Rocket Companies is a Detroit-based FinTech company with a mission to “Help Everyone Home.” Although known to many as a mortgage lender, Rocket’s mission extends to the entire home ownership journey from finding the perfect home, purchasing, financing, and using your home equity. Rocket has grown by making the complex simple, empowering clients to navigate the home ownership journey through intuitive, technology-driven solutions. Rocket’s web and mobile app brings together home search, financing, and servicing in one seamless experience. By combining data analytics and their 11PB of data with advanced automation, Rocket speeds up everything from loan approval to servicing, while maintaining a personalized touch at scale.

Rocket’s client-first approach is central to everything they do. With customizable digital tools and expert guidance from skilled mortgage bankers, Rocket aims to match every client with the right product and the right support quickly, accurately, and securely.

With the advent of generative AI, Rocket recognized an opportunity to go further. Buying a home can still feel overwhelming. This led Rocket to ask: How can we offer the same trusted guidance our clients expect at any hour, on any channel? The result is Rocket AI Agent, a conversational AI assistant designed to transform how clients engage with Rocket’s digital properties. Built on Amazon Bedrock Agents, the Rocket AI Agent combines deep domain knowledge, personalized guidance, and the ability to perform meaningful actions on behalf of clients. Since its launch, it has become a central part of Rocket’s client experience. Clients who interact with Rocket AI Agent are three times more likely to close a loan compared to those who don’t.

Because it’s embedded directly into Rocket’s web and mobile services, it delivers support exactly when and where clients need it. This post explores how Rocket brought that vision to life using Amazon Bedrock Agents, powering a new era of AI-driven support that is consistently available, deeply personalized, and built to take action.

Introducing Rocket AI Agent: A personalized AI homeownership guide

Rocket AI Agent is now available across the majority of Rocket’s web pages and mobile apps. It’s helping clients during loan origination, in servicing, and even within Rocket’s third-party broker system (Rocket Pro), essentially meeting clients wherever they interact with Rocket digitally.The Rocket AI Agent is a purpose-built AI agent designed to do more than answer questions. It delivers real-time, personalized guidance and takes action when needed. It offers:

• 24/7, multilingual assistance through Rocket’s website and mobile services
• Contextual awareness. Rocket AI agent knows what page the client was viewing and tailors its responses based upon this context
• Real-time answers about mortgage options, rates, documents, and processes
• Guided self-service actions, such as filling out preapproval forms or scheduling payments
• Personalized experiences using Rocket’s proprietary data and user context
• Seamless transitions to Rocket Mortgage bankers when human support is needed

Whether someone wants to know why their escrow changed or how to qualify for a refinance, Rocket AI Agent is designed to respond with clarity, confidence, and action.

Amazon Bedrock Agents

Amazon Bedrock Agents is a fully managed, cloud-based capability that customers use to quickly build, test, and scale agentic AI applications on Amazon Web Services (AWS). With built-in integrations and security, customers like Rocket use Amazon Bedrock Agents to accelerate from proof-of-concept to production securely and reliably. These agents extend foundation models (FMs) using the Reasoning and acting (ReAct) framework, allowing them to interpret user intent, plan and execute tasks, and integrate seamlessly with enterprise data and APIs much like a skilled digital assistant.

Agents use the FM to analyze a user’s request, break it into actionable steps, retrieve relevant data, and trigger downstream APIs to complete tasks. This allows Rocket AI Agent to move beyond passive support into proactive assistance, helping clients navigate complex financial processes in real time.Key capabilities of Amazon Bedrock Agents used in Rocket AI Agent include:

• Agent instructions – Set the agent’s objective and role (for example, a mortgage servicing expert), enabling goal-oriented behavior
• Amazon Bedrock Knowledge Bases – Provide fast, accurate retrieval of information from Rocket’s Learning Center and other proprietary documents
• Action group – Define secure operations—such as submitting leads or scheduling payments—that the agent can execute by interacting with Rocket’s backend services
• Agent memory – Memory retention allows Rocket AI Agent to maintain contextual awareness across multiple turns, enhancing user experience with more natural, personalized interactions.
• Amazon Bedrock Guardrails – Supports Rocket’s responsible AI goals by making sure that the agent stays within appropriate topic boundaries.

By combining structured reasoning with the ability to act across systems, Amazon Bedrock Agents empower Rocket AI Agent to deliver outcomes, not just answers.

How the Rocket AI Agent works: Architecture overview

The Rocket AI Agent is a centralized capability deployed across Rocket’s suite of digital properties, designed for scale, flexibility, and job-specific precision. At the core of its architecture is a growing network of domain-specific agents currently eight each focused on distinct functions such as loan origination, servicing, or broker support. These agents work together behind a unified interface to provide seamless, context-aware assistance. The following diagram shows the solution architecture.

