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Large-Scale Product Data Attribution & Enrichment for E-commerce Cataloging Overview: A leading US retail giant partnered with us to automate and scale its e-commerce cataloging operations. The objective was to enrich and standardize product data at scale, enabling accurate classification, improved discoverability, and seamless catalog management across 20,000+ SKUs. Approach: Designed structured workflows for large-scale product data enrichment and attribution Built comprehensive datasets to support AI-driven product identification and matching Integrated Human-in-the-Loop (HITL) mechanisms to enhance classification accuracy Focused on standardization across titles, categories, and visual assets Execution: Curated detailed product attributes including titles, descriptions, images, and specification tables to enable precise AI-based matching and classification Sourced product images at scale via web scraping and normalized datasets for consistent product display pages Conducted taxonomy audits on AI-predicted categories, incorporating HITL feedback loops to correct misclassifications and improve model performance Impact: Automated product metadata creation, significantly improving search optimization Enabled accurate and scalable title generation for better navigation and breadcrumb trails Improved prediction accuracy for image-based categorization Facilitated intelligent product kitting and enhanced catalog structuring.

Audio Transcription & Subtitling for Sports Broadcast Overview: A large crowdsourcing platform partnered with us to deliver high-accuracy transcription and subtitling for a premier league sports broadcaster in India. The project required precise timestamp alignment, linguistic accuracy, and effective handling of high-noise, multi-speaker audio. Approach: Designed workflows for high-volume audio transcription and subtitling Focused on noise reduction and speaker isolation in complex audio environments Implemented a two-level quality control framework for accuracy and consistency Execution: L1 Teams: Subtitle creation, transcription, and initial validation L2 Teams: Segment-level review with timestamp-based corrections Iterative feedback loops to minimize errors and improve consistency Managed challenges including crowd noise, overlapping speakers, and long-duration files Impact: Processed 9,000+ audio files (10–16 minutes each) Delivered accurate English subtitles with precise timestamp synchronization Maintained consistent quality and daily production targets at scale Produced broadcast-ready outputs for seamless viewer experience

PoI Data Enrichment for a Global Navigation & Mapping Platform Overview: A global mapping and navigation provider for luxury automotive brands needed to process large volumes of street-level, geo-tagged images across India. The objective was to enrich raw location data with high-quality PoI (Point of Interest) attributes to enhance mapping intelligence. Approach: Deployed a scalable annotation team for high-volume image processing Designed structured workflows for PoI attribute enrichment Implemented 2-level quality assurance for accuracy and consistency Attribute Enrichment Framework: Temporal Attributes:Time of day, arrival/departure patterns, and dwell time insights Contextual Attributes:POI category, operating hours, and brand affiliation Spatial Attributes:Polygon boundary mapping and parent-child relationship structuring Execution: Processed 75,000+ geo-tagged images across diverse locations Ensured high attribute completeness and consistency Maintained quality through rigorous validation workflows Impact: Achieved 98%+ attribute fill rates across key data fields Enhanced mapping accuracy and PoI intelligence Reduced operational overheads through structured workflows Accelerated client’s go-to-market for India mapping data

Document Classification & Annotation for Loan Underwriting Automation Overview: A leading banking software provider required high-quality annotated datasets to train AI models for automated loan underwriting across multiple lending environments. Approach: Designed custom annotation workflows for diverse financial documents Enabled machine-led data extraction with high accuracy and confidence Supported scalability across multiple lender implementations Annotation Workflow: Document Classification:Identify and classify document types using visual structure and text patterns.Includes salary slips, bank statements, tax returns, property, and insurance documents. NER-Based Annotation:Perform Named Entity Recognition (NER) to label key textual elements.Validate and refine machine-generated annotations for accuracy. Field Extraction:Annotate critical underwriting fields such as borrower name, income, balances, and property valuation.Train models to accurately locate and extract decision-relevant data. Human-in-the-Loop QA:Cross-verify extracted data against source documents.Flag exceptions and inconsistencies for iterative model improvement. Impact: Enabled high-confidence automated data extraction for underwriting workflows Improved model accuracy through continuous feedback loops Built scalable annotation pipelines for multi-lender deployment Reduced manual effort in document processing and validation

Building a Data-Rich Image Repository for a Medical Equipment E-commerce Platform Overview: A leading B2B medical e-commerce platform faced challenges with 5,000+ unstructured and untagged product images, impacting searchability, discoverability, and catalogue consistency. Approach: Deployed a team of 20 data specialists and annotators Established a structured, high-volume image processing workflow Created attribute-rich image datasets aligned to product categories Enriched product titles with key attributes such as category, end-use, and dimensions Execution: Processed each image through multi-layer workflows: Background removal Size standardization Watermarking Intelligent categorization Generated 5 unique, optimized images per product display page (PDP) Ensured duplicate-free, high-quality outputs through multi-level QA Impact: Delivered 50,000+ fully processed and annotated images in 16 weeks Built a searchable, catalogue-ready image repository Enabled seamless integration across e-commerce platform, ERP, and billing systems Improved product discoverability and visual consistency at scale

Creating 6,000+ Sub-District Population Heatmaps for Rural Banking Expansion Overview: A leading digital payments provider in India aimed to deploy a nationwide network of 200,000+ micro-ATMs with a focus on rural and semi-urban regions. The challenge was to identify high-impact locations using granular population insights at the tehsil (sub-district) level. Approach: Built a dedicated team of 35 GIS specialists to manage large-scale geospatial mapping Structured workflows to enable parallel district-level execution across multiple regions Recreated tehsil-level grid maps using tools like GIMP Tagged town and village-level population data onto each map Ensured consistency through standardized mapping frameworks and QA processes Execution: Scaled mapping operations across hundreds of districts simultaneously Delivered high-resolution population heatmaps for precise location planning Implemented multi-level quality checks to maintain data accuracy Impact: Delivered 6,000+ tehsil-level maps across India Enabled data-driven decision-making for micro-ATM placement Completed within committed timelines without compromising quality Provided granular rural insights at town and village cluster levels