How much does an ai data annotation partner cost?
Pricing depends on modality, complexity, and the level of expertise required. Abaka supports real-world pricing models such as $18/hr for LLM math/coding annotation, $12/hr for STEM generalist work, $6/hr for dense captioning, and $3/km for road lane labeling. We typically start with a short pilot to confirm guidelines and QA gates, then propose a blended rate and delivery plan based on your weekly volume targets. Talk to an Expert for a scoped estimate tied to acceptance criteria.
How fast can you start and deliver the first batch?
Most teams can begin with scoping and secure setup in Day 0–3, then receive initial pilot outputs in Week 1–2. After pilot sign-off and calibration, production ramp commonly stabilizes in Week 2–3, depending on modality and review requirements. If you have an urgent launch, we can prioritize a minimum viable label schema and a smaller gold set to accelerate time-to-first-delivery, then expand coverage as guidelines mature.
What modalities and output formats do you support?
Abaka covers text, RLHF, image, video, audio, 3D/4D point clouds, and LiDAR + camera fusion through Abaka Forge. We deliver practical formats such as JSONL and CSV for LLM/RLHF, COCO-style JSON and masks for vision segmentation, timecoded exports for video, and structured manifests for 3D and multi-sensor sequences. If your pipeline has a custom schema, we can align exports to your contract so ingestion is straightforward and reproducible.
How do you ensure high annotation accuracy and consistency?
Accuracy comes from process, not promises. We define acceptance criteria, run a pilot, calibrate reviewers, and use gold tasks and multi-layer QA to detect drift early. Ambiguous samples are escalated to specialist reviewers, and guideline changes are versioned so decisions remain auditable. For large programs, we add regular calibration sessions and disagreement analysis to keep multiple teams aligned, targeting 99% accuracy programs when the task and ground truth allow it.
Can you meet enterprise security and compliance requirements?
Yes. Abaka operates with SOC 2 and ISO 27001 aligned controls, supports GDPR and CCPA requirements, and works under strict NDAs. We set up segregated secure pipelines and role-based access to keep sensitive datasets contained. You also get full IP provenance—your data stays exclusively yours and is never repurposed, resold, or shared. We do not claim HIPAA support unless your legal team confirms separate contractual requirements.
Do you support multilingual annotation and culturally aware QA?
Abaka supports multilingual programs across 50+ countries, including language-specific normalization, locale-aware rubrics, and reviewer calibration per language. For GenAI and safety work, we can incorporate policy guidance and red-flag escalation tailored to regional sensitivities while keeping your core taxonomy consistent. Deliverables can be separated by locale or unified into a single schema with language tags, depending on how your training and evaluation pipelines are structured.
How are you different from other data labeling vendors?
Abaka is built for frontier AI programs that require trust, specialization, and repeatable QA. We never build models that compete with you, and your data is never repurposed, resold, or shared. Our workforce spans 1M+ specialized annotators plus scholar-network domains for high-judgment tasks like math, coding, and medicine. Abaka Forge adds workflow control, audit trails, and large-model automation so you can scale without sacrificing provenance or consistency.
What if we need to change labeling guidelines mid-project?
Change requests are expected in real programs. We manage updates with versioned guidelines, controlled rollouts, and backfill plans so you can keep dataset continuity. Your team can approve a new rubric, run a small recalibration set, and then apply the change to new batches while clearly marking which items follow which version. If a change impacts prior labels, we’ll quantify the scope and propose a relabel strategy focused on the highest-impact slices first.
Can we run a pilot before committing to a large contract?
Yes. We recommend a pilot to validate label definitions, edge-case handling, QA gates, and export formats before scaling. A typical pilot is sized to expose disagreement patterns and clarify ambiguous instructions, then culminates in a production playbook your team can trust. After the pilot, we propose a ramp plan with staffing and weekly throughput targets, plus a reporting cadence so you can monitor quality and delivery in a predictable way.
Who owns the annotated data and can you reuse it?
You own your data and outputs. 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 you can defend dataset origins and usage. If you require additional contractual protections, we can work under strict NDAs and project-level segregation, with access control designed around your security review requirements.
What tools and workflows will my team use day-to-day?
Your team can manage work in Abaka Forge—our platform for collection, cleaning, annotation, and production operations across modalities. You’ll get project dashboards, role-based access, review queues, QA sampling, audit logs, and export pipelines. We can integrate your guidelines and taxonomy into structured task templates, and align exports to your training stack. If your team already uses internal tooling, we can coordinate on schemas and delivery bundles to reduce friction.
What is the minimum project size you can support?
We support both small, high-judgment pilots and large-scale production programs. The practical minimum depends on whether you need specialist reviewers (e.g., math/coding) and how many modalities are involved. Many teams start with a pilot batch sized to validate guidelines and QA, then expand once acceptance criteria are stable. If you only need a small set, we’ll recommend a scope that still provides statistically useful QA signals and clean, reusable exports.
