Platform / Corteq Cortex

The agentic AI platform for regulated missions.

Governed agents that execute real workflows on zero-trust foundations, with compliance engineered in rather than bolted on. Built to ship, to scale, and to clear review.

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Sys / Corteq Cortex

Class / Mission AI

Stack / LLM · RAG · Agents · CV

Posture / Zero Trust · NIST AI RMF

Status / Production

The problem

AI programmes in regulated environments do not fail on capability.

They fail at review. The model works in the demo, and then someone asks where the data went, who approved the action, what happens when the agent is wrong, and what evidence exists that any of it is under control. Those questions are not answered by a better model. They are answered by architecture — and by then the architecture is already fixed.

This is not a copilot.

A copilot suggests, and a human carries the risk. Cortex executes — inside your boundary, under explicit human authority, with every action traceable to its inputs and approvals. That difference is precisely what an authorizing official is assessing.

Platform architecture

Four layers, each one something a reviewer can inspect.

Read it top down: intent, then execution, then evidence, then the ground it all runs on.

Mission Graph

A semantic model of your decisions and workflows, not just your data. Agents reason over how the mission actually runs, so their actions map to real operational steps instead of being inferred from documents.

Intent

Agent Studio

Build and govern agents in one place: tools, permissions, escalation paths and human-in-the-loop gates defined before an agent is allowed near a production system.

Execution

Compliance-as-Code

Controls expressed as code and evaluated continuously, so the evidence an authorization package needs is produced by the system as it runs rather than assembled by hand at the end.

Evidence

Zero-Trust Substrate

Retrieval, inference and execution inside your security boundary. Your data does not leave it.

Ground

Where it runs

Inside the boundary, on the mission’s own ground — federal networks, health systems, and the classified and controlled environments in between.

The moat

Anyone can wire an agent to an API. Almost nobody can get one authorized.

General-purpose agent frameworks optimise for capability and speed of assembly. Neither is the constraint in a regulated mission. The constraint is what you can prove, to whom, and how long it takes.

Governance is upstream, not downstream

Permissions, escalation paths and human gates are declared before an agent runs, not documented after it ships. Retrofitting that onto a framework built without it means rebuilding the framework.

The evidence is a by-product, not a project

Because controls are expressed as code and evaluated continuously, the artifacts an authorization package needs accumulate while the system runs. Most teams start assembling them after the build, from memory.

Domain shape, not a blank canvas

Federal and healthcare workflows arrive with their own review vocabulary, data boundaries and failure consequences. Cortex is built around those, which is the part that cannot be bought as a licence.

The difference in practice

Same ambition. Different path to production.

The usual path

With Cortex

Pilot proves the model can do it

Pilot proves the workflow can be authorized

Security review begins after the build

Controls are declared before the first agent runs

Evidence assembled by hand, from memory

Evidence produced by the system as it operates

Human oversight described in a policy document

Human authority enforced in configuration

Data boundary argued in a diagram

Data boundary enforced by the substrate

Why it clears review

Four questions every reviewer asks, answered by the architecture.

Traceability by default. Every agent action carries its inputs, its tools, its approvals and its outputs. Audit is a query, not a reconstruction.

Human authority is explicit. Gates and hard stops are configuration, not convention, and they are reviewable before deployment.

Evaluation runs continuously. Red-teaming and drift monitoring are part of the pipeline, so evidence stays current after go-live.

Nothing leaves the boundary. The data-flow diagram a reviewer asks for is short, and it is the same one the system enforces.

Bring us the workflow that has not survived review.

We will walk through how Cortex would run it, what evidence it would produce, and what it would take to get it into production.

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Corteq Solutions is an AI-native engineering company for federal agencies and healthcare organizations. We design, deploy, and secure LLM, RAG, and agentic-AI systems that move missions from first pilot to authorized production.