How much does a supervised learning data vendor cost?
Pricing depends on modality, complexity, and the level of expert review required, but Abaka uses clear, real-rate building blocks so you can forecast cost. Examples include $12/hr for a STEM generalist, $18/hr for LLM math/coding specialists, $6/hr for dense captioning, and $3/km for road lane annotation. For some workflows in Abaka Forge, credits are priced at $0.20 USD each. After we review your samples and acceptance criteria, we provide a scoped plan with throughput and QA assumptions.
How fast can you deliver supervised training data?
Most teams can move from scoping to first production batches in 2–3 weeks, depending on guideline maturity, data readiness, and modality. We typically start with a pilot to calibrate decisions and confirm exports, then ramp into production with batch deliveries so your team can begin training early. If you already have stable instructions and an established schema, timelines compress; if your ontology is evolving, we add change-management steps to prevent drift and reduce rework.
What modalities and file formats do you support for supervised learning datasets?
Abaka supports text, image, video, 3D/4D point clouds, LiDAR + camera fusion, audio, and RLHF-style preference datasets. Common outputs include JSONL, CSV, COCO JSON, YOLO TXT, masks, timecoded exports, and custom JSON schemas that match your trainer ingestion. We align on formats during Day 0–3 scoping, validate them during the pilot, and then keep them consistent across weekly deliveries so your pipeline doesn’t break with every batch.
What labeling accuracy can you achieve for supervised learning?
Abaka targets up to 99% accuracy using calibrated guidelines, multi-layer review, and structured QC (including sampling-based audits and escalation for ambiguous cases). The achievable accuracy depends on task ambiguity, label taxonomy clarity, and data quality. We reduce disagreement by running calibration rounds, building an error taxonomy, and updating guidelines with version control. For specialized domains, we can staff scholar-network reviewers so complex decisions are made consistently across the program.
How do you handle data security and compliance for labeling projects?
Abaka operates with SOC 2 and ISO 27001 controls and aligns to GDPR and CCPA requirements. We use strict NDAs, role-based access, and segregated secure pipelines to limit exposure of your data and labeling guidelines. We also support audit-friendly documentation so procurement and security teams can evaluate the engagement without guesswork. Importantly, we maintain full IP provenance and do not introduce copyright risk on collected data.
Can you label multilingual datasets for supervised learning?
Yes. Abaka supports multilingual labeling across 50+ countries, covering tasks like classification, NER, transcription, and instruction tuning data preparation. We align on language-specific guidelines (tokenization, punctuation, numeral formats, code-switching rules) and apply calibration to ensure consistent decisions across annotators. For higher-stakes domains, we can route work to language specialists and add reviewer arbitration to handle ambiguity. Exports are delivered in consistent JSONL/CSV schemas so your multilingual training pipeline stays stable.
How is Abaka different from other data labeling vendors?
Abaka is built for frontier AI workflows where repeatability, provenance, and security matter as much as throughput. We combine a large specialized workforce with Abaka Forge workflows, multi-layer QA, and audit-ready controls (SOC 2, ISO 27001, GDPR, CCPA alignment). We also differentiate on trust: Abaka never builds models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. This reduces both competitive and compliance risk over long engagements.
What happens if our labeling guidelines change mid-project?
Change requests are normal in supervised learning. Abaka handles them with versioned guidelines, controlled rollouts, and impact assessment so you don’t accidentally create label drift. We can isolate changes to specific classes, re-audit affected batches, and plan targeted relabeling when needed. Weekly reporting highlights confusion pairs and error patterns so guideline updates are driven by evidence, not guesswork. The goal is to evolve your ontology while keeping datasets comparable across time.
Can we start with a pilot before committing to a large dataset?
Yes—pilots are often the fastest path to production success. We typically run a pilot in Week 1–2 to validate instructions, measure inter-annotator agreement, and confirm your export schema. The pilot also surfaces ambiguous edge cases early so we can tighten guidelines before scaling. After the pilot, we propose a production plan with QA gates, batch delivery cadence, and staffing assumptions so your team can commit with confidence.
Who owns the labeled data and the annotation guidelines?
You do. Abaka’s trust model is designed around exclusive ownership: your data is never repurposed, resold, or shared. We operate under strict NDAs and segregated secure pipelines, and we can align contractual terms to clarify ownership of outputs and project-specific guidelines. If you provide proprietary taxonomies or internal documents, we treat them as confidential and restrict access to authorized personnel only. This ensures the labeled dataset remains a durable competitive asset for your team.
What tooling do you use to manage supervised labeling and QA?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, review, and delivery across text, image, video, 3D/4D point cloud, and RLHF workflows. Forge supports structured task setup, role-based access, review queues, and export automation. For teams that already have internal tools, we can still deliver in your required schemas and integrate via agreed handoffs; Forge primarily ensures process control, visibility, and repeatability across the project lifecycle.
What is the minimum project size to work with a supervised learning data vendor?
Minimum size depends on modality and the amount of setup required, but Abaka can support small pilots and scale to large, multi-team programs. A common starting point is a pilot batch sized to validate your ontology and QA assumptions—enough examples to cover edge cases and measure agreement. From there, we ramp into production in staged deliveries so you can start training early. If you’re unsure what minimum is appropriate, we can recommend a pilot size after reviewing samples and task complexity.
