How much does an ML data labeling firm cost?
Pricing depends on modality, complexity, and QA depth, but Abaka provides real, transparent baselines you can plan around. For example: LLM Math/Coding labeling can be $18/hr, STEM Generalist work $12/hr, Dense Captioning $6/hr, Image Editing $8/hr, and Road Lane annotation $3/km. If you use Abaka Forge automation, credits are $0.20 USD each. Most teams start with a scoped pilot so you can validate rubrics, throughput, and total cost before scaling.
How long does it take to start a labeling project?
Most teams can begin with a scoped kickoff in Day 0–3, followed by a pilot in Week 1–2. The pilot is where we validate guidelines, run calibration rounds, measure disagreement, and confirm export formats. Many programs reach scaled production in Week 2–3 once rubrics are stable and QA thresholds are met. If your security review requires additional artifacts, we can supply NDA terms, project isolation details, and audit controls early so timing stays predictable.
What data types and formats can you label and deliver?
Abaka supports text, image, video, audio, 3D/4D point cloud, LiDAR + camera fusion, and LLM RLHF workflows. Deliverables include common formats like JSONL/CSV for text and RLHF, COCO JSON and mask outputs for image segmentation, timestamped JSON/CSV for video events, and JSON manifests for 3D/4D labels. We also provide release notes, guideline versions, and QA exports so your engineering team can reproduce datasets and track changes between drops.
How do you measure labeling accuracy and consistency?
We treat accuracy as an operational metric: calibration rounds to align reviewers, gold sets to measure correctness, and sampling plans to detect drift as volume scales. Abaka targets up to 99% accuracy using vertically specialized annotators and multi-layer QA, including expert adjudication for ambiguous cases. You receive QA reports with disagreement themes and corrective actions, plus audit exports that show how many items were reviewed, why changes were made, and which guideline version governed each batch.
Is Abaka secure for sensitive or proprietary data?
Yes. Abaka operates with SOC 2 and ISO 27001 aligned processes, GDPR and CCPA compliance alignment, strict NDAs, and segregated secure pipelines. Access controls and audit trails are designed so your team can limit who sees what, and you can verify how data moved through the workflow. We also maintain full IP provenance and state clearly that your data is exclusively yours—never repurposed, resold, or shared—and we never build models that compete with you.
Can you support multilingual labeling and reviewers?
Yes. Abaka supports multilingual programs through a global workforce across 50+ countries. We can run language-specific calibration and guideline localization to keep intent and edge cases consistent across locales, which is critical for assistants, translation, and multilingual retrieval. For GenAI use cases, we can pair multilingual reviewers with domain specialists (for example, technical content or legal text) and provide structured exports that preserve locale metadata so your training and evaluation pipelines can segment performance accurately.
How are you different from other data labeling companies?
Abaka is designed for frontier AI needs: scholar-network expertise (math, coding, languages, medicine, law, science), multi-layer QA with auditability, and Abaka Forge workflows spanning collection, cleaning, annotation, and production. We also have a trust differentiator: we never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. Operationally, we focus on predictable pilots, drift control, and standard exports so you avoid lock-in and relabel churn.
What if we need to change the labeling guidelines mid-project?
Change requests are expected—especially once you see model errors and edge cases. Abaka handles this by versioning guidelines, re-running calibration when definitions change, and documenting release notes so your team knows which batches follow which rules. We can also run targeted backfills: instead of relabeling everything, we identify impacted subsets and update only what’s necessary for comparability. Weekly reporting includes a summary of guideline changes, adjudication outcomes, and any expected metric shifts caused by the update.
Can we run a pilot before committing to a large labeling program?
Yes—most teams start with a pilot in Week 1–2 after a Day 0–3 kickoff. The pilot validates task design, reviewer calibration, throughput, QA thresholds, and export formats. You’ll receive sample labels, audit outputs, and recommendations for scaling—including staffing plans and where automation makes sense. This de-risks the full program and helps you forecast cost and timeline with real data, not estimates. After the pilot, scaling typically begins in Week 2–3.
Who owns the labeled data and can it be reused elsewhere?
You own your data and outputs. Abaka’s policy is that your data is exclusively yours—never repurposed, resold, or shared. We also never build models that compete with you, so there’s no incentive to reuse your assets. For additional assurance, we work under strict NDAs and can structure segregated secure pipelines so each project is isolated. Deliverables are provided in standard formats with clear provenance and audit trails so your team can store, reproduce, and govern datasets internally.
What tools do you use for labeling and QA?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, training, and production. It supports text, image, video, 3D/4D point cloud, and RLHF workflows with built-in queue management, guideline and template control, and QA features like sampling, gold sets, and adjudication. Automation can accelerate repetitive steps up to 50× while maintaining human oversight. Exports are produced in standard formats so your ML pipeline can ingest them without custom vendor-specific dependencies.
What is the minimum project size you can support?
There’s no one-size minimum, but projects typically start with a pilot batch sized to validate guidelines and QA—often a few thousand items for text/image tasks, or a smaller number of higher-effort samples for video and 3D. The right minimum depends on label complexity and the variance of your data. We’ll recommend a pilot size that can reveal disagreement patterns and throughput constraints (for example, per-annotator limits around 500 files/day) so you can scale confidently after the first results.