Here are three foundational elements that shape Rocket AI Agent’s architecture:

1. Client initiation: The client uses the chat function within Rocket’s mobile app or web page
2. Rocket AI Agent API: Rocket’s AI Agent API provides a unified API interface to the agents supporting the chat functionality
3. Agent routing: The AI Agent API routes the request to the correct Amazon Bedrock agent based on static criteria, such as web or mobile property that the client entered the chat through, or the use of LLM-based intent identification
4. Agent processing: The agent breaks the task into subtasks, determines the right sequence, and executes actions and knowledge as it works
5. Task execution: The agent uses Rocket data in knowledge bases to find info, send results to the client, and perform actions to get work done
6. Guardrails: Enforce Rocket’s responsible AI policies by blocking topics and language that deviate from the goals of the experience
7. Prompt management: Helps Rocket manage a library of prompts for its AI agents and optimize prompts for particular FMs

This modular, scalable design has allowed Rocket to serve diverse client needs efficiently and consistently across services and across the homeownership lifecycle.

Impact and outcomes

Since launching Rocket AI Agent, we’ve seen transformative improvements across the client journey and internal operations:

• Threefold increase in conversion rates from web traffic to closed loans, as Rocket AI Agent captures leads around the clock even outside traditional business hours.
• Operational efficiency gains, particularly through chat containment. With the implementation of the AI assistant to support prospective clients exploring Rocket’s offerings, Rocket saw an 85% decrease in transfer to customer care and a 45% decrease in transfer to servicing specialists. This reduction in handoffs to human agents has freed up team capacity to focus on more complex, high-impact client needs.
• Higher customer satisfaction (CSAT) scores, with 68% of clients providing high satisfaction ratings across servicing and origination chat interactions. Top drivers include quick response times, clear communication, and accurate information, all contributing to greater client trust and reduced friction.
• Stronger client engagement, with users completing more tasks independently, driven by intuitive, personalized self-service capabilities.
• Greater personalization and flexibility. Rocket AI Agents adapt to each client’s stage in the homeownership journey and their preferences, offering the ability to escalate to a banker on their terms. This personalized support reflects Rocket’s core mission to “Help Everyone Home,” by meeting clients where they are and giving them the confidence to move forward.
• Expanded language support, including Spanish-language assistance, to better serve a diverse and growing demographic.

Rocket has deployed Rocket AI Agents across its digital services, including the servicing portal and third-party broker systems facilitating, providing continuity of experience wherever clients engage. By delivering consistent, on-brand support across these touchpoints, Rocket is transforming the way clients experience homeownership. Through the personalization capabilities of Amazon Bedrock Agents, Rocket can tailor every interaction to a client’s context and preferences bringing its mission to “Help Everyone Home” to life through scalable, intelligent engagement.

Lessons learned

Throughout the development and deployment of the Rocket AI Agent, the Rocket team uncovered several key lessons that shaped both its technical strategy and the overall client experience. These insights can serve as valuable guidance for other organizations building generative AI applications at scale:

• Curate your data carefully: The quality of responses generated by generative AI is closely tied to the quality and structure of its source data. Rocket built their enterprise knowledge base using Amazon Bedrock Knowledge Bases, which internally uses Amazon Kendra for retrieval across Rocket’s content libraries, including FAQs, compliance documents, and servicing workflows.
• Limit the agent’s scope per task: Rocket found that assigning each agent a tight scope of 3–5 actions led to more maintainable, testable, and high-performing agents. For example, the payment agent focuses only on tasks like scheduling payments and providing due dates, while the refinance agent handles rate simulations and lead capture. Each agent’s capabilities use Amazon Bedrock action groups with well-documented interfaces and monitored task resolution rates separately.
• Prioritize graceful escalation: Escalation isn’t failure, it’s a critical part of user trust. Rocket implemented uncertainty thresholds using confidence scores and specific keyword triggers to detect when an interaction might require human assistance. In those cases, Rocket AI Agent proactively transitions the session to a live support agent or gives the user the option to escalate. This avoids frustrating conversational loops and makes sure that complex or sensitive interactions receive the appropriate level of human care.
• Expect user behavior to evolve: Real-world usage is dynamic. Clients will interact with the system in unexpected ways, and patterns change over time. Investing in observability and user feedback loops is essential for adapting quickly.
• Using cross-Region inference from the start: To provide scalable, resilient model performance, Rocket enabled cross-Region inference early in development. This allows inference requests to be routed to the optimal AWS Region within the supported geography, improving latency and model availability by automatically distributing load based on capacity. During peak traffic windows such as product launches or interest rate shifts this architecture has allowed Rocket to avoid Regional service quota bottlenecks, maintain responsiveness, and increase throughput by taking advantage of compute capacity across multiple AWS Regions. The result is a smoother, more consistent user experience even under bursty, unpredictable load conditions.

