How much does an ml data labeling vendor cost per hour or per task?
Pricing depends on modality, complexity, and QA depth, but Abaka uses transparent, referenceable rates. For example, LLM Math/Coding annotation is $18/hr, STEM Generalist work is $12/hr, Dense Captioning is $6/hr, and Image Editing is $8/hr. For automotive perception, Road Lane labeling is priced at $3/km. If you use Abaka Forge, credits are $0.20 USD each. We’ll recommend the most cost-efficient mix after a Day 0–3 scoping sprint and a small pilot.
How fast can you start and deliver the first labeled batch?
Most teams can start with scoping in Day 0–3, then receive a pilot batch during Week 1–2. After you approve the rubric and acceptance criteria, production ramp typically begins Week 2–3. The exact timing depends on data access setup, guideline maturity, and the level of adjudication required for edge cases. We prioritize fast, calibrated pilots because they reduce downstream relabeling and make scaling more predictable.
What data modalities and output formats do you support?
Abaka supports text, LLM RLHF, images, video, 3D/4D point clouds, LiDAR + camera fusion, and audio. Output formats include JSONL, CSV/TSV, COCO JSON, Pascal VOC XML, PNG masks, SRT/VTT for audio/video, and client-defined schemas. We align formats and schemas during scoping so exports plug directly into your training, evaluation, and data catalog workflows with minimal glue code.
What accuracy can you guarantee for data labeling?
Abaka commonly targets 99% accuracy on agreed task definitions, measured via calibrated gold sets, sampling plans, and adjudication rules defined with your team. Accuracy is not a one-size number—different tasks have different ambiguity and base rates—so we define measurable acceptance criteria during the pilot. We also cap throughput at 500 files/day per annotator to reduce fatigue effects and maintain consistent decision quality at scale.
How do you protect sensitive or proprietary data during labeling?
We operate with SOC 2 and ISO 27001 aligned controls, GDPR and CCPA practices, strict NDAs, and segregated secure pipelines. Access is provisioned by role, and workflows are designed to minimize exposure while preserving auditability. We also provide full IP provenance and ensure your data is exclusively yours—never repurposed, resold, or shared. During scoping, we align on your security requirements and configure the pipeline accordingly.
Do you support multilingual labeling and non-English datasets?
Yes. Abaka supports multilingual programs with coverage across 50+ countries. We can staff language-specific annotators and reviewers, set language-specific rubrics, and apply consistent QA and adjudication to reduce cross-locale drift. For multilingual LLM work, we can support instruction following checks, preference labeling, and safety rubrics while maintaining consistent schema outputs so your evaluation and training pipelines remain comparable across languages.
How is Abaka different from other data labeling vendors?
Many vendors optimize for raw throughput; Abaka optimizes for audit-ready quality and trust. We never build models that compete with you, and your data is exclusively yours—never repurposed or resold. Operationally, we combine domain-specialized reviewers with multi-layer QA and Abaka Forge workflows to keep guidelines versioned and decisions traceable. This reduces relabeling cycles, makes evaluation defensible, and helps your team ship more reliably.
What happens if we need to change labeling guidelines mid-project?
Change requests are normal. Abaka manages them through versioned rubrics, controlled rollouts, and clear dataset versioning so you can compare model performance across label-policy changes. We typically pilot the change on a small batch, measure agreement and QA impact, then apply it to production once you approve. If a change affects previously delivered data, we’ll recommend a targeted backfill strategy to update only the impacted slices rather than relabeling everything.
Can we start with a pilot project before committing to a larger program?
Yes. We recommend a pilot during Week 1–2 to validate guidelines, outputs, and acceptance criteria before scaling. Pilots are designed to surface ambiguity early, establish gold tasks, and confirm that exports fit your training stack. After pilot sign-off, we can ramp production quickly while maintaining multi-layer QA and adjudication rules. This approach lowers risk and helps procurement evaluate quality and operational fit with real artifacts.
Who owns the labeled data and can it be reused elsewhere?
You own your data and the labeled outputs. Abaka does not repurpose, resell, or share your datasets—ever. We also maintain full IP provenance and can support 0% copyright risk on collected data when data collection is part of the engagement. This is especially important for teams training frontier models, where provenance and exclusivity are necessary for both legal and competitive reasons.
What tools and platforms do you use for data labeling?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, training handoffs, and production workflows across text, RLHF, image, video, and 3D/4D point cloud. Forge supports secure, segregated pipelines and export-ready outputs with audit logs. Large-model automation can speed up repetitive steps by up to 50x while preserving human oversight where needed. Forge credits are priced at $0.20 USD each for predictable scaling.
Is there a minimum project size for working with an ml data labeling vendor?
There’s no single minimum, but the best ROI typically starts with a pilot that is large enough to measure agreement and QA reliably. Many teams begin with a few hundred to a few thousand items (or a small number of videos/scenes) to validate rubrics, output formats, and operational cadence. Once the workflow is proven, we can scale to larger volumes quickly by expanding annotator capacity while keeping QA and adjudication consistent.
