See some examples of what COMET AI Assistant can answer in seconds, when the answer is already in your data. There is no limit to what you can ask.

Every HSEQ team asks itself the same questions after a serious failure. Are we seeing this pattern for the first time, or has it been sitting in our data for days, weeks, months or even years? The honest answer is usually the second one. The information exists, it is just spread across reports, spreadsheets and people's memories, which means nobody sees it until it is too late to act on it.

COMET AI Assistant's Insight AI changes what "seeing it" costs you. Instead of a team spending days pulling reports together, you ask a question in plain language and get an answer in seconds. These are a few that come up constantly, but they are examples, not a menu. If the answer is sitting somewhere in your investigation data, Insight AI can find it, whatever the question happens to be.

Are there recurring systemic issues hiding across our investigations?

Training gaps and equipment failures rarely show up as one big event. They show up as five small ones, each written up by a different investigator, each treated as its own story. COMET AI Assistant’s Insight AI compares root causes across every investigation in your portfolio, so the same failure showing up for the fourth time stops looking like a coincidence and starts looking like what it is.

The value here is straightforward. Fixing a training gap or a piece of equipment once, instead of writing up its consequences five separate times, is cheaper in every direction, fewer repeat incidents, less time spent investigating the same root cause, and a stronger case to leadership for the investment that actually solves it.

Which safety barriers most often fail or are missing in our incidents?

A barrier that fails once is an incident. A barrier that fails five times is a design problem. Insight AI tracks barrier performance across your whole portfolio, showing you which controls keep letting you down and which ones were never there to begin with. That is a very different conversation to have with your board than "we had another incident."

Knowing which barriers are weak lets you target capital and effort where it mostly reduces risk, rather than spreading investment evenly across controls that were never the problem. That is a stronger basis for a safety case, and a more defensible one if a regulator ever asks how you prioritised.

What are the common factors in our high potential near misses?

Near misses get logged and, too often, filed away. But your high potential near misses are telling you exactly where the next serious event is most likely to come from. Insight AI pulls contributing factors across near misses and high potential events together, so the vulnerabilities worth acting on quickly are not buried under the ones that can wait.

This is prevention at its cheapest point. Acting on a near miss costs a fraction of what a serious event costs, in injury, downtime, reputation or regulatory attention. Surfacing the right near misses fast is one of the highest return actions a safety team can take.

Which incident types are we investigating the most, and are they the ones costing us the most?

Teams often end up investigating whatever comes through the door loudest, not necessarily whatever is doing the most damage. A high volume of minor incidents can quietly absorb more investigation time than a smaller number of serious ones, without anyone deciding that on purpose. Insight AI compares incident type against frequency, severity and cost across your portfolio, so you can see where your team's time is going and whether that matches where the real risk sits.

This is a resourcing question as much as a safety one. If your busiest incident category is not your costliest one, that is a case for rebalancing where investigation effort goes, and a much stronger basis for a HSEQ lead to justify headcount or budget to leadership than "the team is busy."

What early warning patterns precede our most serious events?

Almost every serious event has a trail leading up to it. Smaller incidents, near misses, observations that on their own looked unremarkable. Insight AI compares timelines and contributing factors across your most serious events to surface the early signs that were there all along, so next time you catch them before the event, not after the report.

This is the question with the highest stakes attached. Catching an early warning pattern before the serious event it precedes is the difference between a near miss you learn from and a fatality you investigate. Everything else in this list supports operational efficiency. This one supports the case for the whole investigation function existing in the first place.

What are the common factors behind our quality failures or non-conformances?

Quality issues and non-conformances often get investigated in isolation from safety incidents, even though the same root causes, a skipped step, an unclear procedure, a piece of equipment out of calibration, tend to sit behind both. Insight AI pulls contributing factors across quality investigations alongside your safety data, so a pattern causing scrapped product or failed audits is not treated as unrelated to the pattern causing near misses on the same line.

The return here is often the easiest one to put a number on. Non-conformances have a direct cost, rework, waste, failed audits, customer complaints, in a way that is simpler to quantify than safety risk. Finding the shared root cause once, instead of running separate investigations into quality and safety versions of the same failure, cuts that cost and closes the gap faster.

All you have to do is ask

None of this requires building a dashboard, configuring a filter, or waiting on someone else's analysis. Insight AI works from questions asked in plain language, and answers pulled straight from your own investigation data. The intelligence was already there. Now it is accessible to whoever needs it, when they need it.

That changes who gets to ask the question in the first place. Right now, the answers to most of these questions sit with whoever has the time to pull the reports together, usually one person, usually not until someone senior asks for them. When anyone on the team can ask directly and get an answer in seconds, the pattern gets caught by whoever spots it first, not by whoever happens to be running the next quarterly review.

The six questions above are a starting point, not a limit. Every investigation you have ever logged, every near miss, every preventive action, every non-conformance, is a question waiting to be asked. The only thing that changes is how quickly you get the answer.

Want to see what Insight AI could find in your own portfolio? Book a demo of COMET AI Assistant