How much does an AI data annotation company cost?
Pricing depends on modality, complexity, and reviewer depth, but we always propose a concrete, auditable rate card for the workstream. Examples include LLM math/coding annotation at $18/hr, STEM generalist labeling at $12/hr, dense captioning at $6/hr, image editing at $8/hr, and road lane annotation at $3/km. For evaluation work, red teaming can be priced at $8/eval and defensive coding at $15/eval. After scoping, we recommend the most cost-effective mix of specialists, QA layers, and automation.
How long does it take to start and deliver the first batch?
Most teams can start with a pilot quickly once the schema, sample data, and security requirements are confirmed. A common pattern is Day 0–3 for scoping and guideline drafting, then Week 1–2 for pilot production with calibration rounds, followed by Week 2–3 to scale and lock the workflow. Timing depends on modality and edge-case density, but we focus on shipping a first validated batch early so your team can test training compatibility before full production ramps.
What modalities and export formats do you support?
Abaka supports text, LLM RLHF, images, video, 3D/4D point clouds, LiDAR + camera fusion, and audio. We export in common, pipeline-friendly formats such as JSONL, CSV/TSV, COCO-style JSON, masks, and structured manifests that link assets to labels and metadata. If your training system expects a custom schema, we can map outputs accordingly and document the transformation so it remains stable across dataset refreshes and taxonomy changes.
How do you ensure annotation accuracy and consistency?
We use calibration rounds, gold sets, and multi-layer QA to reduce rater variance and catch drift early. For high-impact slices, we add targeted audits and reviewer adjudication rather than blanket rework, which keeps costs controlled. Where domain knowledge is required, we staff scholar-network reviewers (e.g., math, coding, medicine, law) to reduce ambiguous interpretations. You also get versioned guidelines and tracked policy decisions so label definitions remain consistent across weeks and across annotator cohorts.
What security and compliance standards do you follow?
Abaka operates with SOC 2 and ISO 27001 controls, supports GDPR and CCPA requirements, and runs strict NDAs with segregated secure pipelines. Access can be scoped by role, and delivery artifacts are designed to be auditable to support procurement and security review. We also maintain full IP provenance, aiming for 0% copyright risk on collected data. If you have additional internal controls, we align workflows and reporting to meet them before production starts.
Do you support multilingual annotation and evaluation?
Yes. Abaka supports multilingual text labeling, translation-quality evaluation, and language-specific RLHF tasks using global coverage across 50+ countries. We can route tasks to language-capable annotators, calibrate rubrics per locale, and run QA checks to ensure consistency across languages. For multilingual audio, we support transcription with timestamps and related labeling like intent and sentiment. If you need domain-specific language expertise (legal, medical, technical), we can staff appropriately and document glossary rules.
How is Abaka different from other data labeling vendors?
Abaka is built for frontier AI programs that need both scale and governance. We combine vertically specialized annotators with scholar-network reviewers, plus Abaka Forge for end-to-end workflow control and automation. We never build models that compete with you—your data is exclusively yours and is never repurposed, resold, or shared. Operationally, we emphasize calibrated guidelines, QA gates, and auditable delivery artifacts, which reduces rework and stabilizes outcomes across continuous training cycles.
What if our label schema changes after we start?
Schema changes are normal—what matters is controlling them. We manage change requests through versioned guidelines, impact assessment, and controlled migrations so your datasets stay comparable across time. When a taxonomy update affects prior work, we help you decide between selective relabeling, mapping layers, or creating a new dataset version. Abaka Forge keeps task history, reviewer decisions, and exports traceable, so your team can reproduce results and avoid silent shifts that show up as unexpected model regressions.
Can we run a small pilot before committing to scale?
Yes. Many teams start with a pilot to validate schema clarity, rater calibration, export compatibility, and QA thresholds. We’ll propose a pilot that includes representative edge cases and a small but meaningful volume, then deliver early samples so your team can test training ingestion. The pilot is also where we tune rubrics, finalize ambiguity policies, and decide the right QA depth. Once validated, we scale the same workflow rather than switching processes midstream.
Who owns the data and the labels you produce?
You do. Abaka’s model is designed so your data is exclusively yours—never repurposed, resold, or shared. We operate under strict NDAs and maintain full IP provenance to support governance and downstream commercialization. Deliverables are produced for your use, in your formats, and with traceable process artifacts. If you require specific contractual language around ownership, retention, or deletion, we align during scoping and follow it in production workflows.
What tools do annotators use, and can we integrate with our pipeline?
Work is delivered through Abaka Forge, our platform for collection, cleaning, annotation, and production operations across text, RLHF, image, video, and 3D/4D. We can export in standard formats and provide structured manifests so your MLOps pipeline can ingest reliably. If you have internal tools, we can align on data interchange formats and validation checks so exports are deterministic. Abaka Forge also supports workflow controls like role-based access, review queues, and audit trails.
Is there a minimum project size to work with your AI data annotation company?
We support both focused pilots and large-scale production programs. A small project can be viable when it’s well scoped—clear guidelines, representative samples, and defined acceptance criteria—because it lets both teams validate quality and workflow fit. For ongoing needs, we can scale capacity elastically as your roadmap changes without rebuilding the process each time. If you’re unsure of the right starting size, we’ll recommend a pilot volume that covers edge cases and produces actionable training or evaluation signal.
