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AI-Staff Augmentation: AI-Powered Care Assistant Robot

TinyBots needed a stable, scalable engineering team to power Tessa across 150+ European care providers, InApps's IT Staff Augmentation delivered the cross-device platform behind the robot.

An InApps engineer at his desk in the open-plan Ho Chi Minh City office
Project overview

Digital Banking Transformation

NovaPay Financial Group is a mid-market neobank operating across Southeast Asia with 2M+ registered users. Founded in 2017, they grew rapidly but their legacy monolithic platform became a bottleneck: slow releases, high downtime, and a poor mobile experience were causing customer churn.

The competitive neobank landscape demanded a modern mobile-first experience. NovaPay's engineering team lacked the bandwidth and expertise to execute a full platform overhaul while keeping production stable. They needed a trusted technology partner to lead the rebuild.

Project goals

  • Rebuild core banking platform on cloud-native microservices architecture
  • Deliver iOS and Android apps with feature parity within 36 weeks
  • Integrate AI-powered fraud detection with <50ms transaction scoring
  • Achieve 99.9% uptime SLA post-launch
  • Reduce operational costs by at least 40%
What they needed

Client requirements

Across each key area, these requirements shaped every architectural and product decision we made.

Mobile Application

NovaPay needed a native mobile presence on both iOS and Android, with a seamless user experience that could compete with top-tier fintech apps in the region.

  • Native iOS and Android apps with shared business logic
  • Biometric authentication (Face ID, fingerprint)
  • Real-time transaction feeds and push notifications
  • Offline mode for balance viewing

Core Banking

The legacy monolith had to be replaced without downtime. NovaPay required a modern, event-driven backend that could scale independently per service.

  • Microservices architecture with clear service boundaries
  • Event-driven transaction processing
  • Multi-currency support (USD, VND, SGD, THB)
  • Automated reconciliation engine

AI & Security

Regulators and customers demanded real-time fraud protection and full data security compliance across all transaction flows.

  • Real-time fraud scoring under 50ms per transaction
  • Anomaly detection with weekly model retraining
  • PCI-DSS Level 1 compliance
  • End-to-end encryption for all data in transit and at rest

Infrastructure

The platform had to be resilient enough to handle rapid user growth and regional expansion without engineering intervention.

  • 99.9% uptime SLA with zero-downtime deployments
  • Auto-scaling to handle 10x peak traffic
  • Multi-region failover (Singapore + Ho Chi Minh City)
  • Full observability: logs, metrics, distributed tracing
The problem

Business challenges

The core obstacles that shaped every decision we made in rebuilding this platform.

01

Legacy Monolith

A 7-year-old Java monolith with no clear service boundaries. Any change risked breaking unrelated features, making iterative modernisation nearly impossible.

02

Zero Mobile Experience

No native mobile app and a mobile web score of 28/100 on Google Lighthouse, resulting in a 64% bounce rate on mobile devices.

03

Fraud & Security Gaps

Manual fraud review caused 4-6 hour delays in flagging suspicious transactions. Regulators demanded a real-time automated detection system.

04

Scalability Ceiling

Peak-hour traffic caused database connection exhaustion. The system could not scale horizontally without creating race conditions in the transaction ledger.

How we solved it

Our solutions

Each solution addresses a specific pain point while contributing to a unified, future-proof platform, from infrastructure to mobile to AI, every layer rebuilt with scale, security, and speed in mind.

Strangler Fig Migration

Incrementally replaced the monolith using the strangler fig pattern, routing traffic to new microservices behind a facade API gateway while keeping legacy services alive during transition.

React Native Cross-Platform App

Built a single React Native codebase sharing ~80% of business logic across iOS and Android, with native modules for biometric auth and performance-critical screens.

ML Fraud Detection Pipeline

Deployed a dual-model system on AWS SageMaker: a gradient-boosted tree for known patterns and an autoencoder for anomaly detection, scoring every transaction in under 50ms.

Event-Driven Architecture

Replaced synchronous API chains with Apache Kafka event streams, enabling horizontal scaling and eliminating the transaction race conditions that caused data inconsistencies.

Built with

Technology stack

Leveraging high-expertise engineering and battle-tested technologies, the InApps team delivered a scalable, production-ready solution built for long-term growth.

Frontend

React Native, TypeScript

Backend

Node.js, NestJS, Apache Kafka, GraphQL

Cloud

AWS ECS

Database

PostgreSQL, Redis, DynamoDB

Delivery approach

The experts behind it

Every successful delivery starts with the right people. InApps assembled a dedicated, cross-functional team structured for speed, accountability, and long-term partnership.

01 person

Project Manager

Owns delivery timeline, stakeholder communication, and risk management across all 6 phases.

01 person

Solution Architect

Designed the microservices architecture, API gateway, and event-driven Kafka topology.

04 people

Backend Engineers

Built core banking services: auth, accounts, transactions, notifications — each independently deployable.

02 people

Mobile Engineers

Delivered the React Native app for iOS & Android with shared business logic and native biometric modules.

02 people

DevOps / Cloud

Provisioned AWS infrastructure with Terraform, set up CI/CD, observability, and zero-downtime deployments.

02 people

QA Engineers

End-to-end testing, penetration testing, and PCI-DSS compliance validation across all services.

Measurable impact

What we delivered

2M+
Active users served
180ms
Avg transaction latency
99.9%
Uptime post-launch
40%
Operational cost saved

Key outcomes

  • Mobile app reached 4.8 rating on both App Store and Google Play within 60 days
  • Transaction processing latency reduced from 2.3s to 180ms average
  • Zero critical incidents in first 6 months post-launch
  • Engineering team onboarded to new platform in 3 weeks with full documentation
  • NovaPay expanded to 2 new markets within 4 months, using same platform
Within 90 days of launch, NovaPay recorded their highest-ever monthly active user count. The new fraud detection system caught 94% of fraudulent transactions automatically, reducing manual review workload by 85% and saving an estimated $2.3M annually in fraud losses.
NovaPay Financial Group
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