Collect. Annotate. Validate. Build Better AI
Inspection models fail on faint defects, glare, and rare failure types. We annotate images, video, and sensor data through a multi-stage QA process, so your model trains on labels it can trust.
The accuracy of your labels is the accuracy of your AI model, and our process is built around that.
Defects are rare, small, and inconsistent. A scratch, a weld flaw, or a misplaced component can look like normal variation under factory lighting. Sensor streams such as vibration and temperature need labeled fault windows, and a loose boundary teaches a model the wrong threshold.
Labeling errors become escaped defects, false rejects, and predictive maintenance that raises alarms too late or too often.
Annotators are trained on your defect classes, tolerances, and assembly rules before production begins. Every batch passes internal and external quality checks, and we can work inside your own tools.
We label scratches, dents, cracks, discoloration, and contamination on product images and inspection video, using bounding box annotation, polygons, and segmentation matched to your defect classes and severity grades. Consistent boundaries help your model separate real defects from reflections and normal surface texture.
We annotate solder joints, missing or misaligned components, bridging, lifted pads, and trace damage on board images, including high-magnification captures. Component-level classes and pass or fail labels give your model the detail automated optical inspection requires.
We label layer shifts, warping, stringing, voids, and surface porosity in in-process camera footage and finished-part images. Frame-level and region-level labels help your model flag a failing build early, before the part is complete.
We annotate porosity, cracks, undercut, spatter, incomplete fusion, and uneven bead width on weld images and video, and trace seam paths as polylines. Precise defect boundaries and seam geometry support both inspection and weld guidance models.
We label time-series data by marking normal operation, early degradation, fault onset, and failure windows, and by tagging fault types such as bearing wear, imbalance, misalignment, and leaks. Each segment is aligned to the sensor channels and the machine states you define, giving your model clear ground truth for predicting failures with fewer false alarms.
We annotate parts, fasteners, and sub-assemblies in images and line video, and label placement, orientation, completeness, and step sequence, including hand-object interaction where operators are involved. Your model learns to catch missing parts, wrong orientation, and skipped steps.
We classify and localize products, parts, and packaging across lines, bins, and shelves using bounding box annotation and object detection annotation built on your part or SKU hierarchy. Careful class rules help your model tell near-identical variants apart.
We draw line-level text boxes and transcribe nameplates, serial numbers, batch codes, and labels, including engraved, stamped, worn, curved, and angled text. Accurate transcription supports traceability, asset records, and automated label checks.
We classify materials, surface finishes, and texture patterns at image or region level, such as brushed, polished, matte, woven, or composite. These labels support sorting, grading, and finish inspection models.
Through our text annotation services, we tag part names, fault codes, equipment IDs, actions, and procedure steps in maintenance logs, work orders, and work instructions. Structured text helps your model link a reported fault to its likely cause and corrective action.
We annotate people, protective equipment, restricted zones, vehicle proximity, and unsafe postures in floor video, and track them across frames. Consistent tracks help your model raise compliance alerts that operators can trust.
We segment machines, conveyors, tools, pallets, walkways, people, and obstacles at pixel level in factory and warehouse scenes using semantic segmentation annotation. Your robots, guided vehicles, and monitoring systems get a clear map of what sits where.
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.
Manufacturing 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 manufacturing, 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, manufacturing projects may involve product and component images, assembly-line video, defect categories, tools, parts, and visible process states according to client guidelines. 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 Fine-grained defects, reflective surfaces, part variation, camera changes, and disagreement over defect severity or acceptable tolerances. 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.
