QuAIT / AI
We pick an industry, study its hardest problems, and build the AI that solves them.
Products, agents, and consulting — grounded in one industry at a time, so the AI we ship solves real operational problems rather than abstract ones.
Literature triage, live
Most of the corpus is not relevant to your question. The work is finding the few per cent that is.
An animation showing a scan across a body of research papers: a small fraction is identified as relevant and separated from the rest.
How QuAIT AI works
Not a generic AI consultancy. A repeatable model: depth in one industry compounds into products and licensable agents.
01
Choose an industry
We go deep on one domain — its operators, its data, its bottlenecks — before writing a line of code.
02
Build for its problems
Products and AI agents aimed at specific, measurable decisions inside that industry.
03
License what works
Proven agents and frameworks become products other organizations can buy and build on.
Every industry's hardest problem is an information problem wearing a costume.
Too much information, too little time, and a decision that has to be made anyway. Strip the domain language away and supply chains, clinics and research labs are all asking the same question: what, out of all this, actually matters right now? That is the question we build for.
Industries
Depth over breadth. These are the domains where we are building today.
Agent frameworks
The architectures underneath our products, offered to organizations that want to build on them rather than from scratch.
Research Agent Framework
The agent architecture underneath RACE AI: retrieval, credibility filtering, and multi-step research workflows, reusable for any domain with a large document corpus.
Industry Decision Agents
A pattern for embedding AI agents into a specific operational decision — forecasting, triage, routing — with human oversight built in.
Bring us your industry's hardest problem.
If it involves decisions made from too much information and too little time, it is probably a problem we want.