Asguard
Real-time AI Fraud Detection for Fintech environments requiring mission-critical precision.
Asguard specializes in backend infrastructure and AI/LLM integration to deploy specialized models for document intelligence and automated fraud detection systems.
The Challenge
High-volume fintech platforms face the relentless pressure of processing millions of transactions daily. The core challenge was achieving sub-millisecond latency for complex fraud analysis without compromising accuracy or system throughput.
Maximum permissible delay per transaction: 450μs.
The Solution
We engineered a distributed AI architecture that leverages edge-compute nodes for immediate document triage. By integrating opensource OLlama models directly into a high-performance Node.js backend, Asguard filters noise before it hits the central ledger.
Technical Foundry
Ollama/Groq Cloud
Inference Engine
Node.js
High-Volume I/O
Python
Model Training
PostgreSQL
Data Persistence
Oracle Cloud
Infrastructure
Core Engines
The Asguard system operates on a dual-engine philosophy, balancing proactive protection with reactive deep-learning analysis.
Predictive Modeling
01Utilizes synthetic data generation and historical transaction patterns to predict fraudulent vectors before they materialize. This engine constantly evolves via unsupervised learning cycles.
Behavioral Analysis
02Analyzes individual user interaction patterns—ranging from typing speed to navigational quirks—to create a unique 'digital signature' that flags anomalies in real-time.
Measured success in high-load.
Reduction in Fraud
System Uptime