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Iotron AI

Run & assure · AIOps, MLOps & AI-Powered DevOps

CI/CD, cloud and observability with AI: anomaly detection, predictive scaling, incident triage — plus the MLOps pipelines that keep models healthy.

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How we deliver

01What AI changes

Without AI built in

  • Alerts and tickets triaged manually, one at a time
  • Testing and monitoring that cannot tell when a model's behaviour drifts
  • Reporting assembled by hand at the end of each period

With AI inside

Modern systems produce more logs, metrics and traces than any team can read. AI finds the signal, correlates events and shortens the path from alert to fix.

AI proposes; engineers stay accountable.

Every AI-generated artefact — code, test, document or analysis — is reviewed by a named engineer before it reaches you, and AI tools are used with your data only under the terms agreed for the project.

02How we deliver

From first conversation to production.

AI assists every step; people own every decision.

Read our approach
  1. 1

    Define the decision

    Workshop to agree the business problem, success measures, users, data and constraints.

  2. 2

    Prove it fast

    Focused prototype on real data to test feasibility and value.

  3. 3

    Build for production

    Full solution: data pipelines, models, integration, security, interface, testing, deployment.

  4. 4

    Improve continuously

    Monitor performance, retrain, add features, support.

Common questions

What's the difference between AIOps and MLOps?

AIOps uses AI to run IT systems; MLOps operates AI models in production. We do both.

Do we need to change our monitoring tools?

Usually we build on them.

Get in touch

Ready to discuss AIOps, MLOps & AI-Powered DevOps?

Tell us what you want to build or improve. We will reply with an honest view of scope, approach and where AI genuinely adds value.