AtDeutsche Bank Research, the core mission of our analysts is delivering original, independent economic and financial analysis. However, creating research reports and notes relies heavily on a foundation of painstaking manual work. Or at least that was the case until generative AI came along.
Historically, analysts would sift through and gather data from financial statements, regulatory filings, and industry reports. Then, the true challenge begins — synthesizing this vast amount of information to uncover insights and findings. To do this, they have to build financial models, identify patterns and trends, and draw connections between diverse sources, past research, and the broader global context.
As analysts need to work as quickly as possible to brin…
AtDeutsche Bank Research, the core mission of our analysts is delivering original, independent economic and financial analysis. However, creating research reports and notes relies heavily on a foundation of painstaking manual work. Or at least that was the case until generative AI came along.
Historically, analysts would sift through and gather data from financial statements, regulatory filings, and industry reports. Then, the true challenge begins — synthesizing this vast amount of information to uncover insights and findings. To do this, they have to build financial models, identify patterns and trends, and draw connections between diverse sources, past research, and the broader global context.
As analysts need to work as quickly as possible to bring valuable insights to market, this time-consuming process can limit the depth of analysis and the range of topics they can cover.
Our goal was to enhance the research analystexperience and reduce the reliance on manual processes and outsourcing. We created DB Lumina — an AI-powered research agent that helps automate data analysis, streamline workflows, and deliver more accurate and timely insights – all while maintaining the stringent data privacy requirements for the highly regulated financial sector.
“The adoption of the DB Lumina digital assistant by hundreds of research analysts is the culmination of more than 12 months of intense collaboration between dbResearch, our internal development team, and many others. This is just the start of our journey, and we are looking forward to building on this foundation as we continue to push the boundaries of how we responsibly use AI in research production to unlock exciting new innovations across our expansive coverage areas.” -Pam Finelli, Global COO for Investment Research at Deutsche Bank
Creating AI-powered research experiences
DB Lumina has three key features that transform the research experience for analysts and enhance productivity through advanced technologies.
1. Gen AI-powered chat
DB Lumina’s core conversational interface enables analysts to interact with Google’s state-of-the-art AI foundation models , including the multimodalGemini models. They can ask questions, brainstorm ideas, refine writing, and even generate content in real time. Additionally, the chat capability supports uploading and querying documents conversationally, leveraging prior chat history to revisit and continue previous sessions. DB Lumina can help with tasks like summarization, proofreading, translation, and content drafting with precision and speed. In addition, we implemented guardrailing techniques to ensure the generation of compliant and reliable outputs.
2. Prompt templates
Prompt Templates offer pre-configured instructions tailored for document processing with consistent, high-quality outcomes. These templates enable analysts to facilitate the summarization of large documents, extraction of key data points, and the creation of reusable workflows for repetitive tasks. They can be customized for specific roles or business needs, and standardized across teams. Analysts can also save and share templates, ensuring more streamlined operations and enhanced collaboration. This functionality is made possible byGoogle’s long context window combined with advanced prompting techniques, which also provide citations for verification.
3. Knowledge
DB Lumina integrates a Retrieval-Augmented Generation (RAG) architecture that grounds responses in enterprise knowledge sources, such as internal research, external unstructured data (such as SEC filings), and other document repositories. The agent enhances transparency and accuracy by providing inline citations and source viewers for fact-checking. It also implements controlled access to confidential data with audit logging and explainability features, ensuring secure and trustworthy operations. Using advanced RAG architecture, supported by Google Cloud technologies, enables us to bring generative capabilities to enterprise knowledge resources to give analysts access to the latest, most relevant information when creating research reports and notes.
DB Lumina architecture
DB Lumina was designed to enhance Deutsche Bank Research’s productivity by enabling document ingestion, content summarization, Q&A, and editing.
Built on Google Cloud, the architecture leverages the following services:
Google Kubernetes Engine (GKE) for microservice orchestration
Cloud SQL with thepgvector extension for vector support
Cloud Storage for managing and storing unstructured data
Dataflow for document ingestion and embedding
Vertex AI for powering multimodal AI capabilities with Gemini
Discovery Engine API to enable RAG
Cloud Natural Language APIs for text and content moderation All of DB Lumina’s AI capabilities are implemented with guardrails to ensure safe and compliant interactions. We also handle logging and monitoring withGoogle Cloud’s Observability suite, with prompt interactions stored in Cloud Storage and queried through BigQuery. To manage authentication, we use Identity as a Service integrated with Azure AD, and centralize authorization through dbEntitlements.
RAG and document ingestion
When DB Lumina processes and indexes documents, it splits them into chunks and creates embeddings using APIs likeGemini Embeddings API. It then stores these embeddings in a vector database likeVertex AI Vector Search or the pgvector extension on Cloud SQL. Raw text chunks are stored separately, for example, inDatastore or Cloud Storage.
These diagrams below show the typical RAG and ingestion patterns:
Overview of the agent.
When an analyst submits a query, the system then routes it through a query engine. A Python application leverages an LLM API (Gemini 2.0 and 2.5) and retrieves relevant document snippets based on the query, providing context that is then used by the model to generate a relevant response. The sources indicate experimentation with different retrievers, including one using the pgvector extension on Cloud SQL for PostgreSQL, and one based on Vertex AI Search.
User interface
Using sliders in DB Lumina’s interface, users can easily adjust various parameters for summarization, including verbosity, data density, factuality, structure, reader perspective, flow, and individuality. The interface also includes functionality for providing feedback on summaries.
