How much do ML data labeling services cost?
Pricing depends on modality, complexity, and the QA bar you need, but we anchor costs to real, auditable units. For example, LLM math/coding labeling can be priced at $18/hr, STEM generalist work at $12/hr, dense captioning at $6/hr, and image editing at $8/hr. For automotive perception, road lane labeling is available at $3/km. We’ll propose a blended rate card after a Day 0–3 scope, and we can run a small pilot first to validate quality and throughput before scaling.
How fast can you start and deliver the first labeled batch?
Most teams can start within Day 0–3 for scoping, access, and schema alignment, then receive an initial pilot batch in Week 1–2. Production ramp typically begins in Week 2–3 once guidelines and QA gates are calibrated. Timeline varies by modality and how quickly label definitions are approved, but the goal is always the same: deliver early batches quickly so you can train, evaluate, and refine before committing to full-scale production.
What data types and output formats do you support?
We support text, LLM RLHF, images, video, 3D/4D point clouds, LiDAR + camera fusion, and audio. Output formats are tailored to your pipeline: JSONL/CSV for text and RLHF, COCO/YOLO/VOC-style outputs for vision, per-frame JSON and timeline CSV for video, and structured JSON plus manifests for 3D and fused sensor work. We also include batch manifests and audit metadata so datasets are traceable and reproducible over time.
How do you ensure labeling accuracy and consistency?
We treat accuracy as a system, not a promise. Abaka uses rubric-driven guidelines, calibration tasks, gold data, blind audits, and multi-layer QA with targeted rework queues. We cap throughput at 500 files/day per annotator to reduce fatigue-driven errors and maintain reviewer escalation for edge cases. For teams targeting 99% accuracy, we define what accuracy means per task and report it weekly, along with confusion patterns and guideline improvements.
Can you meet enterprise security requirements for sensitive datasets?
Yes. Abaka operates with SOC 2 and ISO 27001-aligned practices, GDPR and CCPA support, strict NDAs, segregated secure pipelines, and audit-friendly controls. We can enforce role-based access, least-privilege workflows, and structured reporting for governance. We also maintain full IP provenance and do not repurpose or resell your data. If you have additional internal controls, we’ll map them during Day 0–3 and incorporate them into the delivery plan.
Do you support multilingual labeling and localization?
Yes. We support multilingual text annotation, translation QA, sentiment/intent labels, and language-specific normalization with annotators across 50+ countries. We localize guidelines with language-specific examples while keeping a consistent global schema, which helps you train models that generalize across regions. Deliverables can include language tags, span offsets, and structured rationales so you can analyze disagreement patterns and improve prompts or model behavior per locale.
How are you different from other data labeling companies?
Abaka is positioned as a trustworthy data partner for frontier AI: founded in 2019, self-funded and profitable, with enterprise-grade compliance and strict NDAs. We never build models that compete with you—your data is exclusively yours and never repurposed, resold, or shared. Operationally, we combine domain-specialist annotators with a platform workflow (Abaka Forge) that keeps guidelines, QA, and audit trails tightly managed across modalities.
What if we need changes to the taxonomy or guidelines mid-project?
Change requests are expected—models evolve and edge cases appear. We manage updates through controlled guideline revisions in Abaka Forge, followed by re-calibration tasks and reviewer alignment to prevent drift. We can also reprocess impacted subsets through targeted rework queues rather than redoing everything. Every change is tracked with a versioned change-log and batch manifests, so you can reproduce training runs and understand which label policy produced which model outcome.
Can we run a pilot before committing to a large labeling engagement?
Yes. A pilot is the fastest way to validate label definitions, QA thresholds, and delivery formats before scaling. In Week 1–2, we deliver a representative batch with measurable QA, disagreement analysis, and a refined guideline set. You’ll see how edge cases are handled and how outputs integrate into your training pipeline. After the pilot, we provide a scale plan: staffing, weekly throughput targets, QA gates, and the cadence for delivery and reporting.
Who owns the labeled data and derived datasets?
You do. Abaka’s policy is that your data is exclusively yours—never repurposed, resold, or shared. We operate under strict NDAs and segregated pipelines, and we provide full IP provenance for delivered datasets. If you supply source data, we treat it as your IP; if we collect data on your behalf, we ensure 0% copyright risk on collected data and deliver provenance records alongside the dataset for auditability.
What tools do you use for labeling and QA?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, and production workflows across text, image, video, 3D/4D point cloud, and RLHF. Forge centralizes guidelines, reviewer workflows, escalation, and audit logs so quality stays consistent across large teams. If you need specific export schemas or integration steps, we adapt outputs to match your training stack while keeping internal governance consistent.
Is there a minimum project size for ML data labeling services?
There’s no single minimum, but the best fit is when you need repeatable quality and governance—not just a one-off batch. We commonly start with a pilot sized to validate your taxonomy and QA bar, then scale to production. If your project is small, we can still help by focusing on high-impact tasks: defining guidelines, labeling a gold dataset, or creating evaluation sets. We’ll recommend the smallest scope that still produces reliable, training-ready outputs.
