How much do ML data labeling experts cost?
Pricing depends on modality, complexity, and the QA depth you require. For example, Abaka can staff LLM math/coding work at $18/hr and STEM generalist work at $12/hr, while image editing tasks can be $8/hr and dense captioning can be $6/hr. For autonomy-style work, road lane labeling can be priced at $3/km. After a short sample review, we propose a scoped plan with clear deliverables, QA gates, and an estimated run-rate so you can forecast spend.
How fast can you start and when will we see first outputs?
Most teams can start within days once scope and access are confirmed. In Day 0–3, we align on taxonomy, guidelines, and export formats; in Week 1, you typically receive a pilot batch to validate quality and edge cases. Scale delivery usually stabilizes by Week 2–3 depending on volume and the number of modalities. We prioritize predictable weekly drops with QA reporting, so your training and evaluation cadence stays consistent rather than “bursty.”
What data types and output formats do you support for labeling?
We cover text, RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion workflows, and audio. Output formats are tailored to your pipeline—commonly JSONL/CSV for text and RLHF, COCO/YOLO/VOC plus masks for images, per-frame JSON for video, and structured JSON sidecars for 3D and sensor fusion. We document schemas and keep format versions stable across batches, so your ingestion and training jobs don’t break when guidelines evolve.
What accuracy can we expect from your data labeling experts?
Accuracy depends on task ambiguity, guideline maturity, and the strength of the QA gates, but Abaka programs are designed to target 99% accuracy on audited samples with multi-layer QA. We use calibrated reviewers, seeded audits, and structured adjudication to control drift. During the pilot, we establish measurable acceptance criteria and track disagreement and rework rates so you can see where the task definition needs refinement before scaling to large volumes.
How do you keep our data secure during labeling?
Abaka operates with SOC 2 and ISO 27001 controls, strict NDAs, and segregated secure pipelines. We support access control by role and project, and we can align delivery workflows to your governance requirements. We also maintain full IP provenance, and we never repurpose, resell, or share your data. If needed, we can scope additional operational controls around storage location, reviewer access, and audit artifacts to match internal security reviews.
Do you support multilingual labeling and evaluation?
Yes. Abaka supports multilingual programs through a global workforce spanning 50+ countries. We can label and evaluate datasets across languages for tasks like classification, extraction, translation QA, and RLHF-style preference judgments. The key is consistent guidelines and reviewer calibration: we align language-specific examples, define acceptance criteria, and add targeted audits for locale-specific ambiguity. Outputs include language metadata so your team can slice performance and identify gaps by region or script.
How are you different from other data labeling companies?
Abaka is built for teams that need trustworthy data rather than “labels at any cost.” We combine vertically specialized annotators with multi-layer QA and versioned guidelines, and we deliver through Abaka Forge so workflows are repeatable across modalities. We also never build models that compete with you, and your data is exclusively yours—never repurposed or resold. Finally, our compliance posture (SOC 2, ISO 27001, NDAs, segregated pipelines) reduces vendor risk for enterprise deployments.
What if our labeling guidelines change mid-project?
Change requests are expected—especially when model feedback reveals new edge cases. We handle changes by versioning guidelines, documenting what changed, and rolling updates through a controlled process so annotators don’t diverge. If changes affect previously labeled data, we propose a targeted relabel plan (not a full redo) and quantify the impact on schedule and cost. This approach keeps your dataset consistent while still allowing rapid iteration as your product and evaluation criteria evolve.
Can we run a small pilot before committing to a large dataset?
Yes. A pilot is the fastest way to validate labeling definitions, QA gates, and export formats. We typically run a focused batch during Week 1–2 and report on agreement rates, rework causes, and time-per-item by category. You get sample outputs you can use in training/evaluation, plus recommendations on guideline refinements before scale. If the pilot passes acceptance thresholds, we ramp production with the same tooling and reporting so the transition is smooth.
Who owns the labeled data and can it be reused elsewhere?
You own the labeled outputs and the underlying data you provide. Abaka does not repurpose, resell, or share your datasets—your data is exclusively yours. We also maintain full IP provenance for collected data so you can track origin and usage rights. If your team needs specific contract language around ownership, retention, deletion, and audit trails, we can align the delivery process to those requirements as part of onboarding.
What tools and workflows do you use for labeling projects?
We run projects in Abaka Forge, our platform for collection, cleaning, annotation, and production workflows across text, image, video, 3D/4D point cloud, and RLHF. Forge supports reviewer queues, audit sampling, adjudication, and export management. Large-model automation can accelerate repetitive steps while keeping humans in the loop for high-stakes judgments. Your team gets visibility into throughput, QA metrics, and change logs so decisions are traceable over time.
Is there a minimum dataset size or minimum engagement?
We support both small pilots and large-scale production, but the best-fit minimum depends on complexity and the overhead of guideline calibration and QA setup. If you have a small dataset, we can scope a pilot that proves feasibility and establishes acceptance thresholds, then scale only if it creates value. For large datasets, we design a cadence that fits your training schedule and avoids volume spikes that reduce consistency. Share your approximate volume and modality, and we’ll recommend the right starting point.
