{"id":47202,"date":"2026-03-11T17:19:20","date_gmt":"2026-03-11T14:19:20","guid":{"rendered":"https:\/\/mk.gen.tr\/rayse-launches-conversational-ai-assistant-rae-to-help-agents-log-work-by-voice-or-text\/"},"modified":"2026-03-11T17:19:20","modified_gmt":"2026-03-11T14:19:20","slug":"rayse-launches-conversational-ai-assistant-rae-to-help-agents-log-work-by-voice-or-text","status":"publish","type":"post","link":"https:\/\/mk.gen.tr\/tr\/rayse-launches-conversational-ai-assistant-rae-to-help-agents-log-work-by-voice-or-text\/","title":{"rendered":"Rayse launches conversational AI assistant RAE to help agents log work by voice or text"},"content":{"rendered":"

Rayse<\/strong> has launched RAE, a conversational AI engine embedded in its Agent Value Platform that allows real estate agents to log work, update client journeys and manage client portals through natural voice or text instead of traditional portal interfaces, the company announced.<\/p>\n

RAE (Rayse<\/a> Assistant Engine) is designed to function as a personal assistant on an agent\u2019s phone, giving users the ability to narrate their day \u2014 from showings and lender calls to offer prep and follow-up \u2014 while the system captures, categorizes and timestamps activities in the background. The tool is being distributed through multiple listing service<\/a> (MLS) and association partnerships nationwide, according to the announcement.<\/p>\n

Rayse\u2019s underlying platform focuses on a pain point many agents<\/a> identify: clients often don\u2019t see the bulk of the work that happens between showings and closings, which can fuel skepticism about agent value and commissions<\/a>. The system tracks behind-the-scenes tasks and exposes them to consumers via a professional client portal, positioning the agent\u2019s effort as visible and measurable.<\/p>\n

By adding RAE, Rayse is aiming to remove the biggest barrier to adoption of many proptech<\/a> tools: the requirement that agents log in and manually enter data.<\/p>\n

\u201cWe stopped asking agents to work the way our platform works. Rayse now works the way agents work,\u201d said Christian Dwiggins, co-CEO of Rayse<\/a>. \u201cThey talk or text about what they\u2019re doing, and RAE handles everything else. Their clients see every showing, every call, every hour of prep, automatically.\u201d<\/p>\n

For housing professionals, the pitch is straightforward: if agents can capture their activity in real time through natural conversation, it becomes easier to demonstrate value to consumers at a time when commission structures and agent compensation are under more scrutiny. At the same time, usage-based data can help brokers and teams understand productivity without forcing agents into rigid CRM workflows.<\/p>\n

RAE<\/a> is built natively inside the Rayse system rather than as a wrapper around a single third-party AI service, the company said. Rayse describes the architecture as large language model (LLM)-agnostic, which allows it to swap or blend models as the AI landscape<\/a> evolves while keeping agent and client data within the platform.<\/p>\n

The company highlighted data privacy and security as a key concern, particularly for MLSs and associations that are distributing the product to their memberships. Rayse said no agent or client data is sent outside its environment for processing, a design choice that may appeal to organizations wary of exposing listing or consumer data to external AI providers.<\/p>\n

Early usage metrics from Rayse\u2019s partner network show rising engagement and adoption since RAE\u2019s introduction, according to the announcement, though the company did not disclose specific numbers.<\/p>\n

Planned enhancements move RAE deeper into personal assistant territory. Conversational journey summaries will give agents instant verbal or text briefings on any client, while proactive reminders will attempt to identify stalled transactions, surface overlooked to-dos and recommend next steps. The goal is a context-aware assistant that tracks every detail in the background and helps agents prioritize actions.<\/p>\n

The rollout also ties into Rayse\u2019s broader engagement strategy, which leans on masterclasses and education with industry voices rather than pure product demos. That approach mirrors a wider trend among vendors and MLSs that are bundling coaching, training and tech tools as a single value proposition for members.<\/p>\n

As AI permeates real estate workflows \u2014 from lead routing and follow-up to listing descriptions and market analysis \u2014 tools like RAE illustrate a shift from standalone apps to embedded assistants that sit on top of existing systems. For brokers, MLSs and associations evaluating AI options, the questions will center on data control, liability, and whether conversational interfaces can produce reliable, auditable records of agent activity.<\/p>\n

Rayse describes itself as an \u201cAgent Value Platform\u201d that combines activity logging, client journey tracking, a consumer-facing portal and conversational AI in a single system distributed via MLS and association relationships.<\/p>\n

This article was generated using HousingWire Automation and reviewed by a HousingWire editor before publication. The system helps convert company announcements and industry data into HousingWire-style news coverage.<\/em><\/p>","protected":false},"excerpt":{"rendered":"

Rayse has launched RAE, a conversational AI engine embedded in its Agent Value Platform that allows real estate agents to log work, update client journeys and manage client portals through natural voice or text instead of traditional portal interfaces, the company announced. RAE (Rayse Assistant Engine) is designed to function as a personal assistant on…<\/p>","protected":false},"author":0,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false},"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/mk.gen.tr\/tr\/wp-json\/wp\/v2\/posts\/47202"}],"collection":[{"href":"https:\/\/mk.gen.tr\/tr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mk.gen.tr\/tr\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/mk.gen.tr\/tr\/wp-json\/wp\/v2\/comments?post=47202"}],"version-history":[{"count":0,"href":"https:\/\/mk.gen.tr\/tr\/wp-json\/wp\/v2\/posts\/47202\/revisions"}],"wp:attachment":[{"href":"https:\/\/mk.gen.tr\/tr\/wp-json\/wp\/v2\/media?parent=47202"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mk.gen.tr\/tr\/wp-json\/wp\/v2\/categories?post=47202"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mk.gen.tr\/tr\/wp-json\/wp\/v2\/tags?post=47202"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}