Case Study - The AI bridge between any two systems

Overview

When Junctra approached us, they had a bold vision: create an integration platform where AI does the heavy lifting of connecting business systems. Traditional iPaaS solutions require extensive manual configuration, but Junctra wanted to flip that model entirely.

The platform needed to automatically analyze API schemas, generate connectors from documentation, and intelligently map fields between systems with different data structures. Most importantly, it needed to be self-healing—detecting when APIs change and automatically adapting.

We built a robust microservices architecture on AWS that could handle thousands of concurrent integrations while maintaining the low latency required for real-time data synchronization.

What we did

• Backend Architecture (Node.js, Python)
• AI/ML Pipeline
• API Gateway Design
• AWS Infrastructure

The self-healing capabilities Roamark built into our platform have been a game-changer. Our customers rarely experience integration downtime even when their connected APIs undergo changes.

Technical approach

The platform's intelligence stems from a multi-layered AI system. At the ingestion layer, we use natural language processing to parse API documentation and extract endpoint specifications, authentication requirements, and data models.

The mapping engine employs machine learning to suggest field mappings between systems, learning from corrections to improve accuracy over time. For the self-healing functionality, we implemented anomaly detection that monitors integration health and triggers automatic remediation workflows.

We chose an event-driven architecture using Apache Kafka for reliable message processing, with PostgreSQL for persistent storage and Redis for caching frequently accessed schemas.

Results

Junctra launched successfully and quickly gained traction with integration teams tired of maintaining brittle point-to-point connections. The platform now serves over 200 companies managing more than 50,000 integrations, with AI-generated connectors reducing initial setup time by 85% compared to traditional methods.