How much does an ML data labeling company cost?
Pricing depends on modality, difficulty, QA depth, and whether you need specialist reviewers. Abaka offers real, predictable rates such as $18/hr for LLM Math/Coding labeling, $12/hr for STEM generalist work, $6/hr for dense captioning, $8/hr for image editing tasks, and $3/km for road lane labeling. For model evaluation, common rates include $8/eval for red teaming and $12/eval for math capabilities. We’ll propose a blended rate and throughput plan after a short scoping call and pilot.
How fast can you start and deliver labeled data?
Most teams can start with a scoped pilot within days, then reach a stable production cadence in 2–3 weeks depending on data readiness and rubric complexity. Day 0–3 is typically used for security alignment, guideline finalization, and task template setup in Abaka Forge. Week 1–2 focuses on calibration and QA baselines. By Week 2–3, we scale production with controlled ramp and weekly deliveries, with change control in place for any schema updates.
What data types and output formats do you support?
We support text, RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion, and audio. Outputs are delivered in pipeline-friendly formats such as JSONL, JSON, CSV, Parquet, and structured manifests, including COCO-style JSON for many vision workflows when appropriate. If you have a custom schema, we’ll match it and provide clear field definitions, naming conventions, and packaging rules so your engineering team can ingest the data with minimal post-processing.
What labeling accuracy can you achieve?
Abaka targets high-precision delivery with multi-layer QA and calibrated reviewers, and we commonly work toward 99% accuracy targets on audited slices when the task is well-specified. Accuracy depends on label ambiguity, taxonomy maturity, and the quality of examples provided. We reduce variance with gold tasks, adjudication, and reviewer calibration, then measure agreement and error patterns continuously. If early pilot results show confusion or edge-case overload, we’ll recommend rubric changes before scaling volume.
How do you protect sensitive data during labeling?
Security is built into the delivery model: strict NDAs, segregated secure pipelines, and compliance controls aligned to SOC 2 and ISO 27001, plus GDPR and CCPA. Access is scoped by project and role, and we maintain audit trails for key workflow actions. We also provide full IP provenance so you can trace how datasets were handled and confirm that your data remains exclusively yours—never repurposed, resold, or shared. This reduces vendor risk for regulated or proprietary workloads.
Do you support multilingual data labeling and localization?
Yes. Abaka operates across 50+ countries and can staff native-language annotators and reviewers for multilingual datasets, including RLHF conversations, classification, extraction, and audio transcription. We help you localize rubrics and examples rather than applying a single English-centric guideline everywhere. For consistency, we use shared core policies plus locale-specific addenda, then measure agreement by language. Deliverables can be separated by locale or unified under one schema, depending on your training pipeline needs.
How are you different from other data labeling companies?
Abaka is designed for frontier AI teams that need auditability and long-running quality, not just one-off labeling. We combine scholar-grade reviewers, a large specialized workforce, and Abaka Forge workflows that support versioned guidelines, QA reporting, and change control. From a trust perspective, we never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. Compliance posture (SOC 2, ISO 27001, GDPR, CCPA) and full IP provenance further reduce risk.
Can we request changes to guidelines after the project starts?
Yes—most production labeling programs evolve. We handle change requests through controlled guideline versioning, impact analysis, and phased rollouts so you can update taxonomies without breaking comparability across dataset versions. For example, we can run a small A/B pilot to validate a new rubric, then apply it prospectively while leaving previous batches untouched, or backfill historical data if needed. You’ll receive clear change logs, sample audits, and updated acceptance criteria so your team can track exactly what changed.
Do you offer a pilot project before a larger commitment?
Yes. We recommend a pilot for nearly every new task, especially for RLHF, reasoning-heavy labeling, or complex multi-label vision work. The pilot validates schema fit, reviewer calibration, throughput assumptions, and QA methodology. You’ll get a small delivery in your required formats plus a QA report highlighting disagreement drivers and edge cases. After that, we propose a production plan that includes weekly release cadence, staffing levels, and a change-control process so scaling doesn’t degrade quality.
Who owns the labeled data and outputs?
You do. Abaka’s position is that your data is exclusively yours—never repurposed, resold, or shared. We also provide full IP provenance to reduce copyright risk, including 0% copyright risk on collected data when Abaka is responsible for custom data capture. If your project includes derived artifacts like rubrics, schemas, or evaluation templates, ownership and reuse terms are documented clearly under the engagement so your team retains control over what you fund and build.
What tooling do you use for annotation and QA?
Projects run on Abaka Forge—our all-in-one platform for collection, cleaning, annotation, and production delivery across text, RLHF, image, video, and 3D/4D point cloud. Forge supports role-based workflows, guideline versioning, audit trails, and QA reporting. We also use large-model automation to speed repetitive checks and routing, while keeping humans responsible for difficult judgment calls. If you need exports that fit an internal toolchain, we deliver standardized manifests and schema-matched outputs.
What is the minimum project size you can support?
We support both small pilots and large-scale production programs. A practical minimum is typically a pilot batch large enough to measure agreement and edge-case coverage—often a few hundred items for text tasks or a smaller but representative set for video/3D where each asset is heavier. If you’re exploring feasibility, we can start with a tightly scoped task, define acceptance criteria, and expand once the rubric is stable. For ongoing programs, we’ll plan weekly volumes to match your training and release cadence.
