The client is a well-established competitive intelligence firm based in the United States. They specialize in tracking and analyzing competitors' direct marketing campaigns across email, social media, and online/offline promotions. Their services span multiple sectors, including banking, insurance, retail, credit cards, energy, and telecom — helping brands understand what their competitors are doing and how to respond.
Following a successful data management project, the client reached out to SunTec India for additional support to manage their annotation workflow, to label a large volume of retail promotional content at a consistent monthly scale. Their core objective was to identify, annotate, and categorize different retail-related categories, such as Entertainment, Food Services, Health & Beauty, Clothing & Accessories, and others, within PDF documents derived from HTML-based email campaigns.
Each identified category had to be marked with bounding boxes using the client's proprietary data annotation tool. In addition to the bounding box, annotators were required to capture additional metadata for each marked element, including the promotional value, brand name, and parent company name. This metadata was fed directly into the client's system to calculate the Relative Promotional Value (RPV) — a metric based on the proportion of the page area each advertisement occupies within the PDF.
In essence, the project required a combination of image annotation services, structured metadata tagging, and brand-entity attribution, with a monthly volume target of 2,50,000 annotations.
At first glance, the project appeared to be a straightforward PDF labeling task — draw bounding boxes, tag metadata, and deliver. However, the combination of scale, source inconsistency, and the analytical weight the client placed on annotation accuracy made this far more demanding than a typical image labeling engagement. Every annotation directly influenced the client's RPV calculations, which meant even small errors could distort the competitive intelligence their end customers relied on.
We deployed a dedicated team of 23 resources, including image annotation specialists, quality analysts, a domain SME (subject matter expert), and a project manager, all trained on the client's data annotation tool. Our approach combined visual annotation with structured quality control workflows to deliver consistent, high-quality outputs month over month.
Our annotators were trained on the client's image annotation platform during an intensive onboarding phase. This included hands-on sessions covering bounding box placement conventions, metadata tagging fields, category classification rules, and the RPV calculation logic. A detailed annotation guideline document was co-developed with the client to serve as the team's operational reference throughout the project.
At the start of each new batch, a senior annotator assessed the incoming files for layout patterns, rendering issues, content block overlap, and resolution quality. Based on this assessment, the team received a batch-level format briefing that flagged specific quirks to watch for, such as ad blocks bleeding into adjacent content, broken image rendering, or non-standard page structures. We also created visual reference sheets for recurring formats that new team members could consult during onboarding and existing annotators could refer to when labeling unfamiliar batch formats.
Our annotators processed each PDF document systematically, scanning the layout, identifying distinct promotional blocks, and drawing precise bounding boxes around each one. Each bounding box was classified into the correct retail category (Entertainment, Food Services, Health & Beauty, Clothing & Accessories, etc.) based on visual and textual cues in the content.
Once the bounding boxes were in place, annotators enriched each annotation with the required metadata — including brand name, parent company, and promotional value. We also maintained and regularly updated an internal reference database of retail brands and their parent companies to ensure consistency. When new or unfamiliar brands appeared in the promotional content, annotators performed targeted web research to verify brand-parent relationships before tagging, reducing the risk of misattribution.
For ambiguous advertisements that straddled multiple categories, we established a formal escalation protocol. Senior annotators and subject matter experts reviewed edge cases and made classification decisions based on predefined rules agreed upon with the client.
Over time, these escalated decisions were documented and fed back into the annotation guidelines, building an expanding library of precedents that reduced ambiguity for the broader team and improved classification speed on similar cases in future batches.
We implemented a three-tier quality assurance process to maintain annotation accuracy at scale:
We maintained a continuous feedback loop with the client. Weekly calibration calls addressed evolving classification rules, new retail categories, and changes to the client's data requirements. This iterative process allowed us to refine our annotation criteria over time and stay aligned with the client's analytics objectives, which was critical for a project spanning multiple years.
The structured approach to retail annotation and data labeling delivered measurable improvements to the client's promotional analytics pipeline, and the project ran for a little over 3 years.
250K+ Annotations Delivered Monthly With no deadline creeps or delays
98.5% Annotation Accuracy Maintained throughout the project
50% Shorter Report Lifecycle Improved time-to-insight for end customers
The team adapted and consistently delivered month after month. This became a long-term partnership because the results were so consistent.
- Project Lead
Whether you need image annotation services for object detection, metadata tagging for competitive intelligence, or specialized retail annotation services (like video annotation for in-store customer support), our team delivers precision at scale. With ISO-certified data security practices and 25+ years of domain experience, we help businesses build smarter AI models with high-quality training data. Additionally, we offer a broad range of AI training data services to help enterprise AI initiatives succeed. Request a free consultation to learn how we can support your annotation needs.