How much do Video Annotation Experts cost?
Pricing depends on task complexity (tracking vs segmentation), QA depth, and whether you need expert escalation. For reference, Abaka programs commonly price specialized work using known baselines such as Dense Captioning at $6/hr and Image Editing at $8/hr, with advanced LLM Math/Coding at $18/hr when relevant expertise is required. For autonomy-style road labeling, Road Lane can be $3/km. We’ll scope a pilot, define acceptance criteria, and then provide a fixed quote per deliverable or an hourly plan that matches your throughput and quality targets.
How fast can you start and deliver the first labeled video batch?
Most teams can start quickly after security and spec alignment. A typical path is Day 0–3 for scope, schema, and acceptance criteria, then Week 1–2 for a calibrated pilot, and Week 2–3 to ramp into production deliveries. Timing varies with footage quality, label types (tracking, polygons, keypoints), and review requirements. We prioritize early feedback with a representative pilot so you validate outputs before scaling. After calibration, weekly release cadences keep training and evaluation schedules predictable.
What video annotation formats do you support (COCO, CVAT, masks)?
We support common exports such as COCO-style JSON, CVAT XML, per-frame JSON, timestamped CSV event logs, and PNG mask sequences for segmentation. If your pipeline uses a custom schema, we can map outputs to your required structure as long as the definitions are explicit and testable. Abaka Forge helps keep format consistency across releases with versioned exports and validation checks. We also deliver accompanying documentation—label maps, attribute definitions, and change logs—so your team can reproduce training runs and compare dataset versions.
How do you ensure accuracy on frame-by-frame and temporal labels?
We engineer quality with multi-layer QA rather than relying on spot checks alone. Video-specific controls include sequence-level review for continuity, audits for track fragmentation and ID switches, and guideline enforcement for occlusion and truncation rules. We use gold tasks to calibrate annotators and reviewers, plus sampling plans that focus on high-risk scenarios like motion blur, low light, and dense scenes. Abaka’s target is 99% accuracy on audited samples, and we tune QA depth to your risk tolerance and use case.
Can you meet enterprise security requirements for sensitive video data?
Yes. Abaka supports SOC 2 and ISO 27001 aligned operations, GDPR and CCPA processes, strict NDAs, and segregated secure pipelines. Access can be controlled by role and project to minimize exposure, and workflows maintain audit trails for labeling and review activities. We also provide full IP provenance—your data is exclusively yours and never repurposed, resold, or shared. If you have additional requirements (network restrictions, data retention rules, or custom governance), we scope them during onboarding and design the pipeline accordingly.
Do you support multilingual video annotation and subtitles?
Yes. Abaka operates across 50+ countries and can support multilingual captioning, transcription, and localized event labeling depending on your target markets. We can produce subtitles aligned to timestamps, translate or localize descriptions, and apply language-specific guidelines for entities or sensitive content. For global datasets, we recommend a shared core ontology with language-specific wording layers to keep meaning consistent across locales. Your team receives language coverage documentation and sampling-based QA reports so you can trust cross-language consistency.
How are you different from other video labeling vendors?
Abaka focuses on repeatable, secure, frontier-grade delivery rather than ad-hoc labeling. You get Abaka Forge workflows for intake, routing, review, exports, and dataset versioning—plus multi-layer QA that evaluates temporal consistency, not just single frames. Security and provenance are built into operations (SOC 2, ISO 27001, GDPR, CCPA), and we never build models that compete with you—your data is never repurposed, resold, or shared. The result is datasets you can reproduce, audit, and scale without quality drift.
Can we request changes if our ontology or guidelines evolve?
Yes—change requests are expected, especially in early iterations. We version your labeling spec and tie tasks to guideline versions so you don’t end up with mixed-policy datasets. When you add classes, attributes, or new event definitions, we run a controlled recalibration: update documentation, adjust QA checks, and label a small validation slice before scaling changes across the backlog. If you need re-labeling, we’ll scope it explicitly so you understand cost, timing, and which dataset versions are impacted.
Do you offer a pilot project for video annotation?
Yes. Pilots typically run on a representative sample of clips that cover your edge cases—occlusions, rare events, motion blur, and difficult lighting. The goal is to validate schema definitions, export formats, and QA acceptance criteria before scaling. We deliver pilot outputs plus a short findings report: ambiguity hotspots, proposed guideline clarifications, and a recommended QA plan for production. After pilot approval, we ramp into weekly releases so your team can iterate on models quickly with stable ground truth.
Who owns the labeled data and outputs?
You do. Abaka’s policy is that your data is exclusively yours—never repurposed, resold, or shared. We operate under strict NDAs and provide full IP provenance to support defensible datasets. Deliverables include the annotations, exports in your required formats, and documentation (label maps, specs, change logs) that enables reproducibility. If you need specific language around IP ownership, retention, or deletion, we can align it with your procurement and legal requirements during contracting.
What tools do you use for video annotation projects?
We run projects on Abaka Forge—our all-in-one platform for collection, cleaning, annotation, and production workflows. Forge supports video, image, text, RLHF, and 3D/4D point cloud programs with task routing, reviewer queues, audit trails, and export controls. Depending on the workflow, we can incorporate automation assists to accelerate suitable steps (while keeping humans in the loop for precision). Your team can define acceptance checks, receive consistent exports, and track dataset versions without stitching together multiple systems.
What is the minimum project size to work with Video Annotation Experts?
There’s no one-size minimum, but the most effective engagements start with a pilot that is large enough to expose edge cases and validate exports—often a curated set of sequences rather than a handful of frames. If you’re exploring feasibility, we can scope a small pilot with clear acceptance criteria and a fast turnaround, then expand once the schema is stable. For ongoing programs, we recommend a weekly cadence so QA signals and guideline updates can compound over time while your dataset scales predictably.