Zustis is an applied AI research company. We investigate how AI can understand, reason, and act in complex domains — then turn the strongest findings into working systems for real business problems.
Research-led. Domain-grounded. Tested in real workflows.
Research, applied
Generic models can look capable until the work depends on specialised language, connected knowledge, exceptions, and real consequences. Closing that gap demands more than implementation. It demands new methods.
Our research spans domain language, knowledge graphs, ontologies, neuro-symbolic reasoning, and validated agents. We apply it where existing AI stops being dependable.
The result is twofold: new knowledge that advances our research mission, and working systems that solve consequential business problems.
Where research creates value
Turn contracts, regulations, technical records, and multilingual documents into structured evidence your experts can review and act on.
Connect every recommendation to the source knowledge, rules, relationships, and exceptions your teams need to understand and audit each decision.
Turn repeatable expert judgment into governed workflows that surface risk, recommend actions, and improve through real feedback.
Our applied research model
A dependable system for your business and new evidence for our research. We begin with a consequential problem, investigate what existing AI misses, and build only what reality validates.
Bring us a problem worth researchingChoose the consequential decision, the evidence it requires, and the outcome that would prove the work useful.
Study the language, knowledge, reasoning, and constraints that existing systems fail to handle reliably.
Turn the findings into a focused system, test it with real users, and feed the evidence back into the research.
Evidence, not promises
Applied Research Case Study
How we shipped curlit to production in fourteen hours — a working developer utility, now live and open-sourced — using a heterogeneous dual-agent planner/executor/reviewer loop across Claude Opus 4.6 and GPT-5.4, supervised by a single human architect.
from blank repo to production deployment
frontier models in planner / executor / reviewer roles
human architect setting intent & arbitrating
"Research is not separate from real work.
Real work is where research proves what it knows."
Ask the harder question. Build the answer. Validate it against reality.
Work with Zustis
If the answer is not obvious and the outcome matters, we can investigate it together — and turn what works into a dependable system.