Collect. Annotate. Validate. Build Better AI
Crowded shelves, near-identical packaging, and ever-changing catalogues break retail and fashion models. We deliver image and video annotation for SKU recognition, shelf monitoring, apparel tagging, and in-store analytics, backed by multi-stage QA, so your model trains on labels it can trust.
Your model can only be as accurate as its labels, and our process is built around that.
Retail imagery is dense and repetitive. Products sit edge to edge, packaging differs by a single word or color, and catalogues run to hundreds of classes that change with every season and promotion. In fashion, one garment can need a bounding box, a polygon, and a dozen attribute tags. A single misplaced SKU label teaches the AI model to confuse two products, and in in-store video, one lost track breaks customer journey analysis.
These errors surface as wrong stock counts, planogram compliance alerts nobody trusts, and visual search that returns the wrong item.
Annotators are trained on your SKU lists, class hierarchies, and attribute rules before production begins. Every batch passes internal and external quality checks, and errors are corrected at the source.
Shelf monitoring models compare what is on the shelf with what the planogram says should be there. We label every product against a digital planogram so models can spot gaps, misplaced items, and incorrect facings. This supports restocking alerts and compliance checks across aisles and store formats.
Product recognition models need to identify an item down to the exact SKU. We draw a box around each product and assign its product code, handling lookalike packs, size variants, and multilingual packaging. This labeling forms the base for automated stock checks, checkout recognition, and shelf analysis.
Inventory monitoring uses labeled images and video to count stock on shelves, in backrooms, and in warehouses. Annotation of each unit and its status lets models estimate quantities and flag low stock. Accurate labels reduce miscounts and help teams act before products run out.
Fashion annotation labels garments, footwear, bags, jewelry and other accessories using boxes or polygons, with attributes such as style, color, pattern and fit. This supports outfit recognition, trend analysis and catalogue automation. Annotators work to your style taxonomy so labels stay consistent across large image collections.
Logo and brand annotation locates and identifies brand marks across products, signage, packaging and media. Annotators assign each brand to main and supporting classes where your project requires it. The data helps measure brand visibility, placement, and competitor presence.
Online catalogue annotation prepares product images and listings for e-commerce AI. We label images, extract text, and fill product fields so catalogues stay clean and searchable. Standardized labeling also reduces duplicate and mismatched listings.
Product attribute annotation records the details that shoppers filter by, including color, size, material, style, brand, and features. Annotators read both images and titles to capture each value accurately. Complete attributes improve search, filtering, and recommendation quality.
Shopping and product tagging links items in images and video to the matching products in a catalogue. Annotators mark each item and attach the right product reference. This powers shoppable content, where viewers can select an item and go straight to its listing.
Visual search lets shoppers find products using a photo, and recommendation models suggest items that match their taste. Both depend on well-labeled product images with consistent attributes and categories. We prepare that training data so similar items are grouped correctly and matches feel relevant.
In-store video analytics models learn from labeled footage of people moving through a store. Annotators track shoppers across frames and tag actions such as browsing, picking up items, and queuing. These labels support footfall, dwell time, and behavior studies.
Sentiment and feedback analysis turns reviews, surveys, and support messages into labeled data. Annotators tag the opinion, its strength, and the subject it refers to. Retail teams use the output to track satisfaction and find recurring product or service issues.
Retail analytics models combine labeled visual, text, and behavioral data to explain what is selling, where, and why. Annotation of products, shoppers, and feedback provides the structured inputs these models need. Clear, consistent labels make the resulting insights easier to trust and act on.
Conversational shopping assistants must understand what a shopper wants and respond helpfully. We label intents, product entities, attributes, and conversation turns so models can follow a request from first message to purchase. Annotators also review responses for relevance and accuracy.
Demand forecasting models rely on clean, well-structured product and sales-related data. Annotation supports this by classifying products, tagging attributes, and labeling signals such as promotions and seasonal items. Consistent labels let models group similar products and read demand patterns more reliably.
