Collect. Annotate. Validate. Build Better AI
Content models fail when scenes, brands, and moderation rules are labeled inconsistently. We annotate video, images, audio, and text 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.
Media content is fast, varied, and subjective. Scenes cut quickly, brand logos appear for a few frames, gestures differ by culture, and moderation rules depend on context. Two reviewers can label the same clip differently unless the guidelines are exact.
These differences become weak recommendations, missed brand exposure and moderation that is inconsistent.
Annotators are trained on your policies, scene definitions, and caption styles before production begins. Every batch passes internal and external quality checks, and reviewers are supported with daily feedback.
We transcribe dialogue, mark speaker changes, add sound cues, and time-align every line, then deliver subtitles in your target languages following your style guide for line length, reading speed, and tone. Accurate captions support accessibility, localisation, and speech models.
We label logos, product placements, and sponsor signage in gameplay, broadcast, and streaming video, recording brand, position, size, and duration on screen. Clear visibility labels, including partial occlusion and motion blur, give sponsors measurements they can rely on.
We label scene boundaries, shot types, settings, characters, objects, actions, on-screen text, and game interface elements at frame and segment level. Your model gains the context to find key moments, build highlights, and index large libraries.
We label offensive, prohibited, and banned content across images, video, audio, and text against your policy tiers, and escalate borderline cases for review. Exact policy rules keep decisions consistent across reviewers, regions, and content types.
Our keypoint annotation marks hand and body landmarks, labels gesture classes, and sets the start and end of each movement, with left and right hands distinguished where your classes need it. These labels support gesture controls, motion tracking, and interactive experiences.
We label content attributes such as genre, theme, mood, pacing, and audience suitability, and provide relevance judgments and side-by-side ranking against your criteria. Well-defined labels give recommendation models a clearer signal of what viewers and players actually want.
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.
Video annotation services is the structured labeling of data so a machine-learning or analytics system can learn from examples or be evaluated against defined criteria. For gaming, entertainment & media, 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, gaming, entertainment & media projects may involve video frames, visible objects, characters, actions, scene boundaries, audio transcripts, and content tags when defined by the project. 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 Fast motion, visual effects, scene transitions, overlapping speech, character identity across frames, and consistency across long clips. 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.
