How much does AI data annotation hire cost with Abaka?
Pricing depends on modality, domain difficulty, and QA depth, but we use clear, real rate cards to keep costs predictable. Examples include STEM Generalist annotation at $12/hr and LLM Math/Coding at $18/hr. For certain autonomy tasks, road lane annotation can be priced at $3/km. We’ll recommend the most cost-effective mix of annotators, reviewers, and sampling strategy after reviewing your samples and acceptance criteria, then confirm a pilot budget before scaling.
How long does it take to ramp an annotation team after we hire?
Most programs start with scoping in Day 0–3, then a pilot in Week 1–2 to calibrate guidelines and QA. After pilot acceptance, many teams reach stable production scale in roughly Week 2–3, depending on complexity and volume. If your task includes multiple modalities or domain-heavy rubrics (e.g., coding or medical), ramp time can vary, but we prioritize early sample review and calibration to avoid expensive rework later. Weekly checkpoints keep iteration timelines predictable.
What data types and export formats do you support for annotation hire?
Abaka supports text, RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion, and audio. Common exports include JSON/JSONL, CSV, COCO JSON, YOLO TXT, Pascal VOC XML, masks (PNG), SRT/VTT, and modality-specific manifests. If your training pipeline expects schema-validated JSON or custom sidecars, we can align on the required structure during the Day 0–3 scoping phase and validate it during the pilot before scaling.
What annotation accuracy can you achieve and how is it measured?
Abaka targets high-precision delivery with multi-layer QA and supports 99% accuracy targets, measured against agreed acceptance criteria. We typically define quality via a combination of gold sets, sampling audits, reviewer adjudication, and disagreement analysis tied to rubric versions. Accuracy measurement is not a single global number—your team chooses what matters (class-level precision, boundary tolerance, rubric score reliability), and we operationalize it with consistent checks and weekly reporting.
Is Abaka secure enough for sensitive datasets and enterprise compliance?
Yes—Abaka operates with strict NDAs and segregated secure pipelines and supports compliance requirements aligned to SOC 2, ISO 27001, GDPR, and CCPA. We implement controlled access, audit trails, and clear operational controls for who touches what data and when. If you require additional vendor security reviews, we’ll map those needs during scoping and provide the documentation and workflow design needed to reduce delays. Your data remains exclusively yours and is never repurposed.
Do you support multilingual annotation and non-English datasets?
Yes. Abaka’s workforce spans 50+ countries, enabling multilingual data annotation hire for classification, NER, translation QA, and multilingual RLHF. We can match annotators and reviewers to language and regional context, which is critical for nuance, safety, and intent. For multilingual projects, we recommend defining language-specific rubrics and adding targeted QA sampling per locale to prevent silent drift. Deliverables can be organized by locale and exported in consistent schemas for training.
How is Abaka different from other data labeling vendors or marketplaces?
Most marketplaces optimize for staffing, not outcomes. Abaka provides a managed, auditable pipeline with Abaka Forge workflows, calibrated rubrics, and multi-layer QA designed for frontier AI teams. We also differentiate on trust: we never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. With Abaka, you get domain reviewers, secure delivery, and predictable iteration, not a loose collection of contractors.
How do change requests work once the project is in production?
Change requests are handled through versioned guidelines and controlled rollouts to prevent mixed definitions inside the same dataset. We’ll propose the update, test it on a small sample, and confirm acceptance criteria before scaling. If the change affects historical data, we can re-annotate targeted slices rather than redo everything, and we’ll track the affected batches and versions for auditability. Weekly checkpoints ensure your evolving model requirements translate cleanly into annotation rules.
Can we start with a small pilot before hiring a full annotation team?
Yes—starting with a pilot is the recommended path. In Week 1–2 we run a controlled pilot to validate rubrics, edge cases, and export compatibility, then use results to finalize QA thresholds and scaling plans. A pilot reduces risk by surfacing ambiguity early and letting your team assess quality before committing to volume. After pilot acceptance, we typically scale in Week 2–3 with stable staffing, throughput targets, and structured reporting.
Who owns the annotated data and can Abaka reuse it?
You own the data and the resulting annotations. Abaka does not repurpose, resell, or share your datasets, and we never build models that compete with you. We also emphasize provenance: projects are delivered with traceability and operational controls that help you understand how each batch was produced and which guideline version it followed. If you require specific IP terms or additional restrictions, we can align them in the SOW and operational workflow.
What tools do annotators use and can we integrate with our pipeline?
Annotation work runs on Abaka Forge, our platform for collection, cleaning, annotation, and production workflows across modalities. We support standard exports (e.g., JSONL, COCO, YOLO) and can validate schemas during the pilot so outputs land cleanly in your data lake, training jobs, or evaluation harness. If you need custom fields, naming conventions, or folder structures, we’ll implement them as part of the delivery spec and include them in QA checks.
What is the minimum project size for AI data annotation hire?
There’s no one-size minimum, but most teams get the best results with enough volume to justify calibration and QA—often a pilot batch plus a follow-on production tranche. Small projects (a few hundred items) are possible, especially for evaluation sets, but we still recommend clear acceptance criteria and a lightweight QA plan to avoid inconsistent outputs. During scoping, we’ll suggest a pilot size that’s large enough to cover edge cases and validate formats before scaling.
