How much does a Video Labeling Company cost per hour or per task?
Pricing depends on the task type (tracking vs. segmentation vs. keypoints), guideline complexity, and QA depth. For reference, Abaka’s real-world rates include Dense Captioning at $6/hr and Image Editing at $8/hr, with STEM Generalist work at $12/hr and LLM Math/Coding at $18/hr. Video programs are usually scoped as an hourly plan plus QA and project ops, then mapped to clip length and target throughput. Talk to an Expert and we’ll propose a pilot budget with clear acceptance criteria and delivery milestones.
How long does it take to start and deliver the first batch of labeled videos?
Most teams can start with a structured pilot in Week 1–2 after aligning on ontology, sampling, and export specs. Production scale often follows in Week 2–3 once guideline calibration is stable and QA gates are proven. Timeline varies by footage sensitivity, access requirements, and whether you need complex temporal rules (ID continuity, event boundaries, heavy occlusion). We’ll give you a concrete plan with daily/weekly deliveries so your training runs are not blocked.
What video labeling formats and exports do you support?
We support common video and sequence exports such as frame-indexed JSON, COCO-style JSON (where applicable), mask sequences (PNG) with sidecar metadata, and per-frame CSV/JSON for keypoints or attributes. We can also deliver sequence manifests, dataset splits, and versioned guideline references so experiments remain reproducible. If you have a custom training loader, we’ll align the schema early in the pilot and validate with a sample export before scaling.
What accuracy can you achieve for video annotation and tracking?
Accuracy is task-dependent, but Abaka programs can target up to 99% accuracy where applicable through multi-layer QA, reviewer calibration, and adjudication for ambiguous segments. For video, we focus on temporal correctness metrics—track continuity, reduced ID switches, stable masks, and consistent event boundaries—because “single-frame correctness” is not enough. During the pilot, we agree on acceptance criteria and measure errors by class and scenario so quality is transparent and actionable.
How do you keep sensitive video data secure during labeling?
Abaka operates with SOC 2 and ISO 27001 controls and supports GDPR and CCPA-aligned workflows. We use strict NDAs, segregated secure pipelines, and controlled access so only authorized staff can work on your data. We maintain audit trails through Abaka Forge and can structure projects to minimize exposure (sampling, redaction instructions, restricted exports). Your data is exclusively yours—never repurposed, resold, or shared—and Abaka does not build models that compete with you.
Can you label multilingual videos and localized content at scale?
Yes. Abaka operates across 50+ countries and can staff multilingual annotators and reviewers for tasks that combine video with text or audio, such as subtitle alignment, intent labeling, and region-specific scene interpretation. We standardize taxonomies and attribute schemas so labels remain comparable across locales, then apply reviewer calibration to reduce differences in judgment. If your project includes audio or on-screen text, we can produce unified exports that connect temporal video segments to transcription and metadata.
How is Abaka different from other video labeling vendors?
Abaka combines managed operations with Abaka Forge—an all-in-one platform for collection, cleaning, annotation, QA, and production—so workflows are auditable and consistent, not ad hoc. We are self-funded and profitable with no VC or acquisition pressure, and we never build models that compete with you. That keeps incentives aligned to your outcomes: secure handling, predictable delivery, and labels that stay consistent over time. We also bring domain-specialist reviewers for nuanced taxonomies.
What if we need changes to the ontology or labeling rules mid-project?
Change is normal in video programs—new edge cases and classes appear once models hit production. We manage updates through versioned guidelines, change logs, and controlled rollouts so you don’t silently mix incompatible labels. Depending on impact, we can apply changes only to new data, reprocess a targeted slice, or run a short recalibration pilot. Abaka Forge supports auditing and rework routing so updates are measurable, and you can keep experiments reproducible across dataset versions.
Can we run a pilot before committing to a larger contract?
Yes. Most engagements begin with a pilot batch designed to validate ontology clarity, temporal rules (tracking continuity, event boundaries), QA thresholds, and export compatibility. You’ll receive pilot outputs plus a QA report identifying recurring ambiguities and recommended guideline improvements. This reduces risk before scaling to sustained weekly deliveries, and it gives your team evidence—quality metrics and workflow fit—before expanding scope.
Who owns the labeled data and can you reuse it for other customers?
You own your data and the outputs created for your project. Abaka’s policy is that your data is exclusively yours—never repurposed, resold, or shared. We also maintain full IP provenance for collected data (0% copyright risk on collected data) and keep projects under strict NDAs with segregated secure pipelines. If you need additional contractual language about ownership and retention, we can align it during onboarding.
What tools and platforms do you use for video labeling?
We use Abaka Forge, our all-in-one platform that supports video, image, text, RLHF, and 3D/4D point cloud workflows. It includes automation assists, multi-layer QA, adjudication, audit trails, and export pipelines. Because the workflow is centralized, we can enforce consistent guidelines across annotators, rapidly route rework, and keep your program scalable without losing temporal consistency. If you have internal validation scripts, we’ll integrate export checks into the delivery process.
What is the minimum project size for a video labeling engagement?
There’s no fixed minimum, but video programs work best when scoped as a pilot batch large enough to reveal edge cases and stabilize reviewer calibration—typically multiple representative clips per scenario and camera type. If you only have a small dataset, we can still help by focusing on high-value labels: targeted edge-case mining, evaluation sets, or a compact training subset with strong QA. Talk to an Expert and we’ll recommend a pilot size that matches your timeline and budget.