A Masterclass in FinTech Infrastructure: Deploying MavenPay’s AI-Native Microservices Core

Back

The Client

MavenPay is a Canada-based fintech company providing a global banking and cross-border payment platform for expats, international professionals, and businesses. Through its all-in-one app, users can send, receive, and spend in over 50 currencies with instant settlements and significantly lower fees than traditional banks.

The platform simplifies global money management by offering mass payouts, multi-currency invoicing, and crypto-to-fiat swaps. As a FINTRAC-registered and ISO-certified provider, MavenPay delivers a secure, fast, and borderless financial experience for the modern workforce.

Industry:Financial Services
Company Size:2-10
Country:Canada

We was particularly impressed with the depth of expertise, ease of communication and well-documented policies.

MavenPay Team

Challenges

Orchestrating a complex microservices architecture – The platform relied on a multi-model approach (integrating Cohere and Groq) with a split Next.js and Python/FastAPI stack, but lacked the enterprise-grade AWS environment required to securely host and orchestrate these microservices.
Establishing secure AI connectivity – To leverage foundational models via AWS Bedrock, the team needed to build the underlying networking and connectivity within their strict cloud perimeter rather than relying on external endpoints.
Navigating a tight time-to-market window – With the product not yet live, the customer was facing a strict 60–90 day deadline to implement the entire cloud architecture and successfully launch the service.
Bridging the infrastructure automation gap – Transitioning to a production launch required a professional deployment strategy, necessitating Terraform-backed infrastructure as code and robust CI/CD pipelines to ensure long-term scalability and repeatability.

The customer is in the process of developing a specialized AI-powered bookkeeping agent designed as a SaaS platform. To bring this product to market, they were facing several critical infrastructure and timeline challenges.

Solutions
MavenPay needed more than cloud infrastructure – they needed an automated, production-grade foundation capable of supporting complex AI workloads under a strict launch deadline. The solution was structured around three core pillars: automated infrastructure, containerized microservices, and secure AI connectivity.
1. Architectural foundation via automated Infrastructure as Code
  
  A scalable AWS foundation was built from the ground up using Terraform, establishing a secure networking layer (VPC) and the resource configurations needed to host the Next.js frontend and the Python/FastAPI backend.
2. Containerised microservices using ECS Fargate
  
  To support the microservices approach, the application layers were deployed using Amazon ECS with Fargate, removing the need for manual server management and allowing the bookkeeping agent to scale automatically based on processing demand.
3. Advanced AI connectivity through Amazon Bedrock
  
  Direct integration with Amazon Bedrock was enabled to provide access to the Nova Pro model, ensuring the AI agents have a low-latency, secure connection to high-performance foundational models within their own cloud environment.
4. Professional CI/CD pipeline
  
  A comprehensive GitHub Actions pipeline was implemented to automate the building and pushing of container images to Amazon ECR, allowing for seamless, rapid deployments to meet the 60–90 day launch window.
5. Security & Operational Governance
  
  The setup includes centralized secrets management via AWS Secrets Manager and automated budget alarms to ensure financial control and security from day one.

AWS Services Used

Amazon ECS (Fargate)
Application Load Balancer
Amazon RDS PostgreSQL
Amazon Bedrock
Amazon Route 53

AWS Systems Manager (SSM) Parameter Store
Private Bastion Host
AWS GuardDuty & AWS CloudTrail
AWS Budget Alarms
Terraform

Results

The completed architecture successfully transformed MavenPay’s local prototype into a deployable, production-grade SaaS platform. Shifting from manual configuration to a fully automated AWS environment gave the team documented, version-controlled infrastructure they could build on immediately – and a foundation capable of scaling as their user base grows.