The machine didn't fail.
The operator deviated.

Technical software knows machine state.
OWI understands workflow state.
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The problem nobody tracks

Your instruments are smart. They monitor temperature, pressure, reagent levels, error codes. They know their own health.

But they have no idea whether the operator skipped a validation step, rushed through a review, or approved results without checking them.

When something goes wrong, the support call says "machine is broken." The real cause: a procedural deviation that nobody saw.

  • Operator skips reagent verification. Calibration drifts. Results are wrong.
  • Operator approves results in under 1 second. Out-of-tolerance data passes.
  • Operator retries calibration 4 times. Root cause: expired reagent lot, not machine issue.
  • Operator cancels mid-run and restarts. Machine enters confused state.
  • Support engineer dispatched. Finds nothing wrong with the machine.

How OWI works

01

Observe

OWI monitors operator interactions with your technical software in real-time.

02

Compare

Each action is checked against predefined workflow definitions and expected timing.

03

Detect

Skipped steps, rushed approvals, repeated retries, and procedural shortcuts are flagged immediately.

04

Act

Alerts, workflow risk scores, drift analytics, and training recommendations are generated automatically.

See it working

The OWI Test Lab is a simulated operational environment demonstrating workflow deviation detection in a real-time lab calibration scenario.

Four views: fleet overview, workflow replay, procedural drift analytics, and business impact.

Explore the Live Simulation

No login required. Simulated data only.

Measurable operational impact

12
Support tickets prevented / month
$2,400
Reagent waste avoided / month
8
Sample re-runs avoided / month
$38k
Estimated annual savings
Figures are estimated projections based on a simulated five-instrument fleet. Actual impact varies by deployment.

Built by an instrument engineer, not an AI startup

OWI was created by someone who spent 20+ years developing firmware and operator software for biochemistry analyzers — and who watched operators deviate from correct procedures in every way imaginable.

The workflow models are not generic AI. They encode real operational knowledge: which shortcuts cause downstream failures, which retry patterns indicate a root cause the operator is not seeing, which skipped steps lead to support calls days later.

Rule-based detection. Deterministic. Explainable. Auditable.
Works with existing software. No source code changes required.

Interested in a pilot?

We're looking for technical software companies where operator workflow deviations cause real operational cost. If that sounds familiar, let's talk.

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