Studying the distance between
intelligence and the real world.
Our research is dedicated to the infrastructure problems that stand between AI and production.
Intelligence is not the bottleneck. Infrastructure is.
Models are getting smarter every quarter. But without the infrastructure to connect them to real systems, that intelligence cannot produce outcomes.
The gap between what AI can reason about and what it is permitted to do in the real world is not a model problem. It is an infrastructure problem. Data is siloed across hundreds of systems that were never designed to interoperate. Execution is ad-hoc. Governance is bolted on after the fact. The result is that the most capable intelligence on earth still cannot reliably close a loop in production.
Our research focuses on closing that gap. Connectivity across legacy systems. Semantic modeling of operational reality. Policy enforcement at every boundary. Deterministic execution with auditable proof of completion.
Models will keep getting better. But the rate at which that intelligence translates into real outcomes depends on infrastructure. How fast organizations can connect models to the systems where work actually happens. That is the problem space our research occupies.
The distance between a capable model and a reliable outcome is an infrastructure problem. That is what we study.
Five open problems. Five lines of research.
Connect
Making legacy systems intelligible
Enterprise data lives in hundreds of systems that were never designed to talk to each other. Our connectivity research studies how to make legacy infrastructure legible to intelligent software without requiring organizations to rip and replace what already works. The problem is normalization, translation, and streaming across system boundaries that were never designed to be crossed.
Model
A semantic ontology for the operational world
Raw data is not knowledge. We study how to give structure to operational reality so that software can reason about the world the way domain experts do. The research centers on versioned ontologies: entity types, link types, and action schemas where every object is typed, every relationship is explicit, and every change is tracked.
Govern
Policy and audit as infrastructure
Governance cannot be an afterthought bolted onto a system that was built without it. We research how to make policy evaluation and audit logging first-class infrastructure primitives. Policy checked before data is parsed. Audits written in the same transaction as state changes. If governance cannot be enforced, the operation does not proceed.
Act
Closing the loop deterministically
Most enterprise systems stop at observation. We study how to close the loop between signal and action deterministically. The problems here are idempotency, durable execution through an outbox, and auditable proof of completion. Side effects cannot escape the governed boundary.
Compile
The artifact supply chain
Operational intent today lives in email threads and ad-hoc scripts. We study how to compile that intent into immutable, pinned artifacts that can be versioned, tested, diffed, and rolled back like code. Ontology packages, policy bundles, action schemas, workflow definitions.
Decades of work. Weeks of time.
A clinical researcher today spends months navigating siloed patient records, fragmented lab systems, and inconsistent coding standards before a single hypothesis can be tested. If those data sources were connected, modeled into a coherent ontology, and governed by policy, the path from question to discovery collapses from quarters to weeks.
An energy operator managing a grid of aging infrastructure relies on manual inspections, disconnected SCADA systems, and reactive maintenance schedules. Connected sensor networks, modeled asset relationships, and governed autonomous actions turn predictive maintenance from a capital project into an operational capability.
A financial analyst tracing exposure across counterparties currently assembles data from dozens of internal systems and external feeds, reconciling formats by hand. A queryable, governed network of those relationships turns a quarter of analyst work into a single workflow.
These are the problems our research exists to solve.
Every line of research maps to a system.
We do not separate research from product. Each area of study feeds directly into the systems that organizations run in production. Production, in turn, generates the next set of research problems.
Axiom Kernel
The trust boundary. Four planes (Connect, Model, Govern, Act) that make intelligence operational inside governed reality.
Axiom OS
The management surface for the kernel. Where teams compile, review, promote, and deploy operational infrastructure.
Syntropic Intelligence
The AI platform. It reasons, recommends, and drives workflows. Axiom remains responsible for execution, policy, and audit at every boundary.
From healthcare to energy to government to finance. One kernel. Every domain.
Ready to build the future?
Let's discuss how Syntropic can help you build self-evolving, autonomous systems.
Headquarters
- Montreal
1234 Rue de la Montagne
Montreal, QC H3G 1Z1, Canada