How much does an AI data annotation solution cost?
Pricing depends on modality, complexity, and QA depth, but we can anchor cost using proven reference rates. For example, LLM math/coding annotation can start at $18/hr, STEM generalist work at $12/hr, dense captioning at $6/hr, image editing at $8/hr, and road lane annotation at $3/km. We’ll propose a scoped plan with throughput, acceptance criteria, and a clear estimate for pilot and production. Talk to an Expert to size the right mix for your roadmap.
How fast can you start and deliver the first batch?
Most teams can start quickly once scope, access, and acceptance criteria are set. A typical path is Day 0–3 for scoping and security setup, Week 1–2 for guidelines + calibration with a pilot batch, and Week 2–3 for a production ramp. The exact timeline depends on modality (e.g., video/3D takes longer than text), rubric maturity, and the amount of edge-case policy needed. We’ll give you a schedule that matches your training cadence.
What data types and output formats do you support?
We support text, LLM RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion, and audio. Output formats are delivered to fit your pipeline—commonly JSON/JSONL, CSV/TSV, COCO JSON, YOLO TXT, VOC XML, segmentation masks, and structured bundles for sequences. If you already have an internal schema, we can map to it and deliver consistent manifests and batch metadata so merges and re-training runs stay reproducible.
What accuracy levels can you achieve for annotation quality?
Accuracy depends on task clarity, taxonomy complexity, and the QA process, but Abaka programs commonly target up to 99% accuracy on approved tasks using multi-layer QA. We achieve this through calibrated guidelines, gold sets, reviewer escalation, and drift monitoring across batches. For ambiguous tasks, we recommend explicit acceptance thresholds per class and a structured ambiguity policy, so “hard” samples don’t pollute training signal and your evaluation metrics remain meaningful.
How do you keep our data secure during annotation?
Abaka uses strict NDAs, segregated secure pipelines, and controls aligned with SOC 2 and ISO 27001, with GDPR and CCPA readiness. Access is limited to authorized contributors, and workflows are designed to minimize data movement and preserve auditability. We also provide full IP provenance and do not reuse, resell, or repurpose your data. If your team requires additional controls (e.g., isolated environments or custom access policies), we can align during scoping.
Do you support multilingual annotation and localization?
Yes. Abaka supports programs across 50+ countries, enabling multilingual labeling, localized intent interpretation, and region-specific edge-case coverage. For multilingual NLP and RLHF, we route tasks to native or near-native speakers and apply language-specific rubrics to prevent “translation artifacts” from distorting labels. We can also deliver language-stratified sampling and QA reporting so you can see quality and coverage by locale and improve model performance where it matters most.
How is Abaka different from other data labeling vendors?
Abaka is positioned as a trustworthy data partner for frontier AI: we never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. Operationally, you get Abaka Forge for centralized workflows across modalities, vertically specialized annotators (including scholar networks), and production controls like spec versioning and multi-layer QA. This reduces drift and rework, and it keeps datasets audit-ready for repeatable training and evaluation cycles.
What if we change the taxonomy or labeling rules mid-project?
Change requests are normal—what matters is controlling them. We manage changes through spec versioning, controlled rollouts, and batch-level metadata so your team can maintain comparability across training runs. For breaking changes, we’ll recommend strategies such as dual-labeling, partial backfills, or maintaining “v1/v2” splits until the model catches up. The goal is to evolve your dataset without resetting quality or creating silent inconsistencies that undermine evaluation.
Can we run a pilot before committing to a long-term program?
Yes. A pilot is the fastest way to validate rubrics, throughput, and formats with your real data. We typically start with a scoped batch that covers representative edge cases, then iterate on guidelines and QA gates until acceptance criteria are consistently met. The pilot output is delivered in production-style packaging so you can test ingestion and training end-to-end. After sign-off, we ramp capacity while keeping the same definitions and reviewer structure.
Who owns the labeled data and can it be reused?
You own your data and outputs. Abaka’s policy is that your data is exclusively yours—never repurposed, resold, or shared. We also provide full IP provenance and operate secure, segregated pipelines under strict NDAs, which helps ensure your datasets remain defensible and auditable. If you require additional contractual language around ownership, retention, or deletion, we can align during procurement and onboarding.
What tools do you use to manage annotation workflows?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, and production delivery across text, RLHF, image, video, and 3D/4D point cloud. It supports operational controls like task routing, QA gates, and delivery packaging. Abaka Forge can also accelerate workflows via large-model automation (up to 50× faster on appropriate tasks), while keeping human review in the loop for correctness and auditability.
What is the minimum project size for an AI data annotation solution?
There’s no single minimum, but the most efficient starting point is a pilot sized to validate your rubric and output requirements—often a few hundred to a few thousand items for text/image tasks, or a smaller number of higher-effort sequences for video/3D. If your needs are highly specialized (e.g., domain reasoning or multi-sensor fusion), we’ll recommend a pilot that includes enough edge cases to stress-test definitions and QA. From there, scaling is straightforward.
