How much do AI data annotation solutions cost?
Pricing depends on modality, complexity, and the level of domain expertise required. Common baselines include $12/hr for STEM generalists and $18/hr for LLM math/coding annotation. For vision tasks, examples include $6/hr for dense captioning and $8/hr for image editing. Automotive lane annotation can be priced at $3/km. We typically propose a pilot sprint first to confirm guidelines, QA targets, and throughput, then finalize unit economics and weekly delivery cadence with your team.
How fast can you start and deliver the first batch?
Most teams can begin in Day 0–3 with scoping, sampling, and security alignment, followed by a pilot sprint in Week 1–2. Many programs reach steady production in 2–3 weeks after calibration stabilizes. Timing varies with data access constraints, ontology maturity, and whether you need specialist reviewers (for example, math/coding or medical content). We plan deliveries in weekly batches so you can train and validate continuously instead of waiting for a single end-of-project handoff.
What data types and formats do you support for annotation delivery?
We support text, LLM RLHF, images, video, 3D/4D point clouds, LiDAR + camera fusion, and audio. Typical deliverables include JSONL and CSV for text/RLHF, COCO JSON or YOLO formats for image tasks, timestamped JSON/CSV for video, and structured JSON exports for 3D and sensor fusion. If you have an internal schema, we can map outputs to your field names and validation rules to reduce integration effort and ingestion errors.
What accuracy can you achieve for data annotation?
Programs are designed around clear target metrics, commonly aiming for 99% accuracy supported through multi-layer QA. Actual achieved quality depends on task ambiguity, guideline maturity, and the availability of ground truth for calibration. We improve reliability by running calibration rounds, using gold tasks, adjudicating disagreements, and assigning domain-competent reviewers for complex content like coding, math, legal, or biomedical labeling. You also receive QA reporting so your team can track agreement and error categories over time.
How do you keep my data secure during annotation?
We operate with strict NDAs, segregated secure pipelines, and compliance-ready controls aligned to SOC 2 and ISO 27001, with GDPR and CCPA readiness. Access can be scoped to the minimum required for each task, and workflows can be configured to limit data exposure while maintaining QA. We also provide full IP provenance and ensure your datasets remain exclusively yours—never repurposed, resold, or shared. This helps your legal and security teams approve programs with less friction.
Do you support multilingual annotation and global coverage?
Yes. Abaka supports multilingual and region-specific programs through a global network across 50+ countries. This is useful for chat and assistant training, sentiment and intent classification, OCR validation, and multilingual RLHF where cultural context matters. We define language-specific guidelines, run calibration per locale, and maintain consistent rubrics so labels remain comparable across markets. If you need country-level variants (for example, en-US vs en-GB), we can structure the workflow and reporting to preserve those distinctions.
How are you different from other data labeling companies?
Abaka combines three differentiators: (1) trust—your data is exclusively yours and we never build models that compete with you; (2) capability—support for text, RLHF, vision, video, and 3D in Abaka Forge with multi-layer QA targeting 99% accuracy; and (3) domain depth—scholar-network expertise in math, coding, medicine, law, and more. Many providers optimize for lowest-cost clicks; we optimize for repeatable quality and defensible provenance so your models improve reliably.
What if our labeling guidelines change mid-project?
Change is expected as models improve and edge cases emerge. We manage updates through versioned guidelines, controlled change requests, and targeted backfills when needed. Before applying a new rule globally, we run a small calibration set to quantify how labels will shift and whether historical data should be re-labeled for consistency. This prevents silent distribution shifts that can break evaluations. Weekly reporting makes it clear which batches were labeled under which guideline version, so experiments remain comparable.
Can we run a pilot before committing to a large contract?
Yes—pilots are the default path for complex programs. A pilot sprint typically focuses on a representative sample, clear acceptance criteria, and fast feedback on edge cases. You’ll see guideline quality, annotator agreement, QA reporting, and output formats before scaling. After the pilot, we align on a stable ontology, finalize staffing and throughput, and set a weekly delivery plan. This approach reduces risk and ensures production labeling starts from a calibrated baseline rather than assumptions.
Who owns the labeled data and annotations?
You do. Your data and resulting annotations remain exclusively yours and are not repurposed, resold, or shared. We operate under strict NDAs and maintain IP provenance to support governance and downstream audits. If you provide your own raw data, we treat it as your confidential material. If we help with collection, we ensure 0% copyright risk on collected data and provide provenance documentation so you can use the dataset for training and evaluation with confidence.
What tooling do you use for annotation and QA?
We use Abaka Forge, an all-in-one platform that supports collection, cleaning, annotation, and production workflows across text, RLHF, image, video, and 3D/4D point cloud data. Forge supports embedded guidelines and rubrics, reviewer workflows, audit trails, and automation-assisted steps to accelerate throughput while maintaining QA controls. If your team needs specific export schemas, we can configure structured outputs and validation to match your training pipeline and reduce integration overhead.
What is the minimum dataset size or project scope you accept?
There isn’t a single minimum, but we recommend starting with a pilot that’s large enough to expose edge cases and measure agreement—often a few hundred to a few thousand items depending on modality. For video or 3D, pilots can be smaller in item count but still representative in scene diversity. The goal is to validate guidelines, QA metrics, and output formats before scaling. Once calibrated, we can expand to high-volume production with weekly deliveries and predictable capacity.
