OSMIO, Intelligence Verified

Triq Technologies is developing an innovative AI orchestration platform called Osmio that fundamentally reimagines how organizations leverage artificial intelligence. Osmio uses a novel "Panel of Experts" approach to interact with AI technologies.áPhase I research will advance this technology from its current TRL 2 to demonstrate feasibility of three core technical capabilities: multi-model orchestration, automated validation and verification, and vendor-agnostic integration.áThe project addresses critical limitations in current AI implementations: poor specialized performance, operational rigidity, and complex integration requirements. Unlike existing solutions that rely on single large language models (LLMs), Osmio's architecture dynamically assembles and orchestrates multiple specialized AI models that work collaboratively. When a query arrives, the system automatically selects and combines relevant models based on their specific expertise and the query's context.Key technical objectives include:áDevelopment of a dynamic model assembly system that can automatically select and combine appropriate AI models based on query context and required expertiseCreation of an intelligent orchestration layer that enables multiple models to work collaboratively, cross-validate results, and refine outputs through multiple passesImplementation of an automated validation framework to detect and minimize AI hallucinations and inaccuraciesDesign of a vendor-agnostic integration framework that allows seamless switching between or combination of different AI providersDevelopment of a no-code visual pipeline builder that enables non-technical users to create complex AI workflowsThe technical approach leverages advanced methods in model orchestration, automated prompt optimization, and feature engineering. The system will include real-time testing capabilities and built-in mechanisms for continuous model improvement through automated training pipelines.Phase I research will focus on demonstrating the feasibility of these capabilities through prototype development and testing with beta partners across commercial and defense sectors. Success targets include a 30% improvement in accuracy compared to single-model approaches, 35% reduction in operational risk, 25% reduction in model life cycle, and Total Cost of Ownership reduction by 50%.