Transac AI project is geared towards generation of enriched summaries and insights from complex transactional data in real-time or batch using Generative AI and Large Language Models (LLMs).

Imagine fine-tuned LLMs serving as a specialists working several times more efficiently than multiple human analysts, and capable of analyzing hundreds of transactions per second to generate insights and ensure compliance, risk management, and fraud detection; all in real-time. This is the power of Transac AI.

Transac AI is built using a highly scalable, available, and reliable Kubernetes (opens in a new tab)-based distributed microservices & event-driven architecture. This is enabled through the use of low-latency and high-throughput inter-connections between services using gRPC (opens in a new tab), Apache Kafka (opens in a new tab), Connect RPC (opens in a new tab), and GraphQL (opens in a new tab).

Core services are built using Python, Node.js, and Go, and are deployed and managed efficiently using Terraform (opens in a new tab) on Google Cloud (opens in a new tab) and AWS (opens in a new tab) using services like the Google Kubernetes Engine (GKE) (opens in a new tab), AWS EventBridge Scheduler (opens in a new tab), AWS Lambda (opens in a new tab), Confluent Kafka (opens in a new tab), Supabase (opens in a new tab), and more.