How much does an ML data labeling partner cost?
Pricing depends on modality, complexity, and QA depth, but we use real, transparent rate cards and measurable acceptance criteria. Examples: LLM Math/Coding labeling can be $18/hr, STEM generalist work can be $12/hr, dense captioning can be $6/hr, and road lane annotation can be $3/km. If you use Abaka Forge credits for workflow automation and platform usage, credits are $0.20 USD each. We’ll propose a blended plan after a small Day 0–3 sample so you can validate quality before scaling.
How fast can you deliver labeled data after kickoff?
Most teams see a meaningful first delivery within 2–3 weeks, depending on dataset readiness and scope. We typically run a Day 0–3 sample to finalize guidelines and acceptance thresholds, then ramp production in Week 1–2 with calibrated reviewers and gold tasks. For urgent timelines, we can prioritize staffing and batch size while keeping QA gates intact. You’ll get a weekly delivery cadence once the workflow stabilizes.
What modalities and output formats do you support for labeling?
We support text, LLM RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion, and audio workflows. Output formats are tailored to your pipeline and commonly include JSONL/CSV for NLP and RLHF, COCO-style JSON/YOLO TXT/PNG masks for images, timecoded JSON for video, and JSON plus sensor-linked manifests for 3D and fusion datasets. If you have a custom schema, we can map exports to your spec and include validation checks in the delivery step.
How do you ensure labeling accuracy and consistency?
Accuracy comes from process, not hope. Abaka uses versioned guidelines, gold-task gating, reviewer calibration, and multi-layer QA that includes spot checks, consensus, and adjudication for disagreements. We track inter-annotator agreement and produce error taxonomies so you can see where ambiguity or policy gaps cause variance. For domain-sensitive tasks (math, coding, medicine, law), we route work to specialists and add dedicated review layers to maintain consistent decisions across batches.
Can you meet security and compliance requirements for sensitive data?
Yes. Abaka operates with SOC 2 and ISO 27001 aligned controls and supports GDPR and CCPA requirements. We use strict NDAs, segregated secure pipelines, and controlled access patterns for sensitive workflows. We also provide full IP provenance, including 0% copyright risk on collected data, and we do not repurpose or resell your data. If your org needs additional security steps (e.g., dedicated environments or stricter access segmentation), we can scope that during onboarding.
Do you support multilingual data labeling and global coverage?
Yes. Abaka supports multilingual labeling across 50+ countries, which helps when you need locale-specific intents, culturally appropriate RLHF judgments, or regionally grounded entity labeling. We can run language-specific guidelines, separate calibration per language, and consistent reviewer policies so output quality doesn’t vary by geography. Deliverables can include language tags, locale metadata, and stratified QA reporting so your team can track performance and coverage per market.
How is Abaka different from other data labeling companies?
Abaka is optimized for frontier-quality and trust. We pair large, specialized workforces with scholar-network domain expertise and multi-layer QA designed for correctness and consistency—not just raw volume. Our compliance posture includes SOC 2 and ISO 27001 aligned controls, and we provide full IP provenance. We also never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. Abaka Forge adds end-to-end workflow control with auditability.
How do you handle scope changes or labeling guideline updates mid-project?
We treat changes as versioned releases. Abaka logs guideline updates, clarifies impact (what needs relabeling and what doesn’t), and runs targeted backfills rather than broad relabeling when possible. We maintain dataset and guideline versions so your team can reproduce experiments and compare model results cleanly. Weekly change control checkpoints keep policy drift from creeping in, and we can create gold sets that act as regression tests when guidelines evolve.
Can we start with a small pilot before committing to a full program?
Yes—starting with a pilot is the recommended path. In Day 0–3, we label a representative sample, surface ambiguity, propose guideline improvements, and agree on acceptance thresholds. In Week 1–2, we ramp the workforce and QA layers and deliver a first production batch to validate stability. A pilot lets you evaluate quality, throughput, and reporting before scaling, and it reduces the risk of spending heavily on a spec that needs revision.
Who owns the labeled data and can it be reused elsewhere?
You own your data and the resulting labeled outputs. Abaka does not repurpose, resell, or share your datasets—your data is exclusively yours. We also never build models that compete with you, which removes incentives to treat customer data as a long-term asset for internal training. If your team requires specific IP clauses, retention policies, or deletion timelines, we can incorporate those into the project agreement and operational workflow.
What tools do you use for data labeling and QA management?
We use Abaka Forge to run collection, cleaning, annotation, QA, and delivery with audit trails and secure workflows. It supports text, RLHF, image, video, and 3D/4D point cloud labeling, and it can incorporate large-model automation to speed up repetitive steps while keeping human review in the loop. We provide export validation and reporting so your team receives consistent, production-ready outputs aligned to your schema.
Is there a minimum dataset size or minimum contract length?
We support both small and large engagements. Minimums depend on modality and the level of specialization required, but many teams start with a small pilot sample to validate guidelines and QA before scaling. If you only need a narrow dataset (e.g., a targeted evaluation set or a small RLHF batch), we can scope a lean plan with clear acceptance criteria. If you need sustained throughput, we’ll propose a weekly cadence that balances staffing, cost, and QA depth.
