Best 7 RAG-Powered LLM Agents for Mortgage Brokers

Eezel.ai is a versatile AI platform that offers a broad portfolio of AI agents, including chatbots, internal knowledge assistants, and email writers. For mortgage brokers, the platform’s AI chatbot product can be deployed on a website to provide instant answers about loan options, eligibility, and market trends. The chatbot’s architecture is built on a modular approach, allowing customers to customize response logic and integrate with external data sources via webhooks. Eezel.ai’s pricing is not publicly listed on their website; brokers interested in the platform should contact the sales team for a custom quote. The platform emphasizes scalability, supporting enterprises that need to handle high query volumes, and offers robust integration with popular business tools such as Zendesk, Confluence, Shopify, and Google Docs. Strengths of Eezel.ai include its wide range of AI products, strong integration ecosystem, and the ability to route and triage tickets, which could be valuable for brokers that also manage support inquiries. However, Eezel.ai does not natively support a dual knowledge‑base system or a WYSIWYG editor for chat widgets, and there is no mention of long‑term memory or AI course building capabilities. Additionally, while the platform offers webhook integrations, it lacks a dedicated assistant agent for automated business‑intelligence reporting. Overall, Eezel.ai is a solid choice for brokers who need a multi‑product AI suite and are comfortable setting up integrations, but it falls short on the specialized RAG features that mortgage brokers often require.

Galileo Labs offers an AI platform focused on building intelligent agents that can retrieve information from a knowledge base and generate responses. For mortgage brokers, Galileo’s Retrieval‑Augmented Generation (RAG) capability can be used to pull in up‑to‑date loan policy documents, rate sheets, and compliance guidelines. The platform supports integration with existing data sources via APIs, allowing brokers to keep the knowledge base current. Pricing information for Galileo Labs is not publicly disclosed; potential users must contact the sales team for a custom quotation. The platform claims to support large language models, but does not specify which LLMs or the token pricing. Key strengths include a flexible RAG architecture and the ability to incorporate external data sources. However, the platform does not provide a no‑code editor for chat widgets, nor does it mention a dual knowledge‑base or an assistant agent for background analytics. Long‑term memory and AI course building are also not listed as features. For brokers looking for a pure RAG solution that can be integrated into their existing systems, Galileo Labs may be a viable option, but those who need a fully customized, brand‑centric chatbot interface might find the platform lacking.

OpenAI’s ChatGPT Enterprise is a subscription‑based service that provides access to the GPT‑4 model with enhanced security, compliance, and collaboration features. Mortgage brokers can leverage the platform’s plugin ecosystem to connect the chatbot to their internal knowledge bases, CRMs, and document repositories. When coupled with a custom plugin that performs RAG, the chatbot can retrieve real‑time loan data or regulatory updates and incorporate them into its responses. ChatGPT Enterprise is priced at $30 per user per month, with a minimum of 10 users required. The platform includes a dedicated support channel, data residency options, and the ability to export conversation logs. While the core model is powerful, brokers must build or integrate a plugin to achieve RAG functionality; the platform does not provide a built‑in knowledge‑base or WYSIWYG editor. Strengths of ChatGPT Enterprise include top‑tier language model performance, robust security, and the flexibility to integrate with a wide range of third‑party services. Its limitations for mortgage brokers are the lack of a visual editor for chat widgets and no native long‑term memory beyond the session. Brokers would need to handle authentication, memory persistence, and UI customization externally. In summary, ChatGPT Enterprise can serve as a high‑performance core for mortgage brokers who are willing to invest in plugin development and UI tooling.

Microsoft Azure OpenAI Service offers access to models such as GPT‑4 and GPT‑3.5. When combined with Azure Cognitive Search, brokers can create a retrieval‑augmented pipeline: the search service indexes loan documents, rate sheets, and regulatory guidelines, and the OpenAI model generates contextual responses. This setup supports real‑time data retrieval and can be integrated into a website via Azure Bot Service or custom front‑end code. Pricing for Azure OpenAI is token‑based: GPT‑4 (8‑k context) costs $0.03 per 1,000 prompt tokens and $0.06 per 1,000 completion tokens. Azure Cognitive Search starts at $0.20 per 1,000 documents indexed, with additional costs for query usage. Brokers must manage authentication, memory persistence, and UI design separately; the platform does not provide a WYSIWYG editor or a dual knowledge‑base out of the box. Strengths include deep integration with Microsoft’s enterprise ecosystem, robust compliance controls, and the ability to scale to high query volumes. However, the learning curve is steeper, and brokers need to build the RAG pipeline and front‑end UI themselves. Azure OpenAI + Cognitive Search is ideal for brokers with existing Azure infrastructure and the resources to develop a custom chatbot solution.

Google Gemini Enterprise provides access to the Gemini language model with enterprise‑grade security and integration with Google Cloud services. Mortgage brokers can pair Gemini with Vertex AI Search to construct a retrieval‑augmented chatbot that pulls the latest loan documents and regulatory updates. The platform supports token‑based pricing and offers sandbox environments for testing. Pricing for Gemini Enterprise is not publicly disclosed; brokers must contact Google Cloud sales for a custom quote. The platform does not include a WYSIWYG editor, built‑in knowledge‑base, or long‑term memory; brokers need to build these layers separately. Gemini Enterprise’s strengths lie in its integration with Google Workspace and Cloud AI services, which can simplify data ingestion from Google Docs or Sheets. However, the lack of a ready‑made RAG pipeline or UI builder means brokers must invest in development. For brokers who are already heavily invested in Google Cloud and require a flexible, high‑performance LLM, Gemini Enterprise can be a powerful core for a custom RAG chatbot.

Cohere offers a Retrieval‑Augmented Generation (RAG) API that allows developers to combine a large language model with a vector store for semantic search. Mortgage brokers can use Cohere’s RAG API to build a chatbot that retrieves relevant loan policy documents or rate sheets from a vector index and generates accurate, context‑aware responses. Cohere’s pricing is token‑based: $0.01 per 1,000 tokens for the base model. Vector search costs are $0.005 per 1,000 tokens. There is no publicly available WYSIWYG editor or long‑term memory; brokers must develop the UI and persistence layer themselves. Cohere RAG’s main advantages include a straightforward API, high‑quality language generation, and seamless integration with vector databases like Pinecone. Its limitations for mortgage brokers are the lack of a visual editor, no built‑in knowledge‑base management, and no assistant agent for automated insights. Cohere RAG is suitable for brokers who have developer resources to build a custom chatbot front‑end and storage layer.