Intent recognition & classification
Understanding what people mean from natural language — beyond keyword matching to genuine comprehension of goals, context, and implicit requirements.
Research
Advancing the science of intent
Our research explores the frontiers of intent-driven AI — from retrieval-augmented generation to natural language interfaces that change how people interact with software, in their own words.
For fifty years, technology demanded that people learn its language. It’s time for technology to learn ours.The thesis behind our research
Our approach
Research grounded in real-world deployment
We do not research AI in the abstract. Every line of inquiry starts with a problem a real deployment surfaced — an enterprise workflow, a compliance decision, a natural-language request that a keyword match could never satisfy.
That discipline keeps the work honest. We measure outcomes rather than assert them, we build for scale and data sovereignty from the first prototype, and we let production feedback set the research agenda — not the other way around.
Where we focus
Research focus areas
Retrieval-augmented generation
Advancing RAG architectures for enterprise environments: improving retrieval accuracy, reducing hallucination, and grounding models in domain-specific knowledge.
Data-sovereign AI
Model training and inference techniques that operate entirely within customer infrastructure, with zero external data dependencies — sovereignty by default.
Real-time compliance intelligence
Decision engines that read regulatory requirements and make compliance decisions in real time — the research behind VeriAction, our independent compliance platform.
From the team
Recent publications
Our team regularly publishes insights on AI implementation, architecture, and strategy. Request access to a briefing below.
White Paper
The Intent-Driven Software Framework
A guide to designing software that understands what a person means and delivers the outcome — not just another interface to learn.
Request accessTechnical Brief
RAG at Enterprise Scale
Architecture patterns for deploying retrieval-augmented generation in large organizations with complex, distributed data landscapes.
Request accessIndustry Report
Government AI Readiness 2026
An analysis of federal agency AI adoption, procurement challenges, and strategies for successful, FedRAMP-aligned implementation.
Request accessInterested in collaborating on research?
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