How much does a model training labels service provider cost?
Pricing depends on modality, complexity, and the skill level required, but we use clear rate cards rather than vague “per label” guesses. For example, LLM Math/Coding labeling can be $18/hr, STEM Generalist work $12/hr, dense captioning $6/hr, and road-lane annotation $3/km. We’ll recommend a mix of roles (generalists + specialists + reviewers) so you pay for precision only where it improves model outcomes. Talk to an Expert to get a scoped estimate from a sample batch.
How fast can you deliver training labels after kickoff?
Most programs start with a fast scoping and pilot phase so your team can validate guideline clarity and QA gates before scaling. Typical timelines are Day 0–3 for specification and access setup, Week 1–2 for pilot + calibration, and Week 2–3 to ramp into steady production drops. If you already have stable guidelines, we can compress the pilot and start full production sooner. You’ll receive labels in frequent drops so training can start before the full dataset completes.
What data types and formats do you support for labeling outputs?
We support text, LLM RLHF, images, video, 3D/4D point clouds, LiDAR + camera fusion, and audio. Output formats commonly include JSONL and CSV for text/RLHF, COCO JSON and Pascal VOC XML for images, timeline JSON and per-frame exports for video, and PLY/PCD-linked JSON for 3D. If your pipeline uses a custom schema, we’ll map annotations to your structure and include audit metadata (guideline version, reviewer status, and adjudication notes) where useful.
What accuracy can you achieve for model training labels?
Accuracy depends on task ambiguity, guideline specificity, and edge-case rate, but we regularly target up to 99% accuracy on agreed label types using multi-layer QA. We get there through calibration batches, gold-task injection where appropriate, multi-reviewer consensus, and adjudication by senior reviewers. For inherently subjective tasks (e.g., nuanced preference ranking), we focus on rubric adherence and inter-annotator agreement metrics instead of claiming unrealistic perfection. We’ll align on measurable acceptance criteria before scaling.
How do you keep our training data secure during labeling?
Abaka operates with SOC 2 and ISO 27001 controls, GDPR and CCPA alignment, strict NDAs, and segregated secure pipelines. Access is provisioned with least-privilege principles, and workflows are designed to minimize unnecessary exposure while maintaining auditability. We also provide full IP provenance and do not introduce copyright risk when collecting data. Importantly, we never build models that compete with you—your data remains exclusively yours and is never repurposed, resold, or shared.
Can you label multilingual data and handle regional nuance?
Yes. With coverage across 50+ countries, we can staff native or fluent annotators and reviewers for multilingual classification, extraction, translation QA, and safety labeling. We recommend building language-specific calibration sets to avoid “literal but wrong” interpretations, and we track disagreement by locale so your team can see where guidelines need localization. Deliverables can include language IDs, dialect tags, and reviewer notes to support targeted rework or balanced sampling for training.
How are you different from other data labeling vendors?
Two differences matter most: trust and operational rigor. Abaka is built as a trustworthy data partner for frontier AI—SOC 2/ISO 27001-aligned, with strict NDAs, segregated pipelines, and full IP provenance. And we never build models that compete with you, so your data is never repurposed or resold. Operationally, we standardize calibration, adjudication, and slice reporting in Abaka Forge so quality is measurable and improvable—rather than relying on opaque “black box” delivery.
What if we need changes or rework after labels are delivered?
Change requests are normal—taxonomies evolve as your model learns. We handle this with versioned guidelines and controlled rollouts so you can update definitions without fragmenting the dataset. When rework is required, we scope it by slice (e.g., specific intents, rare classes, low-light frames) and route it through adjudication to keep decisions consistent. We can also produce delta exports so your team can patch training sets without re-ingesting everything.
Can we start with a small pilot before scaling?
Yes—starting with a pilot is recommended. A pilot batch lets you validate guideline clarity, reviewer alignment, output formats, and QA thresholds before committing to large volume. We typically run calibration tasks early, then iterate on edge-case handling and acceptance criteria. You’ll receive pilot outputs with audit metadata and feedback on ambiguity hotspots, which often improves your label spec and reduces later rework. Once the pilot meets targets, we scale production while keeping the same QA gates.
Who owns the labeled data and can you reuse it?
You own your data and the resulting labels. Abaka does not repurpose, resell, or share your dataset—ever. We also never build models that compete with you, eliminating conflict-of-interest risk common with some vendors. If you need formal IP clauses, we align them in the MSA and NDAs. We can also support provenance documentation and audit trails so you can demonstrate dataset ownership and governance internally and to external stakeholders.
What tooling do you use for annotation and project management?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, training, and production workflows across text, RLHF, image, video, and 3D/4D point cloud. Forge supports reviewer gates, adjudication, guideline versioning, and automation where appropriate (up to 50x faster for repetitive steps). If you have an internal toolchain, we can still deliver in your formats and integrate via exports and manifests, keeping your downstream training pipeline unchanged.
Is there a minimum dataset size to work with your labeling team?
There’s no strict minimum—what matters is whether we can define success criteria and run a meaningful calibration loop. We often start with a pilot of a few hundred to a few thousand items to validate guidelines and QA. If your project is very small (e.g., under a few hundred items), we’ll recommend a lightweight workflow that focuses on correctness and review rather than heavy ops. For large programs, we design throughput targets and staffing plans to match your delivery cadence.