An evaluation framework for gen AI
Evaluating gen AI applications and agents like DB Lumina requires a custom framework due to the complexity and variability of model outputs. Traditional metrics and generic benchmarks often fail to capture the needs for gen AI features, the nuanced expectations of domain-specific users, and the operational constraints of enterprise environments. This necessitatesa new set of gen AI metrics to accurately measure performance.
The DB Lumina evaluation framework employs a rich and extensible set of both industry-standard and custom-developed metrics, which are mapped to defined categories and documented in a central metric dictionary to ensure consistency across teams and features. Standard metrics like accuracy, completeness, and latency are foundational, but they are augmented with custom metrics, such as citation precision and recall, false rejection rates, and verbosity control — each tailored to the specific demands and regulatory requirements of financial research and document-grounded generation. Popular frameworks like Ragas also provide a solid foundation for assessing how well our RAG system grounds its responses in retrieved documents and avoids hallucinations.
In addition, test datasets are carefully curated to reflect a wide range of real-world scenarios, edge cases, and potential biases across DB Lumina’s core features like chat, document Q&A, templates, and RAG-based knowledge retrieval. These datasets are version-controlled and regularly updated to maintain relevance as the tool evolves. Their purpose is to provide a stable benchmark for evaluating model behavior under controlled conditions, enabling consistent comparisons across optimization cycles.
Evaluation is both quantitative and qualitative, combining automated scoring with human review for aspects like tone, structure, and content fidelity. Importantly, the framework ensures each feature is assessed for correctness, usability, efficiency, and compliance while enabling the rapid feedback and robust risk management needed to support iterative optimization and ongoing performance monitoring. We compare current metric outputs against historical baselines, leveraging stable test sets, Git hash tracking, and automated metric pipelines to support proactive interventions to ensure that performance deviations are caught early and addressed before they impact users or compliance standards.
This layered approach ensures that DB Lumina is not only accurate and efficient but also aligned with Deutsche Bank’s internal standards, achieving a balanced and rigorous evaluation strategy that supports both innovation and accountability.
Bringing new benefits to the business
We developed an initial pilot for DB Lumina withGoogle Cloud Consulting, creating a simple prototype early in the use case development that used only embeddings without prompts. Though it was later surpassed by later versions, this pilot informed the subsequent development of DB Lumina’s RAG architecture.
The project transitioned then through our development and application testing environments to our production deployment, eventually going live in September 2024. Currently, DB Lumina is already in the hands of around 5,000 users across Deutsche Bank Research, specifically in divisions like Investment Bank Origination & Advisory and Fixed Income & Currencies. We plan to roll it out to more than 10,000 users across corporate banking and other functions by the end of the year.
DBLumina is expected to deliver significant business benefits for Deutsche Bank:
Time savings: Analysts reported significant time savings, saving 30 to 45 minutes on preparing earnings note templates and up to two hours when writing research reports and roadshow updates.
Increased analysis depth: One analyst increased the analysis in an earnings report by 50%, adding additions sections by region and activity, as well as a summary section for forecast changes. This was achieved through summarization of earnings releases and investor transcripts and subsequent analysis through conversational prompts.
New analysis opportunities: DB Lumina has created new opportunities for teams to analyze new topics. For example, the U.S. and European Economics teams use DB Lumina to score central bank communications to assess hawkishness and dovishness over time. Another analyst was able to analyze and compare budget speeches from eight different ministries, tallying up keywords related to capacity constraints and growth orientation to identify shifts in priorities.
**Increased accuracy:**Analysts have also started using DB Lumina as part of their editing process. One supervisory analyst noted that since the rollout, there has been a noted improvement in the editorial and grammatical accuracy across analyst notes, especially from non-native English speakers.
Building the future of gen AI and RAG in finance
We’ve seen the power of RAG transform how financial institutions interact with their data. DB Lumina has proved the value of combining retrieval, gen AI, and conversational AI, but this is just the start of our journey. We believe the future lies in embracing and refining the “agentic” capabilities that are inherent in our architecture. We envision building and orchestrating a system where various components act as agents — all working together to provide intelligent and informed responses to complex financial inquiries.
To support our vision moving forward, we plan to deepen agent specialization within our RAG framework, building agents designed to handle specific types of queries or tasks across compliance, investment strategies, and risk assessment. We also want to incorporatethe ReAct (Reasoning and Acting) paradigm into our agents’ decision-making process to enable them to not only retrieve information but also actively reason, plan actions, and refine their searches to provide more accurate and nuanced answers.
In addition, we’ll be actively exploring and implementing more of the tools and services available within Vertex AI to further enhance our AI capabilities. This includes exploring other models for specific tasks or to achieve different performance characteristics, optimizing our vector search infrastructure, and utilizing AI pipelines for greater efficiency and scalability across our RAG system.The ultimate goal is to empower DB Lumina to handle increasingly complex and multi-faceted queries through improved context understanding, ensuring it can accurately interpret context like previous interactions and underlying financial concepts. This includes moving beyond simple question answers to providing analysis and recommendations based on retrieved information. To enhance DB Lumina’s ability to provide real-time information and address queries requiring up-to-date external data, we are planning to integrate a feature for grounding responses with internet-based information.
By focusing on these areas, we aim to transform DB Lumina from a helpful information retriever into a powerful AI agent capable of tackling even the most challenging financial inquiries. This will unlock new opportunities for improved customer service, enhanced decision-making, and greater operational efficiency for financial institutions. The future of RAG and gen AI in finance is bright, and we’re excited to be at the forefront of this transformative technology.
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