CLIENT SUCCESS STORY

How SunTec Data helped tackle counterfeits and brand abuse with advanced data support

In what has been one of the fastest-scaling projects for us - with a team that grew from six to over a hundred people within half a year - we helped a digital revenue recovery company save hundreds of millions for its clients.

THE CLIENT

An AI solution to combat counterfeits flooding the market

Counterfeiting, web spoofing, fake listings, and product copies have witnessed a huge spike in recent years, impacting sales & growth of big brands in the global market. Identifying such online scammers from lakes of websites, marketplaces, and web portals is equivalent to finding a needle in a haystack.

Our client is one of the leading revenue recovery companies with proprietary AI-based software. This tool crawls the web on behalf of businesses to detect IP (intellectual property) infringements. This platform allows our client to support its clientele by identifying copyright violations, product or brand impersonation, product or content piracy, counterfeits, and distribution abuse. With this unique proposition, our client currently assists over a thousand online brands in monitoring fraud and enables them to take necessary legal action backed by evidence to recover their revenue.

SunTec Data helped discover and bridge the gaps in the client’s AI brand protection platform through multiple types of data support solutions.

PROJECT CHALLENGES

Addressing data management obstacles for an AI-driven brand protection software

Handling the avalanche of data being scraped by their AI system for all these brands had become overwhelming. So, the client decided to outsource a few data-specific operations.

During our discussions, we identified a few issues that needed immediate attention.

Data cleaning: To enable the AI platform to function effectively, it needed to be fed with a continuous stream of high-quality data, thus creating a need for consistent data cleaning and data hygiene monitoring.
Data validation: The scraped data (primarily containing links to counterfeit products) needed regular verification.
Data appending: The data collected by their AI contained incomplete information (in certain cases where data crawling & extraction methods failed to extract all the information), so it needed manual data appending.
Web research: In cases where automated scraping failed to produce results, we needed to detect the online scammers through manual web research.
Dedicated team for the entire data management process: We proposed a dedicated team of data researchers to build on the knowledge to perfect the process.

"We suspected most of the issues that came up during our discussion with the team. But, seeing the final scope after our initial talks, we were shocked and frustrated at how much of our time was going towards managing the aftermath of things that we weren’t even viewing as a problem."

-A business user recorded saying after the requirement discussion phase

OUR SOLUTIONS

Verifying influx of extracted data and performing manual research where automated scraping techniques failed

This project ran in phases. We started out with a few basic tasks (mainly data validation) and moved on to data cleaning and web research.

Basic data validation support

Initially, the client assigned us a single task of asset authentication. We matched images with client guidelines and either validated, discarded, or reassigned the image category.

Data appending tasks

We completed the client’s database by entering critical information present in the URLs collected by their AI system.

In-depth manual data research to detect counterfeit listings

We set up a web research team that went through websites, social media platforms, online marketplaces, and other channels (following client-defined parameters). We prepared a consolidated excel sheet with flagged links and uploaded it to the client’s portal.

Web research to detect content piracy

We followed brand-specific guidelines to compare the client’s content against the sources found on the web and created a list of URLs where we detected piracy.

IOR (Infringement on Review)

We sorted the data evidence under categories (counterfeit, brand abuse, replica, copyright, and brand impersonation) to simplify the client’s task of evidence assimilation.

"After evaluating their initial work, we became confident that this was a reliable partner. Their team was open to changes, actively sought feedback, followed radical transparency, and enforced a quick TAT that enabled us to place trust in them and take the project forward."

-Said the client

The achievements we acquired on the way

The client extends its services to over a thousand brands. Initially, we were assigned only a few of them.

By the end of the first month, the brands assigned to us were doubled. Our dedicated team significantly reduced client involvement (for feedback and quality analysis) by nearly 70%. We also improved our accuracy rates from 72% to 95%.

Adopting a proactive approach for this multi-tier data support project helped enhance the client’s operational efficiency

For this particular project, our team showcased admirable resilience. We experimented with approaches, evaluated and reframed processes, and established quality analysis systems while evolving with the client’s needs and workload.

Initially, when we started the project, we divided our teams based on the brands we were assigned. For example, from the entire manual data detection team, each resource was assigned a few brands to work on. When this approach resulted in quality issues and higher errors, we divided our teams based on processes for simplified management.

With continuous process refinement, resource upskilling, and uncompromised data compliance to their standards, we built confidence and became the client’s ‘Vendor of Choice,’ which resulted in the following:

The client recorded a 30% reduced delivery timeline for its clientele.
The client signed a long-term contract with us for continued partnership.
We acquired over 50% of the client’s work. (As of now, we are working for nearly half of all the brands who use this client’s AI system).
The project team scaled from 6 resources to 101 resources (and counting) in a span of six months.

"While there is more to do, we have helped the client achieve the most important thing- increasing the accuracy rate of their AI platform and improving on the promised deliverables to their clientele. "

Project Manager -

Humans-in-the-loop approach

The goal of any AI-based tech platform is to be as close to precision as possible. Achieving this goal across sites is difficult since websites usually have distinct unique underlying patterns. Further, website structures change with time, so these AI-based solutions must be robust enough to adapt. The automated crawling methods, howsoever trained, cannot work unsupervised – they need to work in tandem with a human-in-the-loop approach to deliver the most optimal results.

SunTec understands this man-plus-machine approach very well and continues to effectively serve several AI-based tech platforms for their varied data management needs.

"As a back-office data support vendor, SunTec continues to be a wonderful partner. With immense support from the team and collective efforts, we were able to improve the efficiency of our AI-based brand protection software and save millions in revenue for our customers. Moreover, with dedicated resources, and quick data support, we were able to reduce our delivery timeline by 30% for our customers. We are definitely looking forward to a long term association with their team."

- CEO, Client Company