Manufacturing facility managers were flying blind, relying on delayed reports and disconnected spreadsheets to monitor multi-line production status. A modern industrial operation needed a central nervous system. I designed and developed FABRIX AI — a high-performance, real-time command center that aggregates data across active production lines and delivers instant visibility into yield, OEE (Overall Equipment Effectiveness), and maintenance alerts.

The Challenge

Industrial environments generate massive amounts of data, but it's often siloed or presented in outdated, hard-to-read interfaces. The core challenge was twofold:

Data Aggregation: We needed to ingest live statuses, targets, and outputs from multiple concurrent production lines without latency.

Cognitive Load: Operators on the factory floor needed a UI that instantly communicated critical alerts (like yield drops or material shortages) without requiring them to dig through menus. The interface had to be deeply legible in low-light industrial environments while looking like a premium, modern SaaS product.

he FABRIX AI dashboard transformed raw factory data into an actionable, real-time narrative.

Facility managers now have a single pane of glass showing global OEE, total output, and granular line-by-line efficiency.

The high-contrast alert system dramatically reduces response times to yield drops and maintenance bottlenecks.

The fully responsive, single-page application structure allows the dashboard to be displayed on large factory floor monitors or accessed remotely by shift supervisors without losing visual hierarchy.

I built a workflow that turns a business website into a testable AI phone receptionist.

The system analyzes the website, extracts services, staff, opening hours, policies, and other relevant information, then creates an isolated voice-agent demo for that business. Instead of sending a generic recording, we can provide a real phone number that allows the business to test the agent through an actual call.

When the website or scheduling system supports an integration, the agent can check availability and book appointments during the call. Otherwise, it safely captures the caller’s details and preferred time for staff follow-up.

If a business decides to continue after the demo, the same foundation can be expanded into a production receptionist with databases, customer-specific business rules, calendar and CRM integrations, confirmations, call summaries, analytics, and administrative controls.

The extracted information can also reveal gaps in the business website. Its content and structure can then be improved so both visitors and the AI receptionist receive clearer, more reliable information.

The main lesson I share is that website extraction alone is not enough. Information must be normalized, validated, and restricted to the source material before it can be used safely in a live voice conversation.

The demonstration uses a fictional dental clinic and real recordings from my working voice-agent system. No patient data, real customer information, or invented testimonials are included.

I used Descript to assemble and refine the presentation, edit the narration and real-call sections, create captions, and prepare the final submission.