AigosGate is an embedded multimodal guardrail system.
Designed to work within organizational boundaries, AigosGate functions as an integrated solution for input handling, output sanitization, data pipeline filtering and system-wide monitoring.
Embedded System
AigosGate works locally and within the boundaries of sensitive AI systems. Functioning as a dedicated system for input, output and data pipeline screening, all data stays within your system. Tap on a multitude of API endpoints to embed security and monitoring capabilities with minimal latency regardless of system design.
Any Model, Any Deployment
Easily connect AigosGate to work with any GenAI models hosted locally or via model-as-a-service platforms. Built to run across public and private cloud environments, AigosGate functions as a self-contained local service that fits in with any system design.
Consensus-based Detection
Our consensus-based detection engine addresses multiple dimensions of cyber, compliance and data privacy considerations. While no guardrail systems are foolproof, AigosGate’s content-aware guardrail capabilities safeguards your system against both known and novel risk scenarios.
Text, Documents and Images
Content and context aware screening of text, documents and images mitigate multimodal prompt injection, inappropriate responses, data-loss and data-poisoning attempts. Further tailor the system to optimize for your specific types of use cases.
System-wide GenAI Monitoring
Capture session data, dialog flows, incidents and performance metrics for your production system. Beyond observability, AigosGate’s monitoring capabilities makes it easy for AI engineers to evaluate and enhance system performance, while giving CISOs access to real time insights and data for forensic analyses and security audits.
Resource-light, Predictable Overheads
Secure your GenAI system with fixed and predictable overheads. AigosGate is resource-light and is designed for high throughput even with prior generation GPU and CPU resources.
Appropriate logging facilitates performance, cost and security analyses. We share three key categories of data and metadata every production RAG system should maintain within their logs.
Guardrails are key, but the diversity and complexity of input formats makes it challenging to put them in place, especially for multimodal systems.
Post-retrieval, a combination of filtering and reranking of search results help to refine, optimize and stack content for LLM operations.
