How much does an ML data labeling specialist cost?
Pricing depends on modality, complexity, and QA depth, but Abaka provides clear, real units you can budget against. For example, LLM math/coding labeling can be $18/hr, STEM generalist work can be $12/hr, dense captioning can be $6/hr, and image editing can be $8/hr. For autonomy programs, road lane annotation can be priced at $3/km. We’ll recommend the right unit model (hourly, per-eval, or per-km) after reviewing samples and acceptance criteria so cost aligns to measurable quality.
How fast can you deliver labeled data for my project?
Most teams see initial production-ready batches in 2–3 weeks after kickoff, depending on task complexity and how quickly guidelines stabilize. We typically use the first days to scope and define acceptance criteria, then run a pilot to calibrate rubrics and gold sets, and then scale production with multi-layer QA. If you already have mature guidelines and a stable ontology, timelines can compress. If tasks are novel or require heavy adjudication, we’ll plan phased deliveries so your training can start while refinement continues.
What data modalities and output formats do you support?
Abaka supports text, LLM RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion, and audio. We deliver common training-ready outputs such as JSONL, CSV/TSV, COCO JSON, YOLO TXT, segmentation masks, and synchronized manifests for multimodal datasets. For specialized pipelines, we can match your schema and provide transformation layers so your team doesn’t spend weeks writing converters. Abaka Forge keeps tasks, rubrics, and exports versioned so changes remain traceable.
What accuracy can I expect from your labeling teams?
Accuracy depends on task ambiguity, guideline clarity, and the difficulty of edge cases, but Abaka targets up to 99% accuracy on calibrated tasks with multi-layer QA. We use gold sets, rater calibration, peer review, and expert adjudication to control drift. We also track disagreement rates and error categories, then update rubrics with change control so improvements don’t break reproducibility. For novel tasks, we recommend a pilot batch first to quantify expected accuracy and define measurable acceptance thresholds before scaling.
How do you handle security and compliance for sensitive datasets?
Abaka is built for enterprise data handling with SOC 2 and ISO 27001 controls and alignment with GDPR and CCPA requirements. We operate under strict NDAs, use segregated secure pipelines, and apply role-based access so only approved specialists can view data. Workflows in Abaka Forge maintain audit logs and versioned artifacts for traceability. Importantly, Abaka never builds models that compete with you; your data is exclusively yours and is never repurposed, resold, or shared.
Do you support multilingual labeling and global coverage?
Yes. Abaka supports multilingual programs across 50+ countries, including localization-aware guidelines and region-specific edge cases. For text and audio, we can run language-qualified annotators and reviewers, and for vision tasks we can incorporate locale-specific signage, retail packaging, or cultural context into the ontology. We also maintain consistent QA logic across languages so your datasets remain comparable. If you need a staged rollout, we can start with priority markets and expand once rubrics are proven.
How are you different from other data labeling vendors?
Abaka focuses on trustworthy delivery for frontier AI: vertically specialized annotators, scholar-network reviewers for complex domains, and governed workflows in Abaka Forge. We also emphasize data ownership and trust—Abaka never builds models that compete with you, and your data is never repurposed, resold, or shared. Operationally, we combine multi-layer QA with throughput discipline to protect accuracy at scale. Finally, we support the full lifecycle—collection, cleaning, annotation, and evaluation—so you avoid fragile multi-vendor handoffs.
Can I request changes if my guidelines evolve mid-project?
Yes—change requests are expected in real ML programs, and we manage them with version control. When guidelines change, we update rubrics, retrain relevant annotators, and document decisions in an issue log. We can also run targeted relabeling for impacted slices, rather than redoing everything. Abaka Forge keeps deliveries tied to guideline versions so your training runs remain reproducible. This approach reduces silent schema drift and helps your team compare model performance across dataset iterations.
Can we start with a small pilot before scaling?
A pilot is the fastest way to de-risk quality and cost. We typically run a focused pilot batch to validate the ontology, measure disagreement, and tune acceptance criteria. You’ll review samples, we’ll identify top error classes, and we’ll adjust rubrics and QA gates before scaling. The pilot also clarifies staffing: whether you need generalists, domain specialists (e.g., math/coding), or expert adjudicators. After the pilot, we provide a production plan with predictable throughput and delivery cadence.
Who owns the labeled data and derived artifacts?
You do. Abaka’s operating model is designed so your data is exclusively yours—never repurposed, resold, or shared. We work under strict NDAs and maintain segregated pipelines to prevent cross-customer exposure. Deliverables include labels, rubrics, and QA artifacts according to your contract, and we can support retention or deletion requirements based on your policies. If you need full IP provenance documentation, we provide it so downstream usage is clear and defensible.
What tools do you use for annotation and QA workflows?
Abaka uses Abaka Forge, our all-in-one platform for collection, cleaning, annotation, and production delivery across text, RLHF, image, video, and 3D/4D. Forge supports role-based access, audit logs, and versioned guidelines, and it can automate routine steps using large-model assistance to speed delivery. If your team uses internal tooling, we can still deliver in your preferred schemas and coordinate review via exports and sampling plans, but Forge keeps the operational backbone consistent.
What is the minimum project size you can support?
We support both pilots and large-scale production programs. Minimum size depends less on raw volume and more on whether the task requires specialist calibration, custom rubrics, or multimodal setup. Many teams begin with a small pilot batch to confirm acceptance criteria, then scale once quality is proven. If you only need a small number of high-complexity labels (e.g., expert adjudication or math/coding evaluation), we can scope a specialist-only engagement. Talk to an Expert and we’ll propose a right-sized plan.
