How much does an AI data annotation agency cost?
Pricing depends on modality, difficulty, and the level of expert review you need. Abaka supports transparent baseline rates such as $12/hr for STEM generalists and $18/hr for LLM math/coding annotation, with task-based options like $3/km for road lane labeling. For dataset-style purchases, pricing can be per unit (e.g., $0.01/img for stock images). After a quick sample review, we propose a scoped plan with QA gates, throughput assumptions, and a clear cost range so you can budget accurately.
How fast can you start and deliver the first batch?
Most engagements start with Day 0–3 scoping, then a Week 1–2 pilot to validate guidelines, gold sets, and acceptance criteria. After pilot approval, we typically ramp to production throughput by Week 2–3 depending on modality and complexity. If you already have stable guidelines and a clear taxonomy, timelines can compress. We also support ongoing weekly shipments so your training and evaluation cadence stays consistent, even as requirements evolve.
What data types and formats do you support for annotation delivery?
We support text, RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion, and audio. Output formats are designed to be engineer-friendly: JSONL/CSV/TSV/Parquet for text and RLHF; COCO JSON, YOLO TXT, masks, or VOC XML for image; and timestamped JSON/CSV for video. For 3D and sensor fusion, we deliver structured JSON/CSV with linked sensor metadata and versioning. If you have an internal schema, we can map exports to it.
What accuracy levels can you achieve for AI data annotation?
Accuracy depends on task definition and ambiguity, but Abaka is designed to meet high targets—up to 99% accuracy for well-specified tasks—using multi-layer QA. We use gold sets, adjudication, calibrated reviewers, and sampling to catch drift early. For subjective or open-ended tasks (like complex reasoning or safety judgments), we align on rubrics and measure consistency with reviewer calibration rather than claiming a single universal number. You receive QA reports that show where errors occur and how they’re addressed.
How do you keep our data secure during labeling and RLHF?
We operate with strict NDAs, segregated secure pipelines, and role-based access controls designed for sensitive datasets. Abaka supports SOC 2 and ISO 27001 aligned workflows and is GDPR and CCPA ready. We maintain full IP provenance and do not repurpose, resell, or share your data—ever. Access is limited to trained, project-assigned personnel, and workflows are auditable so your security team can review controls and trace how data moved through collection, annotation, QA, and export.
Do you support multilingual annotation across different locales?
Yes. Abaka supports multilingual coverage across 50+ countries, allowing you to handle language, locale, and cultural nuance in both text labeling and RLHF. We can build language-specific guidelines, calibrate reviewers by locale, and run cross-language consistency checks for taxonomy alignment. This is useful for multilingual assistants, translation and sentiment datasets, and international product experiences. You can also choose a staged rollout—starting with a few priority languages—then scaling once acceptance criteria are met.
How are you different from other data labeling companies?
Abaka focuses on trust, specialization, and auditability. We never build models that compete with you, and your data remains exclusively yours—never repurposed, resold, or shared. You also get scholar-network domains (medicine, law, math, coding, languages) for tasks that require real expertise. Operationally, Abaka Forge unifies modalities with dataset versioning, QA workflows, and audit logs, while large-model automation accelerates routine steps. The result is consistent quality at scale without losing control or transparency.
Can we change guidelines or request re-labeling mid-project?
Yes—change is expected, especially for evolving taxonomies and alignment policies. We use structured change control: we document the new rule, update guidelines, run a calibration batch, and quantify the impact on key metrics. If backfills are required, we prioritize them by model impact and release schedule. Exports are versioned, so you can keep training runs tied to a specific dataset release and avoid mixing label policies. This process minimizes disruption while still letting your team iterate quickly.
Do you offer a pilot project before committing to a larger contract?
Yes. A pilot is the fastest way to validate label definitions, QA gates, throughput assumptions, and export formats. Most pilots run in Week 1–2 after scoping, and include calibration, adjudication, and a clear error taxonomy. You’ll see sample outputs, reviewer notes on ambiguous cases, and a proposed scale plan. If the pilot meets acceptance criteria, we ramp volume in Week 2–3 with the same guidelines and QA structure—so scaling doesn’t introduce drift.
Who owns the labeled data and can you reuse it?
You own your data and the outputs produced for your project. Abaka does not repurpose, resell, or share your datasets—your data is exclusively yours. We also maintain full IP provenance, including 0% copyright risk on collected data, so ownership and usage rights are clear. If we create supporting artifacts like guidelines or rubrics, those are scoped and handled contractually, but the dataset deliverables and project-specific labeling work remain under your control.
What tools do you use for annotation and QA workflows?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, training handoff, and production delivery. It supports Image, Video, Text, RLHF, and 3D/4D point cloud in one place with role-based access and audit logs. Large-model automation can accelerate repetitive steps—up to 50× faster—while human reviewers validate edge cases. You get consistent exports, dataset versioning, and structured QA reporting that your engineering team can integrate into existing pipelines.
What is the minimum project size you can support?
We support both small pilots and scaled production programs. If you’re early, we can start with a tightly scoped pilot batch to validate guidelines and acceptance criteria before committing to larger volumes. If you’re already in production, we can ramp capacity across modalities and languages with elastic staffing. Minimum size depends on the complexity of onboarding (security, tooling, and rubric design), but we’ll recommend the smallest meaningful batch that produces statistically useful QA insights and reliable model signal.
