Building a customer service agent for Financial Services (FSI) requires a unique balance of rigorous security, high availability, and the ability to process complex data in real time. Cloud SQL Enterprise Plus serves as the high-performance “brain” for this architecture, managing both transactional banking data and the vector embeddings needed for modern AI.
High-performance architecture for FSI Agents
This architecture utilizes a Retrieval-Augmented Generation (RAG) pattern, ensuring your bot provides accurate responses based on real-time financial data rather than general model knowledge.
- Orchestration layer: You can use Vertex AI Agent Builder to manage the conversation flow. It connects to your backend systems and handles user intents specific to banking, like “Check my balance” or “Explain this mortgage fee.”
- Vector and transactional storage: Cloud SQL Enterprise Plus acts as the unified storage. You can store sensitive customer transaction history alongside vector embeddings of your internal product PDFs and FAQ documents.
- Real-time embedding generation: Use the google_ml_integration extension (for PostgreSQL) or mysql.ml_embedding (for MySQL) to generate embeddings directly within your database. This eliminates the need to move sensitive FSI data to external services for processing.
- High availability: FSI applications cannot afford downtime. By using Enterprise Plus, you benefit from a 99.99% availability SLA and near-zero downtime for maintenance, ensuring the agent is available 24/7.
Data flow and security
In an FSI context, data privacy is paramount. This architecture keeps data within your secure cloud perimeter.
- User query: A customer asks, “How does the Premier Savings interest rate compare to my current account?”
- Context retrieval: The agent performs a vector search in Cloud SQL Enterprise Plus to find the latest “Premier Savings” product details. Simultaneously, it runs a standard SQL query to fetch the user’s “current account” interest rate.
- Secure grounding: The retrieved data is sent to a Gemini model on Vertex AI as “grounding” context.
- Verified response: The model generates a personalized, accurate response based only on the retrieved data, preventing hallucinations.
Recommended component stack
Get started
You can jumpstart this development by using the Cloud SQL Generative AI Jump Start solution. This template provides the boilerplate code to connect Cloud Run, Vertex AI, and Cloud SQL for a production-ready RAG application.
If you are new to Cloud SQL or want to test a proof of concept for a new AI Agent project, you can start with the 30-day free trial. This trial provides an 8 vCPU Enterprise Plus instance with 64 GB of memory and 100 GB of storage at no cost. It is the fastest way to see how high-performance SQL can speed up your time to market.
Precision at scale: Why your Financial AI agent needs Cloud SQL Enterprise Plus was originally published in Google Cloud – Community on Medium, where people are continuing the conversation by highlighting and responding to this story.
Source Credit: https://medium.com/google-cloud/precision-at-scale-why-your-financial-ai-agent-needs-cloud-sql-enterprise-plus-d512946e0141?source=rss—-e52cf94d98af—4
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