reflects a broader trend among retailers and MLSs that integrate training tools, training and technology as a single value proposition to members.
As AI permeates the real estate workflow — from lead routing and tracking to listing descriptions and market analysis — tools like RAE are showing a shift from standalone apps to embedded assistants that sit on top of existing systems. For buyers, MLSs and organizations exploring AI options, questions will center on data governance, accountability, and whether conversational communications can produce reliable, auditable records of agent activity.
Rayse describes itself as an “Agent Value Platform” that combines logging, client journey tracking, a buyer-facing portal and conversational AI into a single system distributed through MLS and agency relationships.
This article was created using HousingWire Automation and reviewed by a HousingWire editor before publication. The program helps turn company announcements and industry data into HousingWire-style news coverage.
