CUSTOMER STORY · FINANCIAL GENERATIVE AI
FintechAI: Engineering StockGuru financial RAG on AWS.
Financial RAG · Amazon Bedrock · Production AIAT A GLANCE
The project at a glance.
FintechAI develops AI-driven financial-analysis experiences that let users interact with market and investment information through natural language. StockGuru required application infrastructure, controlled financial knowledge and a managed RAG workflow to operate as one system.
TitanBases deployed the AWS infrastructure and GenAI/RAG scope. The workload is confirmed as live in production, while SOW traffic, uptime and cost figures remain design criteria rather than public production measurements.
Solution implemented
- Deployed financial RAG with Amazon S3, Bedrock Knowledge Bases, Titan Text Embeddings V2 and OpenSearch Serverless.
- Established protected application compute, PostgreSQL data services, networking and load balancing.
- Added security, observability, testing and handover controls for ongoing production operation.
Delivery outcomes
- The end-to-end financial knowledge workload is live in production.
- Users can ask natural-language questions and retrieve context from controlled financial sources.
- No latency, accuracy, throughput or business-performance figure is asserted without final benchmark evidence.
ABOUT FINTECHAI
Financial AI needs data, models and infrastructure to operate as one governed system.
FintechAI applies artificial intelligence and data analysis to financial information, investment research and decision-support experiences. StockGuru brings that direction into natural-language question answering and recommendation-oriented workflows.
The TitanBases scope focused on the AWS workload around the product: application and data services, knowledge ingestion, vector retrieval, foundation-model access, security and operational handover.
Financial knowledge assistant
Retrieve controlled sources and answer financial questions in natural language.
Recommendation-ready foundation
Architecture can support future analysis and recommendation workflows.
Protected application stack
Networking, compute and PostgreSQL services support the product layer.
Governed AI operations
Security, monitoring and guardrails support controlled production use.
TitanBases delivered the AWS infrastructure, data and GenAI/RAG implementation for StockGuru, including Bedrock Knowledge Bases, OpenSearch Serverless, protected compute, observability and handover. Product-specific logic and content strategy remain controlled by FintechAI.
THE CHALLENGE
Financial knowledge · Retrieval · Application scale · Governance
A useful financial assistant needs more than foundation-model access.
StockGuru needed to connect natural-language requests with controlled financial knowledge and application data while keeping retrieval, generation and product responsibilities explicit.
The workload also required protected networking, observability, load-test readiness and a path from POC validation to production operation in a financial context.
TitanBases delivered AWS infrastructure, RAG orchestration, security, testing and handover under the SOW. The confirmed production state is not used to invent model-quality, response-time or business KPI claims.
ENGINEERING AT SCALE · PRODUCTION WORKLOAD
One financial workload, one controlled RAG path on AWS.
Source documents, embeddings, vector retrieval, model generation and the application tier are connected through explicit service boundaries.
Production status is confirmed by TitanBases. The SOW 99.9% availability criterion is not presented as measured production uptime.
THE SOLUTION
Application · PostgreSQL · S3 · Bedrock · OpenSearch
Connect application, data and Amazon Bedrock in a complete RAG workflow.
Users enter through Amazon Route 53 and an Application Load Balancer into protected compute. The application uses PostgreSQL for business data, while controlled financial documents are stored in Amazon S3.
Amazon Titan Text Embeddings V2 creates embeddings, OpenSearch Serverless stores the vector index and Bedrock Knowledge Bases retrieves relevant context before an Anthropic Claude model generates a response. Monitoring, guardrails and security controls complete the operating path.
SOLUTION ARCHITECTURE
AWS Architecture · StockGuru financial RAG
ORIGINAL HIGH-LEVEL DIAGRAM · FINTECHAI / STOCKGURU SOW
StockGuru application and financial RAG workflow
Route 53, load balancing and protected worker compute.
PostgreSQL application data and controlled source documents in S3.
Titan embeddings, OpenSearch Serverless and Bedrock Knowledge Bases.
Monitoring, security baseline, guardrails, testing and handover.
DELIVERY SUCCESS · PRODUCTION
The core building blocks for financial GenAI are live on AWS.
These outcomes describe implemented technical capabilities and confirmed production status without turning design criteria into unsupported operating KPIs.
FINANCIAL KNOWLEDGE
RAG
Financial documents, embeddings and vector retrievalMODEL PLATFORM
Bedrock
Managed model and retrieval orchestrationAPPLICATION FOUNDATION
AWS stack
Compute, data, networking and storage foundationOPERATIONS
Secure
Security, monitoring, testing and handover controlsThe supplied material does not include final response-time, retrieval-quality, throughput or business-impact benchmarks. None are inferred here.
TECHNOLOGIES
An AWS ecosystem for financial GenAI, retrieval and application operations.
Services are grouped from the confirmed StockGuru architecture and published case-study evidence.
APPLICATION AND NETWORKING
GENERATIVE AI AND RAG
DATA AND OPERATIONS
SECURITY AND GOVERNANCE
PRODUCTION · NEXT STEPS
Extend StockGuru with evidence from the live workload.
With approval, future revisions can add as-built retrieval quality, response time, data volume and feature-rollout evidence without changing the Golden Visual or the CMS-owned publication model.
CUSTOMER STORY · FINANCIAL AIRELATED CAPABILITIES
RELATED CASE STUDIES
Explore more stories.
RELATED INSIGHTS
engineering insight
AI for Financial Services in Vietnam: TitanBases Visits FintechAI and AWS in Ho Chi Minh City
A working visit to FintechAI in Ho Chi Minh City gave TitanBases a closer view of how AI initiatives in Vietnam's financial-services sector are evolving from individual use cases toward production-ready systems, data platforms, infrastructure, and enterprise integration.
Sep 25, 2026
engineering insight
MongoDB Atlas Vector Search for Large-Scale eKYC Deduplication: Lessons from a 10M-Vector POC
Large-scale identity matching requires more than fast vector search. This engineering insight examines latency, throughput, recall, index behavior and quantization trade-offs observed while evaluating MongoDB Atlas Vector Search for a 10-million-vector eKYC workload.
Sep 21, 2026
engineering insight
Buying ChatGPT Business in Vietnam: A Procurement Checklist for Teams
A practical procurement guide for teams in Vietnam comparing ChatGPT Business Standard and Premium pricing, seat planning, signup steps, privacy controls and onboarding support.
Nov 5, 2026
Prepared by TitanBases Engineering Team
TitanBases engineers share practical guidance from cloud, data and AI delivery work, with technical review focused on production architecture, security and operations.
YOUR NEXT WORKLOAD