Operational Data Intelligence & AI Diagnostics
When equipment fails, the answer is usually buried in telemetry, event logs, and someone's memory of a similar case three years ago. We centralize that scattered data and put a diagnostic copilot on top of it — one that ranks likely causes and shows the evidence for each — then run it for you.
The data exists — it's just scattered
Most operations already collect more data than they use. The telemetry is in one system, the maintenance history in another, and the hard-won judgment about what a given fault pattern means is in the heads of a few senior people who are increasingly hard to replace.
Operational data intelligence brings those sources together and adds a diagnostic layer that reasons over them: what's likely wrong, what evidence points there, and which past cases look the same. The goal isn't a prettier dashboard — it's a faster, better-supported answer to “what do we do about this unit.”
What we build on operational data
Data ingestion and normalization
We pull telemetry, event logs, and operational records out of their silos and into one place the rest of the system can reason over.
Evidence-backed fault ranking
The copilot ranks likely causes and, for each one, shows the readings, log entries, and patterns that point there — so an engineer can agree or overrule with the facts in front of them.
Similar-case retrieval
Surfaces past incidents that look like the current one, turning institutional memory into something searchable instead of something that retires.
Trend monitoring and alerting
Watches for drift and emerging patterns over time, so problems are caught while they're cheap rather than after a failure.
Diagnostic systems you can explore
Furuno Diagnostics
A diagnostic copilot over marine-electronics telemetry that ranks faults and shows the evidence behind each.
Industrial Asset Health
Fleet-wide engine health monitoring with sensor trends, similar-engine comparison, and diagnostics.
Triage Workbench
AI-assisted triage of incoming operational issues, with evidence, audit trail, and a managed-ops view.
This is one of two lanes we work in. See the bigger picture in AI implementation consulting.
Frequently asked questions
What is operational data intelligence?+
Operational data intelligence is the practice of bringing together the data an operation already produces — equipment telemetry, event logs, maintenance records — and adding an analysis layer that helps people diagnose problems and make decisions. The emphasis is on actionable, evidence-backed answers about specific assets, not just charts.
Can AI diagnose equipment problems?+
AI can rank the most likely causes of a fault and lay out the supporting evidence — abnormal readings, relevant log entries, and similar past cases — far faster than manual investigation. A technician or engineer stays in the loop to confirm the diagnosis. It's decision support that compresses the investigation, not a black box that decides alone.
How does the AI explain its diagnosis?+
Every ranked cause comes with its evidence: the specific sensor readings, event-log entries, and historical cases that point to it. That transparency is deliberate — an engineer should be able to see why the system thinks what it thinks and overrule it when their judgment differs.
What data do we need to have for this to work?+
Usually some combination of equipment telemetry or sensor data, event or error logs, and maintenance or incident history. It doesn't need to be clean or in one place to start — consolidating and normalizing scattered sources is part of what we build. Discovery assesses what you have and what's worth using.
How is this different from a dashboard or BI tool?+
A dashboard shows you what's happening and leaves the interpretation to you. Operational data intelligence adds the interpretation: it proposes what's likely wrong, shows the evidence, and points to similar past cases — answering 'what do we do about this' rather than just 'here are the numbers.'
Will it work with our existing systems?+
That's the normal starting point. Operational data is almost always spread across several systems, and the ingestion layer is designed to pull from them rather than require you to replace anything. We map your sources during discovery.
Sitting on operational data you don't use?
Tell us what fails, how you find out, and where the data lives. We'll tell you whether a diagnostic copilot is worth building.
Describe your challenge