How much do supervised learning data services cost?
Pricing depends on modality, domain complexity, and QA depth, but we can anchor budgets with transparent rate cards. For example, LLM Math/Coding annotation is $18/hr, STEM Generalist work is $12/hr, Dense Captioning is $6/hr, and Road Lane annotation is $3/km. If your workflow uses Abaka Forge credits, credits are $0.20 USD each. Most teams start with a scoped pilot to validate guidelines and estimate cost-per-accepted label before scaling to steady-state delivery.
How fast can you deliver a pilot dataset for supervised learning?
Most teams can run a structured pilot in 2–3 weeks. Week 1 focuses on rubric alignment, gold sets, and calibration; Week 2 expands labeling and QA to validate consistency; Week 3 finalizes exports and acceptance criteria. Timing varies with modality and whether you need specialized reviewers (e.g., technical coding/math, multi-lingual coverage, or 3D sequences). If you already have guidelines and sample data, we can often compress the setup phase and deliver initial batches earlier for quick model iteration.
What data types and export formats do you support for supervised learning?
We support text, image, video, audio, 3D/4D point cloud, and LiDAR + camera fusion workflows through Abaka Forge. Exports include common formats like JSONL/CSV for text and RLHF-style tasks, COCO/Pascal VOC/YOLO for images, and time-indexed JSON packages for video and multi-sensor sequences. If you have a custom schema, we can map outputs to your expected structure so your training code ingests labels without fragile conversion scripts.
What accuracy can you achieve for supervised labels?
For supervised learning data services, we commonly target 99% accuracy on audited samples when the task is well-defined and acceptance criteria are clear. Achieved accuracy depends on label ambiguity, domain complexity, and the strictness of rubric definitions. We use multi-layer QA, gold sets, and adjudication to reduce disagreement, and we report quality metrics and error taxonomies so you can see where mistakes occur. When labels are inherently ambiguous, we’ll recommend uncertainty policies and escalation paths rather than forcing false certainty.
How do you protect sensitive data and comply with security requirements?
Abaka operates with SOC 2 and ISO 27001-aligned practices, supports GDPR and CCPA requirements, and works under strict NDAs with segregated secure pipelines. Access controls, role-based workflows, and audit trails are enforced through Abaka Forge. For highly sensitive programs, we can design a minimal-access workflow, limit data exposure to approved reviewers, and maintain provenance logs for every task. Your data remains exclusively yours—never repurposed, resold, or shared—and we never build models that compete with you.
Can you label multilingual supervised learning datasets?
Yes. Abaka supports multilingual and multi-regional labeling across 50+ countries. We staff projects with language-competent annotators and apply the same calibration and QA methods used in English workflows, including rubric localization and cross-language consistency checks. For tasks like entity extraction, classification, and instruction-following, we ensure that label definitions are semantically consistent across languages—not just translated. You also receive reporting segmented by language and region to detect drift or ambiguity early.
How are you different from other data labeling companies?
Abaka is built for frontier AI teams that need trust, provenance, and repeatable operations—not just cheap labels. We combine vertically specialized annotators (including scholar-network reviewers) with Abaka Forge workflows for audits, adjudication, and structured exports. We also differentiate on trust: we never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. That reduces vendor risk and supports long-term supervised learning programs where data becomes a core asset.
What if our label definitions change mid-project?
Change is normal in supervised learning—what matters is controlling it. We use versioned guidelines, change requests, and controlled rollout plans so updated definitions don’t quietly corrupt dataset consistency. When schemas or rubrics change, we can re-calibrate annotators, run targeted back-tests on held-out samples, and optionally refresh affected slices of data. Weekly reporting helps you see the impact of changes on disagreement and accuracy, so your training and evaluation remain comparable across dataset versions.
Can we start with a small pilot before committing to a full program?
Yes—most teams should. A pilot lets you validate rubric clarity, estimate cost-per-accepted label, and identify edge cases before scaling. In a typical 2–3 week pilot, we label an agreed sample, run multi-layer QA, and deliver an error analysis with recommendations. You can then decide whether to scale volume, adjust label definitions, or expand into new modalities. Pilots also help align stakeholders on what “good data” means before larger budgets are committed.
Who owns the labeled data and can you reuse it?
You own your data and your labeled outputs. Abaka does not repurpose, resell, or share your datasets, and we never build models that compete with you. We also maintain full IP provenance and operate with a 0% copyright risk posture on collected data, ensuring the dataset’s origin and rights are clear. If you provide source data, we treat it under your governance requirements and maintain audit trails so you can demonstrate control and ownership to internal or external stakeholders.
What tooling will our team use during the engagement?
Your project runs in Abaka Forge—our all-in-one platform for data workflows including task management, annotation, QA, adjudication, and export automation. Your stakeholders can review samples, track progress, and audit decisions through role-based access. If you already have internal tools, we can align exports to your expected formats and integrate review steps into your process. Forge supports all major modalities and provides provenance and analytics so you can manage supervised learning quality at scale.
Is there a minimum dataset size or minimum engagement size?
There’s no one-size minimum; we scope based on whether a pilot can produce statistically meaningful quality signals and enough variety to expose edge cases. Many teams start with a few thousand text records or a focused set of images/video clips, then scale once the rubric is validated. For 3D or sensor fusion, pilots may be smaller in count but higher in complexity. We’ll recommend a minimum slice that can validate accuracy, disagreement, and throughput before you commit to long-term volume.
