Hi, I’m Raian, and I’m one of the Junior Engineers here at LexData Labs. Most days, you’ll find me toggling between surveillance feeds, spreadsheets, and Power BI dashboards, all in the name of making sure real-world alert systems actually work.
It sounds a bit technical (and it is), but there’s something really satisfying about catching the false positives that machines miss or noticing when a real alert should have fired but didn’t. That kind of detail matters when safety and operations are on the line.
Here’s what a day in my alert validation life looks like not just the tools I use, but the little rituals and real moments that make this work feel grounded.
8:30AM - Morning Sync & Setup
My day officially starts with a client meeting at 8:30 AM. A short check-in where we align on what timeline, alert conditions, or systems we’ll be reviewing. It sets the tone and gives us clarity.
After that, I go through the usual setup:
Connect to the VPN
Fire up Power BI (our main alert tracking tool)
Log into Delinea for Secret Server access credentials
Open a fresh spreadsheet to document the day’s work
While everything loads, I usually sip my first coffee of the day (black, no sugar),and scroll through QA notes to mentally prep. By 9:15 AM, it’s go time.
9:30 AM – 2:00 PM The Real Work Begins
This is where I settle into my focus zone. The core of my day is spent reviewing surveillance videos to check whether specific Power BI alerts match what happened in real life.
Some examples of what I validate:
🔓 A backroom door left open too long
🧍♂️ Queues forming with not enough staff present
🚶♀️ Traffic patterns in certain store zones
Basically, I cross-reference what the system thought happened with what actually occurred in the footage. Did the alert fire too early? Too late? Or maybe not at all?
Each task usually takes 20–25 minutes. I log my findings, timestamps, and comments in the spreadsheet.
🎧 Small personal ritual: I listen to lofi beats or soft ambient playlists while reviewing footage, helps me stay focused without getting overwhelmed.
Midday Recharge Lunch, Walk, Reset
Around 1:00 PM, I take a proper break. I often grab lunch with buddies and eat outside, then step out for a quick walk, even just 15 minutes of fresh air clears my head. This role requires laser focus, so I've learned not to power through the whole day nonstop.
3:00 PM QA Round: Cleaning It Up
Late afternoon is all about quality assurance. This means double-checking timestamp accuracy, ensuring that alert conditions were correctly interpreted, and polishing up spreadsheet formatting.
We’re our own first reviewers and the second set of eyes often catch small things we miss during the rush. It’s a habit I’ve grown to appreciate.
Pro-Tip: I’ve made it a habit to freeze the top row in Excel, that way, column headers stay visible while I scroll through hundreds of entries. It sounds small, but when you're deep into timestamp checks, it saves serious time and prevents mix-ups.
4:30 PM Quick Reset
By late afternoon, my eyes start to protest so I take a quick reset. I usually grab my go-to snack: salted peanuts mixed with a few dark chocolate chips. Weird combo? Maybe. But it hits the sweet-salty balance just right and powers me through the final stretch.
5:00 PM Final Submission & Sign-Off
Before I log off, I bundle up everything:
✅ Total alerts validated
❌ Number of false or missed alerts
📊 Traffic count Slots
The final report gets shared with the client, backed with evidence and comments so they can easily review and act on it. And then… we’re ready to do it all again tomorrow!
Real Talk: Why Human Validation Still Matters
I get asked a lot: “Can’t software do all of this on its own?”
Short answer: Not yet. Not reliably.
Sure, automation has come a long way, but alerts are only as good as the data they’re trained on. That’s where we come in, people who notice subtle patterns, exceptions, and edge cases that machines miss.
Personally, I enjoy being the bridge between tech and real-world judgment.
Final Thoughts: The People Behind the Precision
What drives me? To put it simply: the idea that every alert we validate helps make a system more trustworthy.
Fewer false alarms translate to better safety protocols.
More accurate insights lead to smarter business decisions.
Whether it’s counting foot traffic or confirming a missed door alert, I know this work helps make AI systems a little smarter every day. And honestly, that feels very rewarding.
