Collect. Annotate. Validate. Build Better AI
Inspection models fail on subtle defects, thermal noise, and varied asset types. We annotate drone, thermal, and geospatial 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.
A hairline crack on a turbine blade, a hot spot on a solar cell, or corrosion on a utility pole is easy to miss in a large image. Thermal and infrared data show heat rather than shape, and aerial capture changes scale and angle across every flight.
Labeling errors become missed defects, false maintenance alerts, and inspections that have to be repeated.
Annotators are trained on your defect classes and severity rules before production begins. Every batch passes internal and external quality checks, and errors are corrected at the source.
Solar inspection models need labeled examples of healthy and faulty panels. We use polygon annotation to outline individual panels, cells, and strings, then label defects such as cracks, hot spots, soiling, shading, delamination, and broken glass according to your defect classes. Tight outlines and consistent severity tags help models find faults early and tell cosmetic wear from performance loss.
Consumption models learn what normal usage looks like so they can spot what is not. We label meter and sensor readings, time-series segments, usage events, peaks, seasonal patterns, and anomalies against rules you define. Clean, consistent labels support forecasting, load profiling, and detection of unusual consumption.
Blade inspection depends on close imagery that captures small damage on large structures. Annotators outline blades, towers, and nacelles, and label erosion, cracks, lightning damage, leading edge wear, coating loss, and other surface defects using polygons and segmentation masks. Defect type and severity tags give models the detail needed to prioritize maintenance.
Grid models must recognize the equipment that carries power to customers. We annotate poles, crossarms, conductors, insulators, transformers, switchgear, and related fittings using bounding boxes and polygons on ground, aerial, and vehicle-mounted imagery. Consistent class definitions help models locate assets reliably across terrain, vegetation, and varied lighting.
Leaks and corrosion often start small and are easy to miss. We label corrosion, rust, staining, seepage, spills, and surface deterioration on pipes, tanks, valves, and structures, using polygons that follow the affected area. Severity and type tags let models separate early warning signs from conditions that need immediate action.
Thermal imagery shows heat patterns that normal cameras cannot see. We annotate hot spots, overheating components, insulation gaps, and temperature anomalies on panels, transformers, cables, and connections, and tag intensity ranges where your project requires them. Annotators trained in thermal interpretation help models detect faults before they cause outages.
Asset models need to know what each piece of infrastructure is. We detect and classify assets such as panels, turbines, transformers, substations, tanks, pipelines, and meters using bounding boxes and polygons. A single taxonomy applied across all images keeps asset inventories consistent and easy to update.
Drones reach towers, blades, rooftops, and long pipelines that are costly to inspect on foot. Our image annotation services and video annotation services label assets, defects, and conditions in drone photographs and footage, and track the same asset across frames and flights. Annotators manage changing altitude, angle, and scale so models perform on real inspection data.
Aerial imagery covers large solar farms in a single pass. We annotate arrays, rows, individual panels, inverters, roads, and boundaries, and mark defective or missing panels within each row. These labels support models that scan whole sites and point maintenance teams to the right location.
Mapping models place assets in their correct position within the network. We annotate poles, lines, substations, transformers, and access routes on aerial, satellite, and street-level imagery, and link each asset to its class and location. The result is structured data that supports asset registers, planning, and inspection routes.
Utility networks stretch across large areas and diverse terrain. We apply semantic segmentation services and object labeling to separate line corridors, vegetation, buildings, roads, water, and ground in geospatial imagery, and mark encroachment and access constraints. Consistent labels help models assess corridors, plan routes, and monitor change over time.
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.
Energy data 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 energy & utilities, 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, energy & utilities projects may involve inspection imagery, equipment photographs, infrastructure video, asset categories, visible defects, and other defined operational data. 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 Small or partially hidden defects, harsh environments, variable camera angles, similar-looking components, and the cost of missed or incorrect labels. 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.
