How much does AI data annotation hiring cost with Abaka?
Pricing depends on task complexity, required expertise, and QA depth. As concrete anchors: LLM Math/Coding specialists are $18/hr, STEM Generalists are $12/hr, Dense Captioning is $6/hr, Image Editing is $8/hr, and Road Lane labeling can be $3/km when the unit is distance-based. Platform automation in Abaka Forge uses credits priced at $0.20 USD each. We typically propose a pilot budget first, then scale once quality targets and throughput are validated.
How long does it take to hire and onboard an annotation team?
Most teams can reach a calibrated pilot in about 2–3 weeks, depending on modality, domain complexity, and how mature your guidelines are. The timeline includes role definition, screening, calibration, and a pilot batch with multi-layer QA. If you already have stable specs and gold data, onboarding can be faster; if the taxonomy is still evolving, we’ll include extra time for rubric iteration and edge-case playbooks to prevent drift during scale-up.
What data types and formats can your hired annotators handle?
Abaka supports text, RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion, and audio workflows. Outputs are delivered in production-friendly formats such as JSONL/JSON, CSV/TSV/Parquet, COCO-style JSON for images, tracking exports for video, and structured manifests for multimodal datasets. If your pipeline requires a specific schema, we can map outputs during the pilot so your training and evaluation jobs ingest the data cleanly from day one.
What annotation accuracy can you guarantee?
Accuracy depends on task ambiguity, label density, and the clarity of guidelines, but Abaka supports 99% accuracy targets on applicable workflows through multi-layer QA and calibrated reviewers. We define acceptance criteria up front (sampling plans, audit rates, escalation rules), then validate them during a pilot batch before scaling. For inherently subjective tasks (some RLHF rubrics), we focus on consistent rubric interpretation, reviewer agreement, and drift monitoring rather than overstating a single universal number.
Is AI data annotation hiring secure for proprietary or sensitive data?
Yes—Abaka is designed for enterprise security reviews. We support SOC 2 and ISO 27001 aligned operations, GDPR and CCPA requirements, strict NDAs, and segregated secure pipelines with access control and auditability. We also maintain full IP provenance and 0% copyright risk on collected data. Just as importantly, Abaka never builds competing models: your data is exclusively yours and is never repurposed, resold, or shared.
Can you hire multilingual annotators for global datasets?
Yes. Abaka can staff multilingual annotation and RLHF evaluation across 50+ countries, including language-specific reviewers for guideline interpretation and QA. We recommend starting with a calibration set per language to catch translation ambiguities, culturally-specific content, and label-definition mismatches. For multilingual speech or TTS-related work, we can curate and label audio with language-aware QA so transcripts and rubrics stay consistent across locales.
How are you different from annotation marketplaces or BPO vendors?
Marketplaces typically give you profiles; you still manage screening, training, QA, and drift. Traditional BPO vendors often optimize for volume without deep specialization. Abaka combines a large specialized workforce with a production QA system and Abaka Forge workflows—covering calibration, multi-layer review, audit trails, and change management. We also differentiate on trust: we never build competing models, and your data is never repurposed, resold, or shared.
What happens if we need to change labels, guidelines, or rubrics mid-project?
Change is expected, especially for RLHF and evolving product policies. Abaka handles change requests through versioned guidelines, targeted retraining, and controlled rollout so quality doesn’t regress. We’ll identify which prior batches are affected, propose backfill or relabel strategies, and update QA sampling to focus on the new edge cases. Weekly reviews keep changes visible and measurable, preventing “silent drift” that can undermine training over time.
Can we start with a pilot before committing to a long engagement?
Yes. Most customers start with a pilot designed to validate three things: rubric clarity, achievable quality targets, and throughput. The pilot includes calibration, multi-layer QA, and delivery in your required formats, plus a quality report that highlights systematic errors and edge-case frequency. After the pilot, you can scale the team size and volumes without re-hiring, because the process and guidelines are already stabilized.
Who owns the annotated data and the IP created during the project?
You do. Abaka’s operating principle is that your data is exclusively yours—never repurposed, resold, or shared. We run strict NDAs and maintain full IP provenance so ownership is clear and defensible. If we build supporting artifacts like guideline documents, decision logs, and QA reports for your project, those are delivered to you as part of the engagement so your team can retain continuity across model versions.
What tools do you use to manage the annotation workforce and QA?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, and production workflows across text, RLHF, image, video, and 3D/4D point cloud. Forge supports role-based routing, reviewer escalation, audit sampling, and export mapping to your pipeline. It also provides large-model automation for selected steps to accelerate throughput, with credits priced at $0.20 USD each when automation is part of the workflow.
What is the minimum project size for AI data annotation hiring?
There isn’t a single minimum, but we recommend scoping a pilot that is large enough to expose edge cases and measure drift—often a few thousand items for text/image tasks or a smaller set for complex video/3D workflows. If you only need a short burst, Abaka can still help by staffing a small calibrated team with clear acceptance criteria. We’ll propose a minimum that meaningfully validates quality before you scale.
