How much do AI data annotation experts cost?
Pricing depends on modality, domain complexity, and QA depth, but we can anchor quickly with clear unit economics. For example, LLM Math/Coding annotation is $18/hr, STEM Generalist work is $12/hr, Dense Captioning is $6/hr, and Road Lane annotation is $3/km. Abaka Forge automation is available via credits at $0.20 USD each when applicable. After a Day 0–3 sample, we propose a scoped plan with throughput assumptions, QA sampling, and a total cost range for pilot and production.
How fast can you start and how long does a pilot take?
Most teams can start within days once scope, data access, and security requirements are confirmed. A typical pilot runs 1–2 weeks, including guideline drafting, rater calibration, and a first delivery batch for your review. Many programs reach production throughput in 2–3 weeks total by ramping capacity only after acceptance criteria are met. If you already have mature guidelines and export schemas, timelines can compress; if rubrics are new or highly technical, we spend more time on calibration to prevent drift.
What modalities and output formats do you support for annotation?
We support text, LLM RLHF, image, video, audio, 3D/4D point cloud, and LiDAR + camera fusion workflows in Abaka Forge. Outputs are delivered in practical formats your pipeline can ingest, including JSONL, CSV/TSV, COCO-style JSON, Pascal VOC XML, YOLO TXT, PNG masks, RTTM/TextGrid for audio, and client-defined schemas. During onboarding, we confirm naming conventions, class taxonomies, and required metadata (timestamps, frame IDs, sensor info) to avoid downstream reformatting work.
What accuracy can I expect from your AI data annotation experts?
Accuracy depends on task clarity, ambiguity in the source data, and how “correctness” is defined, but we run multi-layer QA systems designed to achieve high consistency at scale. Where suitable, Abaka supports 99% accuracy programs using calibrated gold sets, reviewer gates, and structured defect feedback loops. For tasks with inherent subjectivity (e.g., preference judgments), we focus on rater calibration, rubric specificity, and drift monitoring so results remain stable week to week. We’ll propose measurable acceptance criteria during the pilot.
How do you protect sensitive data during annotation projects?
Abaka operates with SOC 2 and ISO 27001 aligned practices and supports GDPR and CCPA requirements, with strict NDAs and segregated secure pipelines. Access is controlled by role, and workflows are designed to minimize unnecessary exposure while preserving auditability. We maintain clear IP provenance and handling rules, and we never build models that compete with you. Your data is exclusively yours—never repurposed, resold, or shared—so you can scale labeling on proprietary or sensitive datasets with lower security and legal friction.
Do you support multilingual annotation and non-English datasets?
Yes. Abaka staffs globally across 50+ countries, enabling multilingual labeling, translation review, and locale-specific rubric interpretation. We can run language-specific calibration so annotators apply consistent guidelines across regions, and we can segment QA by language to detect drift early. Outputs are delivered with language metadata and consistent schemas (e.g., JSONL/CSV). If your dataset includes mixed-language content, we can route tasks by language automatically and set different acceptance thresholds where linguistic ambiguity is higher.
How is Abaka different from other data labeling companies?
The differentiators are trust, domain matching, and production controls. Abaka is a trustworthy data partner for frontier AI and does not build models that compete with you; your data is exclusively yours and never repurposed. On delivery, we combine vertically specialized annotators with reviewer gates and calibration to reduce drift—especially in technical RLHF, math, and coding tasks. Finally, Abaka Forge provides unified workflows across modalities with large-model automation where it helps, while preserving auditable human judgment for high-stakes labeling.
What if we need to change guidelines or request relabeling mid-project?
Change is expected, so we handle it through controlled change management rather than ad-hoc rework. We version guidelines, track which batches were labeled under which version, and run calibration whenever a change affects decision boundaries. For relabeling, we can target only impacted subsets using defect categories and sampling rather than restarting the whole dataset. You’ll receive a clear plan outlining what changes, the expected impact on timelines and cost, and how we’ll prevent inconsistency between old and new labels.
Can we run a small pilot before committing to a larger annotation contract?
Yes—pilots are the standard way to de-risk quality and workflow fit. We typically start with a representative sample and a tightly scoped set of classes or rubrics, then deliver initial batches plus QA reports for your review. The pilot validates guideline clarity, export formats, and rater calibration before scaling. If you approve results, we ramp capacity while keeping the same QA gates and measurement plan. If issues appear, we refine rubrics and retrain annotators early—before volume makes corrections expensive.
Who owns the labeled data and can it be reused by the vendor?
You own your labeled data and associated outputs. Abaka’s operating principle is that your data is exclusively yours—never repurposed, resold, or shared. We also maintain clear IP provenance so you can trace what was produced, under what instructions, and with what approvals. Abaka does not build models that compete with you, which reduces incentives for reuse. Contractually, we support strict NDAs and can align on data retention and deletion requirements to meet your internal policies.
What tools do your AI data annotation experts use?
Projects run on Abaka Forge, our all-in-one platform for collection, cleaning, annotation, and production workflows across text, image, video, RLHF, and 3D/4D point cloud. Forge supports task routing, reviewer gates, calibration workflows, and export templates so your outputs match your pipeline. Large-model automation can accelerate repeatable steps—up to 50× faster—while humans handle expert judgments and QA. If you have a required schema or export structure, we configure Forge to deliver consistently from the start.
What is the minimum project size for working with AI data annotation experts?
There isn’t a single minimum, but the most efficient engagements start with a pilot sized to validate quality and workflow—often a few hundred to a few thousand items, depending on modality and complexity. For RLHF or technical evaluation, we may start smaller to ensure calibration before scaling. After the pilot, we can support sustained production from weekly refreshes to large backfills. If you’re unsure, we’ll recommend a pilot scope that produces statistically meaningful QA signals without overspending.
