How much does a model training data firm cost?
Pricing depends on modality, complexity, and the level of expertise required, but we keep it concrete and auditable. Common rates include $18/hr for LLM Math/Coding work and $12/hr for STEM generalist annotation. For vision, Image Editing is typically $8/hr and Dense Captioning is $6/hr. For autonomous driving lane work, Road Lane labeling is priced at $3/km. We’ll propose a pilot plan with a clear unit estimate and a not-to-exceed budget—Talk to an Expert to scope yours.
How fast can you deliver the first training-ready dataset?
Most teams can start with a pilot within 2–3 weeks, depending on security onboarding and guideline complexity. We typically use Day 0–3 for scoping, risk checks, and acceptance criteria, then Week 1–2 for pilot production and reviewer calibration. If the pilot meets quality gates, we scale in Week 2–3 with versioned batch releases. For urgent needs, we can prioritize a narrow scope to deliver a first usable batch sooner while keeping QA intact.
What data modalities and output formats do you support?
We support text, RLHF, images, video, 3D/4D point clouds, LiDAR + camera fusion, and audio. Outputs are delivered in practical training formats like JSONL, CSV, Parquet, COCO-style JSON, YOLO TXT, and project-defined schemas for robotics and sensor data. If your training pipeline requires custom fields—metadata, uncertainty flags, reviewer IDs, or version tags—we’ll implement them in Abaka Forge and validate exports before delivery.
What accuracy levels can you achieve for labels and evaluations?
Accuracy depends on task ambiguity and the strictness of your label definitions, but we commonly target up to 99% accuracy using multi-layer QA, calibrated reviewers, and gold sets. For complex domains like math, coding, or medical reasoning, we add domain-specialist review rather than forcing generic guidelines. We also track error categories and implement guideline clarifications quickly, so accuracy improves over time instead of drifting as volume increases.
How do you protect sensitive data and prompts?
We operate with strict NDAs and segregated secure pipelines, and we align to SOC 2 and ISO 27001 practices while supporting GDPR and CCPA requirements. Access can be restricted by role and project, and sensitive batches can be compartmentalized so only approved workers see them. We maintain full IP provenance and ensure your data is exclusively yours—never repurposed, resold, or shared—reducing both operational and strategic risk for your team.
Can you handle multilingual datasets and localization work?
Yes. We support multilingual collection, annotation, and evaluation across 50+ countries, including region-specific language variants and culturally appropriate safety policies. We can localize prompts, verify translations, and run rubric-based evaluations for tone, factuality, and instruction adherence. For multilingual speech projects, we also support transcription and related audio labeling, delivering standard formats like SRT/VTT or JSON sidecars to integrate with your pipeline.
How is Abaka different from other data labeling vendors?
Abaka is designed for frontier AI, not commodity labeling. You get Abaka Forge for end-to-end orchestration, plus workflows that combine large-model automation with expert human verification. We emphasize auditability—versioned datasets, acceptance criteria, and reviewer calibration—so your team can trust comparisons between model checkpoints. Critically, we never build models that compete with you, and your data is exclusively yours—never repurposed or resold—removing a major strategic risk.
What if we need to change guidelines or schemas mid-project?
Change requests are expected, especially when models reveal new edge cases. We manage updates through versioned guidelines and dataset releases, so you can keep historical comparability while improving future batches. We’ll propose whether changes require partial relabeling, a new label set, or a “bridging” dataset that maps old definitions to new ones. Weekly reporting highlights recurring ambiguities so your team can prioritize guideline updates that reduce rework.
Can we start with a pilot before committing long-term?
Yes—most engagements begin with a pilot designed to prove quality, speed, and fit with your training stack. A typical pilot includes: finalized specs, a small but representative dataset slice, calibrated QA, and validated exports. You’ll get clear metrics on throughput, error types, and acceptance rates, plus a scale plan. If the pilot meets your bar, we expand to ongoing delivery with predictable weekly releases.
Who owns the data and the resulting labels?
You do. Your raw inputs, derived labels, guidelines, and outputs remain your IP. Abaka does not repurpose, resell, or share your data—ever. We maintain full IP provenance, which helps you document chain-of-custody and reduce downstream legal risk. If you need contractual language around exclusivity, retention, deletion timelines, and access logging, we can support those requirements as part of onboarding.
What tools do you use to manage and deliver datasets?
We use Abaka Forge, our all-in-one platform for collection, cleaning, annotation, and production delivery across Text, RLHF, Image, Video, and 3D/4D point clouds. It includes workflow automation, reviewer calibration, QA sampling, and export validation. If your team already has internal tooling, we can integrate by delivering outputs in your required schemas and running compatibility checks. We also support ongoing reporting so stakeholders can track quality and cost.
What is the minimum project size you can support?
We support both small pilots and large-scale production, but we recommend starting with a scope that is big enough to surface edge cases—often a few thousand items for text or image tasks, or a smaller number of high-complexity samples for RLHF and expert evaluation. If you’re uncertain, we’ll propose a minimum viable pilot that validates quality gates, export formats, and security workflows—then scale once the pipeline is stable.
