How to Improve Asset Data Quality with Rule-Based QA

I like to think I’ve got a pretty sharp eye for detail.

But even with years of experience, I know I’d still miss things if I were back out in the field, auditing thousands of square metres of complex facilities.

Not because I’m not good at my job. Not because I don’t care. But because when you’re deep inside a large-scale asset audit, like a prison or an emergency department, there’s a lot going on.

You’re walking through unfamiliar buildings. Taking photos. Checking systems. Working to a tight schedule. Often the site’s still in use. Sometimes someone’s shadowing you. You’re trying to stay focused, but there’s a lot competing for your attention.

It’s exhausting, mentally and physically.

And when you’re looking at thousands of assets over long days, even the best auditors can’t stay sharp every second.

That’s why we don’t rely on memory or effort alone.

At Kairos, we’ve built rule-based QA into the process — not just at the desktop review stage, but during capture, right there on site.

It helps us stay accurate, even when we’re tired. It supports good auditors on tough days. And it catches the small things, before they become big problems.

It’s Like a Checklist, But Smarter

Surgeons use checklists. So do pilots.

Not because they’re inexperienced. But because, in high-stakes environments, you want to rely on a system, not just memory.

Now, maybe a building condition audit isn’t heart surgery or an emergency landing.

But I can tell you this.

If you’re running asset capture for a large healthcare site or a justice facility, the stress is real, the expectations are high, and mistakes have real consequences.

A single oversight can distort a lifecycle model, blow out a maintenance forecast, or lead to a CapEx decision based on flawed data.

That’s why we’ve baked rules and logic into Audify, our proprietary audit platform.

And it works.

Let me walk you through a few examples:

• Missing relationships
  If we capture a reciprocating chiller that’s water-cooled, the system checks whether a cooling tower is present. If it’s not, we get a flag.
• Suspicious age vs condition
  If an asset is 20 years old and recorded as perfect condition, something’s off. Either the condition is wrong or this asset has aged abnormally well, both are worth double-checking.
• Mismatch in expected quantities
  If a project specifies QR codes should be 10 characters, and one turns up with eight, we flag it. Shadows and poor scanning conditions can throw off capture. Better to catch it now than rely on it later.
• Suspiciously low volumes in large buildings
  Two fire doors in a multi-storey commercial site? That’s probably not right.
• Cost mismatch flags
  We once had 700 solar panel inverters accidentally added instead of 700 panels. The rule picked it up by comparing the total replacement cost against the expected asset value. That saved a serious headache.
• Wall height logic
  If a room has 50 square metres of floor space and a standard wall height of 2.7 metres, we can estimate total wall surface area. Subtract the area for doors and windows, and we can check whether the captured wall data makes sense. If not, we investigate.

These are just a handful. We’re building new ones all the time, especially when we see edge cases or trends in certain environments.

It’s Not Just About Errors, It’s About Learning

One of the biggest benefits?

We track which rules get triggered, how often, and by whom.

That gives us a feedback loop we can use to:

• Pinpoint training needs at an individual level
• Adapt our approach for new environments or asset types
• Continuously improve the platform, not just the data

It also means we can reduce QA time by up to 80 percent, because we’re working by exception, not line-by-line.

And when you’re capturing 50,000+ assets, that makes a huge difference.

Turn Quality Into a Competitive Edge

You can’t scale quality on gut feel.

Not when you’re managing thousands of assets, across hundreds of sites, with teams under pressure to move fast.

The smartest operators know this. That’s why they systemise quality using rule-based QA checks built into their tools and workflows.

That’s exactly what we’ve done with Audify.

It doesn’t just catch errors. It prevents them. It doesn’t just save time. It gives your team the confidence that what they’re capturing is accurate, complete and actionable.

Because at the end of the day, better data means better decisions. And better decisions build better businesses.

If that’s the direction you’re heading with your asset management strategy, we should talk.