How much do model training labels services cost?
Pricing depends on modality, rubric complexity, and reviewer depth, but we use clear, referenceable rate cards. For example, LLM Math/Coding labeling starts at $18/hr, STEM generalist labeling at $12/hr, dense captioning at $6/hr, and road lane annotation at $3/km. We’ll propose a blended plan after a short pilot so you see effective cost per accepted label and expected rework rates. Talk to an Expert to get a scoped estimate tied to your acceptance criteria and timelines.
How fast can you start and when do we see first deliverables?
Most teams can launch within Day 0–3 for scoping, security setup, and label-spec finalization, then receive pilot outputs during Week 1–2. For stable production throughput, a typical ramp is Week 2–3 after calibration and adjudication rules are approved. Exact timing depends on dataset readiness, rubric maturity, and whether you need multi-modality exports. We design the plan so you can start training on early batches while the pipeline scales.
What modalities and file formats do you support for training labels?
We support text, RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion, and audio. Output formats include JSONL, CSV/TSV, Parquet, COCO JSON, Pascal VOC XML, CVAT XML, and custom schemas you define. If you have an internal training-data contract, we’ll mirror it and add versioning so future rubric changes do not silently break downstream loaders. Abaka Forge helps manage consistent exports across teams and time.
How do you ensure labeling accuracy stays high at scale?
Accuracy comes from system design: calibrated guidelines, golden sets, inter-annotator agreement checks, and adjudication for high-disagreement samples. We also use multi-layer QA with targeted audits on long-tail slices (where models tend to fail) rather than only sampling easy cases. You receive weekly error categories and drift signals so we can fix rubric gaps quickly. The goal is stable, training-ready labels—so model deltas reflect learning, not label noise.
What security and compliance controls do you support?
Abaka operates with SOC 2 and ISO 27001-aligned controls, strict NDAs, segregated secure pipelines, and support for GDPR and CCPA obligations. Access is role-based, and workflows are auditable with task logs and reviewer traceability. We also provide full IP provenance and do not introduce copyright risk through questionable sourcing. If your program requires additional constraints (air-gapped workflows, restricted locations, or device controls), we can scope those during onboarding.
Can you label multilingual data and non-English edge cases?
Yes. Abaka supports multilingual labeling across 50+ countries, including localized intent, safety policy interpretation, and culturally sensitive content where direct translation is insufficient. We can provide language-specific rubrics, reviewer calibration per locale, and consistent schema mapping into a single training format. For mixed-language datasets, we also label language ID, code-switching segments, and normalization fields so training pipelines stay stable across regions and releases.
How are you different from other data labeling vendors?
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 combine scholar-network expertise for difficult domains (math, coding, medicine, law, languages) with multi-layer QA and Abaka Forge workflow controls. That means you get both scale and auditability, with fewer hidden failure modes from guideline drift and inconsistent reviewers.
How do you handle change requests when our labeling guidelines evolve?
Guidelines always evolve as your model learns. We version rubrics, update edge-case libraries, and run rapid recalibration so changes propagate within 24–72 hours, not weeks. For breaking changes, we propose backfill strategies—targeted relabeling on affected slices, dual-labeled transition windows, and clear cutover dates—so you can preserve comparability across training runs. Abaka Forge keeps the full history, making it easy to trace which rubric produced which labels.
Can we run a pilot before committing to a larger labeling program?
Yes—pilots are the default path for complex label specs. We typically run a Week 1–2 pilot that includes calibration, golden sets, adjudication rules, and an export contract that matches your training pipeline. You’ll see acceptance rates, common error types, and the operational cadence before scaling. After the pilot, we provide a production plan for Week 2–3 ramp with clear quality gates and weekly reporting so you can commit confidently.
Who owns the labeled data and can it be reused by others?
You own your labeled data. Abaka does not repurpose, resell, or share your datasets. We also do not build models that compete with you, so incentives remain aligned around your success. Where we collect data on your behalf, we maintain full IP provenance and document sourcing to eliminate copyright risk. Access controls, segregated pipelines, and audit trails ensure your data stays exclusive throughout the engagement.
What tooling do we get—do you provide an annotation platform?
Yes. Abaka Forge is our all-in-one platform for collection, cleaning, annotation, and production workflows across modalities (text, RLHF, image, video, and 3D/4D). It supports guideline versioning, reviewer calibration, adjudication, QA sampling, and consistent exports. If you already have internal tools, we can also deliver via your preferred formats and integrate with your storage and review workflows, while still running Abaka-managed QA and reporting.
What is the minimum project size for model training labels services?
We support both small, high-difficulty pilots and large-scale production. Minimums depend on modality and complexity, but many teams start with a focused pilot batch sized to validate rubrics and QA gates, then scale once acceptance criteria are proven. If you only need a narrow slice (e.g., long-tail error correction or safety labeling for a new policy), we can design a targeted engagement. Talk to an Expert and we’ll recommend the smallest plan that still produces reliable training outcomes.
