By Stephen Biswas, AI Engineer, LexData Labs
Hi there! I’m Stephen Biswas, an AI Engineer at LexData Labs, where every day is a thrilling adventure in the world of artificial intelligence. I thrive on turning complex challenges into smart, elegant solutions, coding by day, brainstorming by night and always fueled by a passion for innovation and a good cup of tea!
Ahead is a glimpse into one of my typical days; a behind-the-scenes look at how I juggle teamwork, cutting-edge tech, and a few laughs while developing AI magic. Welcome to my world!
8:00 AM – 8:30 AM | Morning Team Huddle
My day starts with the classic team meeting where we put our heads together to chat about new ideas or sweep up any leftover tasks from yesterday. Sometimes I feel like we're detectives solving the mystery of "Where did the time go?" Half an hour flies by as we map out our grand plan to leverage AI technology… or at least nail this project.
8:30 AM – 9:45 AM | Claude Exploration and Snacks Ritual
After the meeting, I fire up Claude, our AI co-pilot and brainstorming partner. Today, my goal is to design and implement a face detection AI automation system that enhances our security measures while enabling a seamless smart-attendance platform. This initiative will help improve safety and make attendance management more efficient and reliable. But first, I take a crucial 15-minute break; because nothing says productivity like tea and snacks. I’m convinced those cookies have secret powers to boost coding creativity! (Or maybe it’s just the caffeine.)
9:45 AM – 11:00 PM | Coding & AI Adventuring
To uphold privacy, we begin with a small sample group of LexData Labs employees who are informed about the project in advance. With their consent, the system is tested exclusively on this group for facial recognition. Our approach remains focused, transparent, and voluntary, ensuring participants clearly understand the purpose. By embedding privacy-by-design principles, we prioritize ethical, compliant, and purpose-driven applications at every stage.
Face Detection for Anonymization / Redaction → Blurring or masking faces in video before data leaves the device.
Consent-Based Occupancy Counting → Measuring how many people are in a space without identifying who they are, and only with proper consent.
On-Device Processing → Sensitive data never leaves local hardware.
At LexData Labs, we believe innovation must go hand in hand with responsibility. Our updated privacy-first pipeline is built to protect individual rights while enabling AI systems that are both powerful and ethical; ensuring we innovate responsibly while staying aligned with GDPR, CCPA, and global privacy standards.
Video Ingest: Captures raw video frames from cameras.
Face Detection: Detects faces in real-time.
Secure Output: Displays employees’ names and ID numbers on the screen in real time.
We also evaluate how effectively our models balance speed and accuracy. The snapshot below highlights latency (in milliseconds) alongside precision (in %)
11:00 PM – 12:00 PM | Coding & AI Adventuring
I collect consented, carefully controlled datasets that include diverse angles and lighting conditions. Each image is another puzzle piece - not for recognition, but for building models that can reliably detect and anonymize.
Every new photo teaches the system something valuable: how to adapt to changes in light, how to maintain accuracy, and how to keep privacy protections intact. Over time, it evolved from being just another program into a responsible tool; one designed to enable insights without ever crossing into surveillance.
12:00 PM – 1:00 PM | Global Brainstorm with Our Overseas Wizards
Time for the internal team meeting with our remote specialists. Their feedback is like giving my code a power-up with the bonus of hearing funny accents and occasional "Can you hear me?" moments. It’s cool how technology lets me learn from these brainiacs thousands of miles away without leaving my chair.
1:00 PM – 2:00 PM | Lunch Break - Fueling Up for Round Two
I take a full hour to refuel. You’d think coding makes one hungry, but it’s really the snacks that disappear mysteriously during meetings that drive this habit.
2:00 PM – 4:30 PM | Training the AI (and Trying Not to Break It)
Back at it, I feed the pictures into the code and start training the model. I run tests, wait eagerly, then cross my fingers. One false move and my AI thinks everyone looks like a cat. (Don’t ask me how.) After a few tweaks, the model actually starts to recognize faces correctly and even remembers who’s a morning person and who’s a coffee fiend (kidding, but wouldn’t that be cool?)
4:30 PM – 5:00 PM | Wrapping Up and Final Checks
With time winding down, I double-check if I missed anything. If all looks good, I shut down the Claude and get ready to call it a day; feeling like a proud AI wizard who tamed the wild digital jungle for now. Tomorrow, I get to do it all again. Maybe the AI will even learn to say “Good morning” by then!
