# Upgrading a live platform across 185+ sites from RFID to AI computer vision

> How Narendra Gupta led a live platform upgrade from RFID to AI computer vision across 185+ sites, reaching 99% number-plate accuracy with no tags.

- **Role**: Delivery Manager
- **Company**: Lynkit Solutions
- **Clients**: JSW, CWC (180+ sites), Landmark CFS, DHL, Haier
- **Scale**: 185+ live sites
- **Team**: 6 (product, AI, dev, hardware, QA)
- **Timeline**: 2 months, pilot to all sites
- **Tech**: AI computer vision, RFID, IoT
- **Outcome**: 99% ANPR accuracy

## Context

The platform is an AI vision and IoT system for access automation and asset tracking. It runs across **185+ live sites** for clients including **JSW**, **CWC (180+ sites)**, **Landmark CFS**, **DHL** and **Haier**, and it was originally built on RFID.

As Delivery Manager at Lynkit Solutions, I own end-to-end delivery of 2 enterprise SaaS products, along with an **INR 5 Cr+ revenue portfolio**.

## Why we moved away from RFID

RFID worked, but it caused problems at the gate every day:

- Tags got lost, damaged or swapped between vehicles.
- Every site had to buy and issue tags, which added cost.
- Vehicles often arrived without a tag, so entry slowed down or had to be done by hand.
- Readers picked up the wrong tag, couldn't read some tag types, and sometimes missed tags that were right there.
- Each tag had to be mapped to a vehicle number by hand, and mistakes crept in.

## The challenge

Move the platform's recognition from RFID to **AI computer vision** while it stayed live at every site, and add new capabilities on top:

- AI-based **number-plate recognition**
- AI-based **ID recognition**
- **Damage detection**

Every site had to keep running through the change, and every client needed to know what was changing and when.

## My role

- Defined the requirements and scope of the upgrade.
- Coordinated engineering, QA and site teams.
- Managed client communication through the change.
- Owned escalations, change requests and SLA adherence across all live sites.

## How we did it

- **Pilot first.** We started with one pilot site before rolling out anywhere else.
- **RFID and AI side by side.** At the pilot, both systems ran in parallel, so the gate never depended on the new system alone while we checked its accuracy.
- **A small team of six:** me, a product manager, an AI developer, a developer, a hardware engineer and a tester.
- **Two months** to implement it across all sites, from the pilot to the last rollout.

## The hardest part

Number plates. Plate formats vary, and some characters look almost the same to a camera: 1 and I, Q, O and 0, M and W, V and Y. We annotated training images specifically for these look-alike characters, so the model learned to tell them apart.

## Outcome

- **No tags needed.** Vehicles are recognised by their number plates, so sites no longer buy, issue or track RFID tags.
- **99% ANPR accuracy** on number-plate reading.
- **No more manual entry or mapping errors** at the gate.
- **Faster and more accurate** entry for every vehicle.
- **The client asked us to roll it out to all their sites** and to move to new ANPR-based hardware.