AI infrastructure and engineering for production systems

Run AI-powered systems with the controls production needs

Korex puts the architecture, monitoring, safeguards, and operating discipline in place so AI-enabled workflows can be used safely where reliability matters.

What we do

  1. AI & Operational Strategy — We identify where AI, automation, and better systems can create measurable business leverage.
  2. Custom Operational Systems — We design and build the platforms, integrations, workflows, and tools your business runs on.
  3. AI Infrastructure & Reliability — We put the architecture, monitoring, safeguards, and controls in place to run AI safely in production.
  4. Ongoing Operations & Ownership — We monitor, maintain, improve, and support the systems we build, staying accountable long after launch.

Common starting points

  • AI Readiness Audit — Assess workflows, data, systems, risks, and opportunities before committing to AI build work.
  • Internal Tools & Workflow Software — Build internal tools, workflow software, dashboards, and integrations for teams outgrowing manual workarounds.
  • Software Rescue & Stabilisation — Stabilise unreliable software, inherited systems, supplier handoffs, fragile integrations, and business-critical workflows.

What this covers

  • Design production architecture for AI-powered workflows and applications
  • Add monitoring, logging, alerting, and operational visibility
  • Put safeguards around prompts, outputs, data handling, and failure modes
  • Improve reliability across APIs, integrations, queues, databases, and deployment pipelines

When this is useful

  • AI features are moving from prototype into real customer or internal workflows
  • Systems need clearer monitoring, fallback paths, or operational controls
  • Teams are worried about quality, drift, data exposure, or unexpected model behaviour
  • The business needs AI capability without increasing production risk

What you get

  • A more dependable foundation for AI-enabled products and workflows
  • Clearer observability so issues are easier to detect, understand, and resolve
  • Practical controls that reduce operational, data, and reliability risk
  • Architecture that can scale without becoming fragile or opaque

How we work

  • We treat AI as part of a wider production system
  • We design for failure modes, not only ideal paths
  • We favour measurable reliability over vague confidence
  • We keep systems understandable enough to operate under pressure

Frequently asked questions

It covers work to design production architecture for AI-powered workflows and applications.