Engineering the systems between intelligence and enterprise.

Interas Research is building the foundation for the agentic enterprise. Not the agents themselves, but the infrastructure that makes them reliable, controllable, and ready to operate inside real companies.

Meet Vektor Work with us
The First Call · 11 seconds
01 · Our first product

Vektor is the AI agent control engine.

It sits between AI agents and enterprise systems, above the tools you already use; agents keep their own identity, gateway, and runtime, and Vektor gives you one place that answers six questions.

Which agents are operating?
Inventory
What can each agent do?
Authority
What is each agent doing?
Activity
What happened when nobody was watching?
History
Can I stop one agent without stopping everything else?
Intervention
Has it changed from what we approved?
Drift
Fig. 1 · The Vektor demo
A registered agent, a policy check, and the record of every call.

As enterprises give AI agents the authority to act, Vektor ensures that authority remains visible, bounded, and under human control.

Identity
Bring your own.
Gateway
Bring your own.
Runtime
Bring your own. Amazon Bedrock AgentCore on AWS; NVIDIA OpenShell.
02 · Where we work

The industries asking the hardest version of the question.

Not whether AI agents are coming, but what they are allowed to touch. We work where an agent's actions have to be accountable.

HealthcareAgents read patient records and shape care decisions, so every access has to be justified.
Utility techAgents act on grid and field operations, where one wrong action can mean an outage.
Public sectorAgents handle citizen data, and every decision has to stand up to an audit.
MediaAgents draft and publish under your name, so rights and approvals have to hold.
RetailAgents change prices, orders, and customer data at scale and in real time.
FinanceAgents move money and read accounts, and regulators expect a full record.
03 · The shift

AI is introducing probabilistic behavior into enterprise systems and workflows.

Older enterprise systems were deterministic: what they did traced back to code logic, and the system could be audited by reading it. Intelligence does not work that way. With models inside the enterprise, actions no longer trace to definitive logic, and that calls for a new kind of systems engineering.

The difficult problems increasingly aren't just inside the model. They appear when AI meets real organizations:

  1. 01Existing applications
  2. 02Fragmented knowledge
  3. 03Enterprise identity
  4. 04Permissions and policies
  5. 05Complex workflows
  6. 06Multiple clouds and runtimes
  7. 07Security requirements
  8. 08Human oversight

These are the problems Interas Research works on. We combine applied research with hands-on engineering to understand these gaps, build solutions against real enterprise workloads, and turn repeatable solutions into products.

04 · What we believe Vision

A future where intelligence empowers people and organizations while authority remains firmly human.

Mission

To develop the engineering principles, systems, and standards that keep increasingly capable intelligence under deliberate human authority.

We surround non-deterministic intelligence with deterministic systems.

05 · Contributors

What authority are we prepared to delegate to intelligence?

We are looking for contributors.

Interas Research works with engineers, researchers, companies, and teams exploring the systems around increasingly capable intelligence.

If you are working on agent infrastructure, identity, authorization, runtime controls, AI security, observability, or human oversight, and confronting the same questions, we would like to hear from you.

Bring us the difficult version of the problem.

vektor@interaslabs.com