These lessons are a reminder that although generative AI can unlock powerful capabilities, thoughtful implementation is key to delivering sustainable value and trusted experiences.

What’s Next: Moving toward multi-agent collaboration

Rocket is just beginning to realize the potential of agentic AI. Building on the success of domain-specific agents, the next phase focuses on scaling these capabilities through multi-agent collaboration powered by Amazon Bedrock Agents. This evolution will allow Rocket to orchestrate agents across domains and deliver intelligent, end-to-end experiences that mirror the complexity of real client journeys.

By enabling agents to work together seamlessly, Rocket is laying the groundwork for a future where AI not only responds to questions but proactively navigates entire workflows from discovery and qualification to servicing and beyond.

Benefits for Rocket

Multi-agent collaboration marks a transformative step forward in Rocket’s journey to build agentic AI–powered experiences that reimagine homeownership from the very first question to the final signature. By enabling multiple specialized agents to coordinate within a single conversation, Rocket can unlock a new level of intelligence, automation, and personalization across its digital services.

• End-to-end personalization: By allowing multiple domain-specific agents (such as refinance, servicing, and loan options) to share context and coordinate, Rocket can deliver more tailored, intelligent responses that evolve with the client’s homeownership journey in real time.
• Back-office integration: With agents capable of invoking secure backend APIs and workflows, Rocket can begin to automate parts of its back-office operations, such as document verification, follow-ups, and lead routing, improving speed, accuracy, and operational efficiency.
• Context switching: Move fluidly between servicing, origination, and refinancing within one chat.
• Orchestration: Handle multistep tasks that span multiple Rocket business units.

With multi-agent orchestration, Rocket is laying the foundation for a consistently-available, deeply personalized assistant that not only answers questions but drives meaningful outcomes from home search to loan closing and beyond. It represents the next chapter in Rocket’s mission to “Help Everyone Home.”

Conclusion

Rocket AI Agent is more than a digital assistant. It’s a reimagined approach to client engagement, powered by agentic AI. By combining Amazon Bedrock Agents with Rocket’s proprietary data and backend systems, Rocket has created a smarter, more scalable, and more human experience available 24/7, without the wait.

To dive deeper into building intelligent, multi-agent applications with Amazon Bedrock Agents, explore the AWS workshop, Unified User Experiences with Hierarchical Multi-Agent Collaboration. This hands-on workshop includes open source code and best practices drawn from real-world financial services implementations, demonstrating how multi-agent systems can automate complex workflows to deliver next-generation customer experience.

Rocket puts it simply: “Together with AWS, we’re getting started. Our goal is to empower every client to move forward with confidence and, ultimately, to Help Everyone Home.”

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About the authors

Manali Sapre is a Senior Director at Rocket Mortgage, bringing over 20 years of experience leading transformative technology initiatives across the company. She has been at the forefront of innovation—spearheading Rocket’s first-generation AI chat platform, building the company’s original digital mortgage application, and launching scalable lead generation systems. Manali has also led multiple AI-driven initiatives focused on banker efficiency and internal productivity, helping to embed smart, human-centric technology into the daily workflows of team members. Her passion lies in solving complex challenges through collaboration, mentoring the next generation of tech leaders, and creating intuitive, high-impact experiences. Outside of work, Manali enjoys hiking, traveling, and spending quality time with her family.

Seshidhar Raghupathi is a software architect at Rocket with over 12 years of experience driving innovation, scalability, and system resilience across AI and client communication platforms. He was instrumental in developing Rocket’s first cloud-based digital mortgage application and has since led several impactful initiatives to enhance intelligent, personalized client experiences. His expertise spans backend architecture, AI integration, platform modernization, and cross-team enablement. He is known for his ability to execute tactically while aligning with long-term strategic goals, particularly in enhancing security, scalability, and user experience. Outside of work, Seshi enjoys spending time with family, playing sports, and connecting with friends.

Venkata Santosh Sajjan Alla is a Senior Solutions Architect at AWS Financial Services, driving AI-led transformation across North America’s FinTech sector. He partners with organizations to design and execute cloud and AI strategies that speed up innovation and deliver measurable business impact. His work has consistently translated into millions in value through enhanced efficiency and additional revenue streams. With deep expertise in AI/ML, Generative AI, and cloud-native architectures, Sajjan enables financial institutions to achieve scalable, data-driven outcomes. When not architecting the future of finance, he enjoys traveling and spending time with family. Connect with him on LinkedIn.

Axel Larsson is a Principal Solutions Architect at AWS based in the greater New York City area. He supports FinTech customers and is passionate about helping them transform their business through cloud and AI technology. Outside of work, he is an avid tinkerer and enjoys experimenting with home automation.