Research
Advancing the Science of Intent
Our research explores the frontiers of intent-driven AI, from retrieval-augmented generation to natural language interfaces that fundamentally change how humans interact with software.
Research Focus Areas
Intent Recognition & Classification
Understanding user intent from natural language input — beyond keyword matching to genuine comprehension of goals, context, and implicit requirements.
Retrieval-Augmented Generation
Advancing RAG architectures for enterprise environments: improving retrieval accuracy, reducing hallucination, and enabling domain-specific knowledge grounding.
Data Sovereign AI
Developing model training and inference techniques that operate entirely within customer infrastructure with zero external data dependencies.
Real-Time Compliance Intelligence
AI-driven compliance decision engines that process regulatory requirements and make enforcement decisions in sub-millisecond timeframes.
Recent Publications
Our team regularly publishes insights on AI implementation, architecture, and strategy.
White Paper
The Intent-Driven Solutions Framework
A comprehensive guide to designing software that understands user intent and delivers outcomes, not just interfaces.
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RAG at Enterprise Scale
Architecture patterns for deploying Retrieval-Augmented Generation in large organizations with complex data landscapes.
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Government AI Readiness 2026
Analysis of federal agency AI adoption, procurement challenges, and strategies for successful implementation.
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