How much does an ML data labeling provider cost?
Pricing depends on modality, complexity, and the level of expert review required, but Abaka offers clear unit economics. Examples include LLM math/coding annotation at $18/hr, STEM generalist work at $12/hr, dense captioning at $6/hr, and road lane labeling at $3/km. For some model-evaluation tasks, red teaming can be $8/eval and defensive coding $15/eval. During scoping, we’ll propose the most cost-effective workflow, including automation inside Abaka Forge where it fits, then confirm pricing before production.
How fast can you start and deliver the first batch?
Most teams can start quickly once access and specs are confirmed. In a typical engagement, Day 0–3 covers scope, secure setup, and guideline finalization. Week 1–2 runs a pilot with calibration, reviewer adjudication, and QA reporting. After pilot approval, production scale commonly ramps in 2–3 weeks, depending on modality and task complexity. If you already have clear specs and a ready dataset, we can compress timelines by reusing proven workflows and focusing on calibration plus acceptance testing.
What data types and formats do you support for labeling handoff?
Abaka supports text, LLM RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion, and audio workflows. Common outputs include JSONL/CSV/Parquet for text and RLHF, COCO JSON and YOLO TXT for image tasks, mask PNGs for segmentation, and frame-indexed JSON/CSV timelines for video. For 3D and fusion, we deliver timestamped annotation bundles with consistent coordinate conventions and track metadata. We’ll align outputs to your training stack and document schemas for repeatability.
What accuracy level can you achieve on data labeling projects?
Abaka targets up to 99% accuracy on suitable tasks using multi-layer QA, gold-standard calibration, and reviewer adjudication. Actual outcomes depend on label ambiguity, ontology design, and the quality of input data, so we validate accuracy during the pilot. We reduce error rates by enforcing clear decision rules, limiting per-annotator throughput to protect focus, and escalating ambiguous cases to domain reviewers. You also receive QA artifacts—disagreement categories, confusion matrices (where applicable), and sampled audits—so you can measure and improve over time.
How do you protect sensitive data during labeling?
Abaka operates with SOC 2 and ISO 27001-aligned controls, supports GDPR and CCPA requirements, and uses strict NDAs plus segregated secure pipelines. Access is restricted by role, tasks can be isolated into dedicated pods, and audit logs support traceability. We also emphasize full IP provenance and 0% copyright risk for collected data, reducing legal exposure when datasets are used for training and release. During kickoff, we align with your security team on data handling, retention, and review procedures.
Do you support multilingual labeling and localization?
Yes. Abaka supports multilingual text labeling, localization-sensitive taxonomies, and region-specific guidelines, drawing on annotator coverage across 50+ countries. We can run language-specific calibration and reviewer workflows to maintain consistency across locales, and we’ll help you define where semantics should be shared globally vs localized (for example, intent categories or safety policies). Deliverables can be normalized into a single schema with language and region metadata, enabling cleaner training splits and more reliable evaluation across markets.
How is Abaka different from other data labeling companies?
Abaka is positioned as a trustworthy data partner for frontier AI: we never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. Operationally, we emphasize repeatability with calibration, multi-layer QA, and throughput limits that protect quality at scale. You also get Abaka Forge as a standardized workflow system across modalities, plus access to scholar-network domain specialists for high-difficulty tasks like coding, mathematics, and specialized vertical labeling.
Can we request guideline changes or label taxonomy updates mid-project?
Yes—change requests are common as models surface new failure modes. We manage changes through controlled versioning: updated specs, new calibration rounds, and a defined cutover point so you can track which batches follow which rules. If a change impacts previously labeled data, we propose a relabel or partial remediation plan tied to measurable acceptance criteria. This approach prevents silent label drift, keeps evaluation consistent, and helps your team maintain clean experiment comparisons across iterations.
Can we run a small pilot before committing to a larger program?
Yes. We typically recommend a pilot to validate ambiguity hotspots, measure QA outcomes, and confirm output formats. The pilot includes gold-set creation, annotator calibration, reviewer adjudication, and a QA report with recommended guideline refinements. You’ll see concrete samples and metrics before scaling. After pilot approval, we ramp capacity using vetted pods and keep the same spec and QA gates so production behaves like an expanded version of the pilot—not a different process.
Who owns the labeled data and can it be reused by the vendor?
You own your labeled outputs and the derived datasets. Abaka’s policy is that your data is exclusively yours—never repurposed, resold, or shared—and we do not build models that compete with you. We also provide IP provenance practices designed to reduce copyright risk, especially when data is collected or curated. If your legal team needs specific contract language around ownership, retention, deletion, and audit rights, we can align those terms during procurement.
What tooling do you use for data labeling and quality control?
We use Abaka Forge, our all-in-one platform for collection, cleaning, annotation, training, and production workflows. Forge supports multi-layer QA, reviewer routing, audit logs, and standardized exports across text, RLHF, image, video, 3D/4D point cloud, fusion, and audio. Automation can accelerate repetitive steps (with human verification), and structured QA artifacts help your team diagnose systematic errors. If you have an existing labeling stack, we can align exports and integrate handoffs to minimize workflow disruption.
What is the minimum project size you can support?
We support both small pilots and scaled production programs. Minimum size depends on modality and complexity, but we can start with a targeted pilot batch designed to validate guidelines, QA gates, and output formats. For teams with uncertain requirements, we recommend beginning with a focused scope—one taxonomy, one modality, and a representative sample—then expanding once acceptance criteria are proven. This reduces risk, gives you cleaner cost forecasts, and ensures early outputs are directly usable for training and evaluation.
