Organizations today generate massive volumes of data from sensors, logs, and transactional systems. But without context, this data remains incomplete – disconnected numbers that are hard to interpret and harder to act on.
Maranics provides the missing link. By combining machine data with human input, we create context-rich datasets that AI agents and teams across domains can actually understand and use.
Why Context Matters
Machine data captures what happened. Human input explains why it happened, how it was handled, and under what conditions. Without this layer:
- AI agents misinterpret anomalies.
- Teams across functions lack a shared understanding.
- Critical decisions are made without traceability or rationale.
As we noted in our World Maritime Day reflections, “up to 95% of the most important operational data can only be captured by humans.”
Industry research reinforces this point. Human-in-the-loop data pipelines preserve context and accountability at points where automation alone falls short.
[..] data alone doesn’t yield valuable information. It takes people to analyze and interpret that data for insights – The Human Factor
Checklists as a Core Mechanism
One of the most effective ways we capture human data is through our checklist app.
- Template-driven: checklists can be defined once and instantly reused.
- Simple interface: easy for crews and operators to adopt.
- Instant updates: when processes change, checklists adapt immediately without code deployments.
- Structured by design: every entry is validated, timestamped, and enriched with metadata.
This flexibility makes Maranics a tool that can react instantly to process changes. Evidence from healthcare and aviation shows the same principle: digital checklists increase compliance and improve performance in high-risk settings (See: Comparing the Effects of Paper and Digital Checklists).
From Data to Understanding
By combining machine signals with human input, Maranics transforms fragmented data into a complete picture:
- Capture – systems log events, humans add context via checklists.
- Enrich – inputs are validated and tagged with metadata.
- Correlate – machine and human data are linked together.
- Trigger – contextualized data drives workflows and alerts.
- Interpret – AI and teams see meaning, not just numbers.
- Adapt – processes evolve instantly through updated templates.
The result: data that is not just collected but understood.
Use Cases
- Operations – reduce false alarms by pairing system alerts with human confirmation.
- Compliance – build auditable records enriched with human rationale.
- Cross-domain collaboration – share contextualized data across engineering, operations, and compliance.
- AI enablement – train and run AI agents on data that contains context, reducing false positives. Industry experience shows that human investigators are essential to avoid false positives in sensitive domains such as fraud detection (See: The Role of Human-in-the-Loop). Avoiding the “crap in, crap out” problem ensures AI delivers reliable results (See: Data Quality in AI).
See It in Action
Want to see how this works in practice? Our video library shows the platform in real-world scenarios, capturing human input, enriching machine data, and driving processes forward.
Looking Ahead
We continue to extend the platform with new capabilities:
- Hands-free interaction – enabling checklist use in constrained environments.
- Progress indicators – visualizing the status of multi-step operations.
- Image recognition – reading expiry dates on fire extinguishers, safety signs, and labels to automatically capture and contextualize information.
- Data usage visibility – showing where and how data points are reused across workflows.
Conclusion
Maranics bridges the gap between raw data and real understanding. By embedding human context into machine data through checklists, workflows, and automation, we turn isolated inputs into meaningful, actionable information.
Without context, data risks falling into the “crap in, crap out” trap. With Maranics, your data doesn’t just exist – it makes sense, drives better decisions, and powers AI that you can trust.
