How much does an AI data annotation vendor cost?
Pricing depends on modality, complexity, and the expertise required. Abaka offers real, transparent rate options such as $18/hr for LLM math/coding work, $12/hr for STEM generalists, $6/hr for dense captioning, and $3/km for road lane annotation. For model evaluation tasks, pricing can be per-eval (e.g., $8/eval for red teaming or $12/eval for math capabilities). We’ll recommend the most cost-effective mix after reviewing your schema and QA requirements.
How fast can you start and deliver the first batch?
Most teams can go from kickoff to first production outputs in 2–3 weeks, depending on guideline maturity and security onboarding. We typically use Day 0–3 to lock scope and acceptance criteria, then run a pilot in Week 1–2 to calibrate rubrics and QA. If you already have stable guidelines and clear output formats, we can accelerate. The key is to avoid rushing into scale before pilot calibration, which often causes 4–8 weeks of rework later.
What modalities and output formats do you support for annotation delivery?
Abaka supports text, RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion, and audio projects. Outputs are tailored to your pipeline and can include JSONL/CSV/TSV for text and RLHF, COCO-style JSON and PNG masks for vision, track/event JSON for video, and structured 3D bundles for point clouds and fusion. If you have a proprietary schema, we map to it and validate with a pilot export so integration is smooth before scale.
What accuracy can you achieve for data annotation?
Accuracy depends on task ambiguity, class balance, and rubric clarity, but Abaka can target up to 99% accuracy on suitable tasks using calibrated guidelines, gold tasks, audit sampling, and adjudication for edge cases. We also control throughput (up to 500 files/day per annotator) to reduce fatigue-driven errors. For subjective tasks (e.g., preference judgments), we focus on agreement metrics, reviewer calibration, and consistent rubric application rather than a single headline accuracy number.
How do you keep our training data secure?
Abaka operates with SOC 2 and ISO 27001 controls, GDPR and CCPA alignment, strict NDAs, and segregated secure pipelines. Access is role-based and logged, and projects are designed for traceability from raw inputs through QA decisions to final exports. We also maintain full IP provenance and do not repurpose or resell your data—your datasets remain exclusively yours. If your team has specific security requirements, we align them during Day 0–3 scoping.
Do you support multilingual annotation and global coverage?
Yes. Abaka supports multilingual programs with staffing coverage across 50+ countries, which is useful for translation validation, multilingual RLHF, and region-specific domain language. We can standardize rubrics across languages, or create localized variants when nuance is critical. For multilingual launches, we recommend a pilot per language family to calibrate edge cases and reduce disagreements before scaling, especially when labels depend on cultural context or regulated terminology.
How are you different from other data annotation companies?
Many vendors focus on raw throughput; Abaka is built for trustworthy frontier AI data. We pair specialist staffing (including scholar-network domains like math and coding) with multi-layer QA, controlled throughput, and Abaka Forge workflows that keep guidelines versioned and auditable. We also differentiate on trust: Abaka never builds models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. That reduces both performance and strategic risk for your team.
What if we need to change the labeling schema mid-project?
Schema changes are normal—what matters is controlled rollout. Abaka uses versioned guidelines and change-control logs so every label can be traced to the rubric in effect when it was created. We help you decide whether a change requires targeted relabeling, selective backfills, or forward-only adoption. This prevents the common failure mode where teams redo entire datasets after a rubric update, which can add 2–6 weeks and distort experiment comparisons.
Can we run a pilot before committing to a larger engagement?
Yes. We recommend a pilot for nearly every new program, especially RLHF and multimodal work. A pilot batch validates rubric clarity, output formats, QA gates, and turnaround times—before scaling headcount. You’ll receive sample exports, disagreement summaries, and edge-case notes so your team can assess integration effort and data quality. If the pilot meets acceptance criteria, we scale production with predictable weekly deliveries and staffing that matches your training cadence.
Who owns the labeled data and derived datasets?
You do. Abaka’s engagement model is designed so your data is exclusively yours—never repurposed, resold, or shared. We operate under strict NDAs and deliver exports with traceability and full IP provenance for clean lineage. If you need specific contractual language around IP ownership, retention, and deletion, we align it during onboarding so your legal and security teams can sign off before any production work begins.
What tools do you use for annotation and QA?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, and production workflows across text, RLHF, image, video, and 3D/4D point cloud. The platform supports role-based access, audit trails, gold tasks, QA sampling, and workflow automation (up to 50× faster on suitable steps). If your team needs specific export structures or validation checks, we configure them inside Abaka Forge and confirm via pilot exports.
What is the minimum project size to work with your annotation team?
There’s no single minimum that fits every modality; the practical minimum depends on whether you need specialist staffing, pilot calibration, and custom schema work. For most teams, a pilot batch large enough to measure disagreement and QA performance is the right starting point. We can support small, high-complexity jobs (e.g., math/coding RLHF) and larger throughput programs (vision/video/3D). Share your target volume and timeline, and we’ll propose a scoped pilot and scale plan.