Explore our data collection services.
Domain-trained expert annotators and daily throughput tracking against SLA targets.
A rigorous multi-level quality assurance process that ensures a 97%+ quality benchmark, validated on every project.
A risk-free 2-day pilot before full production, and team deployment within 24 business hours of sign-off. You see our work before you commit to volume.
Every engagement is tailored to your goals, with annotation guidelines, QA, and delivery cadence adapted to your project.
Pay-per-verified-productive-hour billing. You are not paying for our learning curve or mistakes, our internal rework, or idle bench time.
ISO/IEC 27001:2022, ISO 9001:2015, TISAX Level 3, HIPAA, and GDPR compliance. Biometric access, 24*7 CCTV monitoring, and policies and procedures that prohibit cameras and unauthorised storage devices on the floor.
100% in-house delivery. No outsourcing, no work-from-home access to client data, no exceptions.
Retail image annotation is the structured labeling of data so a machine-learning or analytics system can learn from examples or be evaluated against defined criteria. For fashion, retail & e-commerce, the work begins with a written label taxonomy, clear inclusion and exclusion rules, and representative sample data. The exact scope depends on the model objective and the client's acceptance criteria.
Depending on the approved scope, fashion, retail & e-commerce projects may involve product images, apparel attributes, product categories, visible features, catalog text, and image-to-product matching when specified. The team should define each label, the unit being annotated, how to treat uncertain cases, and whether the task is frame-level, object-level, document-level, or text-level. These examples describe possible project requirements, not a guarantee that every modality or use case is currently offered.
Output formats and tooling should be confirmed during discovery rather than assumed from a general industry page. The project brief should specify the required schema, field names, class IDs, metadata, export structure, and any client platform access. Qualitas Global should confirm compatibility with the requested tools before the pilot begins; no specific format or third-party platform is promised here.
A quality plan should define the annotation guideline, reviewer responsibilities, sampling or review coverage, error categories, escalation rules, and acceptance criteria before production. Multi-level quality checks are a stated part of the intended service approach, but the exact workflow and quality SLA need internal confirmation. Disagreements should be documented, corrected, and reflected in updated guidance where the same issue could recur.
Data access, storage, transfer, retention, deletion, and physical work arrangements should be agreed before any sensitive files are shared. The project owner should confirm the applicable security controls and whether any certification or regulatory commitment applies to the specific engagement. Do not assume a particular ISO standard, HIPAA status, GDPR status, or other certification without current internal evidence.
Pricing should be scoped around the task definition, data complexity, annotation guideline, expected review effort, language or domain requirements, and the delivery arrangement. The stated commercial model is based on productive hours, with scope and acceptance criteria agreed in advance. A quote should clarify what counts as productive work, how rework is treated, and which activities are included, without relying on an unverified public price.
Yes, the recommended first step is to discuss a representative sample and agree on a pilot scope before committing a larger dataset. The pilot can test the label definitions, edge-case handling, reviewer workflow, output structure, and acceptance criteria. Start dates, sample size, turnaround, staffing, and scale-up capacity should be confirmed by the project team after reviewing the data and requirements.
Common challenges include Similar colors or patterns, folded or occluded garments, inconsistent product photography, variant confusion, and changing catalog taxonomies. A clear guideline should show positive and negative examples, explain ambiguous cases, and define when annotators should escalate rather than guess. To get started, share the intended model or business use, data types, approximate volume, required outputs, security constraints, and a sample if permitted. Qualitas Global can then confirm fit and propose a pilot.
We audit, correct and validate auto-annotated aerial data against your guidelines before it reaches your training pipeline.
Whether you need precisely annotated training data, real-world data collection, a dedicated in-house team, or other staffing solutions, Qualitas Global delivers all three under one roof. Backed by over a decade of experience, we back every engagement with a risk-free 2-day pilot before you commit.
