How much does video annotation outsourcing cost?
Pricing depends on label type (tracking vs segmentation vs event spans), clip length/FPS, ontology complexity, and QA depth. As a reference point, Abaka’s annotation programs include options like Dense Captioning at $6/hr, Image Editing at $8/hr, STEM Generalist at $12/hr, and LLM Math/Coding at $18/hr for higher-skill workstreams. For autonomy-adjacent tasks, Road Lane labeling can be priced at $3/km. We’ll propose a scoped plan with measurable acceptance criteria and a pilot before scaling.
How fast can you start a video annotation outsourcing project?
Most teams can move from kickoff to a first accepted pilot batch in about 2–3 weeks, depending on security onboarding and ontology readiness. Day 0–3 is typically used to finalize label definitions and set up Abaka Forge workflows and access controls. Week 1–2 focuses on pilot production and QA calibration. After the pilot is accepted, we ramp staffing and deliver consistent weekly drops with an operating cadence for reviews and change requests.
What video formats and output formats do you support?
We commonly ingest MP4/MOV and image sequences, and we can align to your frame rates and clip boundaries. Outputs are delivered as structured sidecars such as JSON/JSONL and CSV timecodes, and can be adapted to common CV training pipelines (e.g., COCO-style JSON variants). If you need masks, we can deliver PNG masks or encoded representations alongside metadata. We’ll confirm exact schemas during onboarding to match your training and evaluation stack.
What accuracy can you achieve for video labeling?
Abaka programs can target 99% accuracy through multi-layer QA, calibrated reviewers, and explicit edge-case policies for temporal consistency. Accuracy in video is not just per-frame correctness—it includes stable identity across occlusion, consistent boundaries over time, and repeatable start/end definitions for events. We define acceptance criteria with you, run a pilot to calibrate guidelines, and then maintain quality through audits, escalation paths, and weekly review of disagreement patterns.
How do you keep sensitive video secure during outsourcing?
Security is built into Abaka’s delivery model: SOC 2 and ISO 27001 controls, GDPR and CCPA alignment, strict NDAs, and segregated secure pipelines. Access is limited by role and project, and we maintain audit-friendly logging. We also provide full IP provenance and ensure your data is exclusively yours—never repurposed, resold, or shared. This structure reduces security-review cycles and helps you scale safely across teams and geographies.
Can you annotate multilingual video content (signage, speech, on-screen text)?
Yes. Abaka operates across 50+ countries and can staff programs with language coverage for subtitles, on-screen text, and region-specific context. For video understanding, we can combine temporal labels with text tasks like transcription, translation, or captioning when needed. We’ll define language-specific guidelines (e.g., transliteration rules, sensitive content handling, and locale-aware taxonomies) and keep outputs consistent through reviewer routing and standardized QA checks.
How is Abaka different from other video labeling companies?
Abaka is designed for trustworthy, security-forward delivery at scale. We never build models that compete with you, and your data is never repurposed, resold, or shared. Operationally, Abaka Forge provides an all-in-one workflow with automation to accelerate repetitive tasks while preserving human verification for temporal edge cases. Combined with compliance (SOC 2, ISO 27001, GDPR, CCPA) and full IP provenance, this reduces both dataset risk and procurement friction.
What if we need to change the ontology or guidelines mid-project?
Change requests are normal—what matters is controlled rollout. Abaka versions ontologies and guidelines, documents what changed, and identifies which clips are impacted. We can run a small recalibration batch to confirm the new definition, then apply updates in a staged manner so you don’t mix incompatible labels in the same training split. Weekly reviews ensure disagreements and edge cases are captured quickly, and your team gets a clear change log for reproducibility.
Can we start with a pilot before committing to scale?
Yes. We recommend a pilot to validate label definitions, outputs, and QA standards before scaling volume. The pilot typically includes representative edge cases (crowds, occlusions, low light, motion blur) and produces an acceptance-tested sample you can train/evaluate on. After pilot review, Abaka finalizes the playbook, calibrates reviewer coverage, and proposes a ramp plan to steady-state weekly drops inside Abaka Forge.
Who owns the labeled data and can it be reused elsewhere?
You own your data and the resulting labels. Abaka does not repurpose, resell, or share your datasets—your data remains exclusively yours. We maintain full IP provenance and operate under strict NDAs and segregated pipelines to protect access and prevent commingling. This is especially important for proprietary roadway footage, factory video, and security-sensitive streams where downstream use must be tightly controlled.
What tools do you use for video annotation outsourcing?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, and production workflows across text, images, video, 3D/4D point clouds, and RLHF. Abaka Forge supports QA routing, reviewer playback, escalation workflows, and automation to speed up repetitive steps. If you have strict export requirements or need to integrate with internal tooling, we map outputs to your schemas and confirm compatibility during the pilot phase.
What is the minimum project size for video annotation outsourcing?
There is no single minimum, but the best starting point is enough clips to validate edge cases and measure quality reliably—often a pilot batch that represents your real distribution. If you have a small dataset, we can still help by tightening ontology, establishing policies, and delivering a clean, acceptance-tested set for evaluation. For large programs, we’ll design a ramp plan that balances speed with the 500 files/day per-annotator cap to preserve quality.