How much does an AI data annotation specialist service cost?
Pricing depends on modality, complexity, and QA depth, but we use real, scoped unit economics rather than vague packages. Common baselines include LLM Math/Coding annotation at $18/hr, STEM Generalist at $12/hr, Dense Captioning at $6/hr, and Image Editing at $8/hr. For autonomy-specific work, Road Lane annotation is priced at $3/km. We’ll propose a pilot budget after reviewing your samples, rubrics, required accuracy, and export formats—then scale pricing with volume and risk-based QA.
How fast can you start and deliver the first batch?
Most teams can start with a pilot quickly once scope and security requirements are confirmed. A typical flow is Day 0–3 for scoping and samples, then Week 1–2 for calibration and pilot delivery, and Week 2–3 to ramp into steady production. Timing varies based on the number of classes, ambiguity level, and whether you need data collection in addition to labeling. We’ll commit to a concrete schedule after reviewing sample size and acceptance criteria.
What data types and output formats do you support for AI annotation?
We support text, LLM RLHF, images, video, 3D/4D point clouds, LiDAR + camera fusion, and audio. Outputs are tailored to your pipeline and commonly include JSONL, CSV/TSV, COCO JSON, Pascal VOC XML, PNG masks, SRT/VTT, and custom JSON schemas. If you already have an internal schema, we align exports to it and document any transforms. The key is keeping the format stable across batches so training and evaluation stay comparable.
How do you ensure annotation accuracy and consistency over time?
Accuracy comes from specialist-led guidelines and production controls, not just spot checks. We run calibration rounds, maintain versioned decision rules, and use multi-layer QA with adjudication for ambiguous items. We also monitor disagreement trends and report error taxonomies so your team can fix root causes in the rubric. Abaka’s approach targets 99% accuracy where appropriate, while protecting consistency by capping per-annotator throughput (e.g., up to 500 files/day) and focusing QA on high-risk classes.
Is my data secure, and can you support strict NDAs?
Yes. Abaka operates with strict NDAs, segregated secure pipelines, and compliance programs aligned to SOC 2 and ISO 27001, with GDPR and CCPA practices. Access is controlled so only approved staff handle your data, and workflows are designed to minimize unnecessary exposure. We also maintain full IP provenance for collected data, aiming for 0% copyright risk. If your team requires additional controls (air-gapped environments or special review protocols), we’ll scope them during onboarding.
Do you support multilingual annotation and non-English markets?
Yes. Abaka works with a globally distributed workforce across 50+ countries, enabling multilingual text annotation, RLHF evaluation across languages, and localized labeling for vision datasets. We define language-specific guidelines (tone, cultural context, domain terminology) and use calibrated reviewers to keep decisions consistent across regions. This is particularly useful for global customer support models, translation evaluation, multilingual TTS-related datasets, and international retail or finance document workflows.
How is Abaka different from other data labeling vendors?
Two differences matter most: trust and operational rigor. Abaka never builds models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. On delivery, we emphasize specialist-led rubrics, adjudication, and QA analytics rather than generic labeling at scale. Abaka Forge provides production workflows across modalities, with automation where it helps and human judgment where it matters. The result is stable datasets that support reliable training and evaluation decisions.
Can we request changes to guidelines mid-project without breaking consistency?
Yes—change control is built into the workflow. We version guidelines, document what changed and why, and run targeted back-checks or re-labeling only where needed. Weekly reviews help your team approve updates before they propagate, preventing silent label drift across batches. If a change impacts model comparability, we’ll recommend a controlled migration plan: dual-labeled samples, conversion rules, and clear dataset versioning so training and eval remain interpretable over time.
Can we run a paid pilot before committing to a long-term contract?
Yes. Most teams start with a pilot designed to validate schema, rubrics, QA policy, and export formats. The pilot typically includes calibration rounds, a defined batch size, and a quality report showing disagreement drivers and edge-case outcomes. After the pilot, you can scale into production with the same guidelines and reviewer calibration, reducing ramp risk. We’ll scope the pilot to your timeframe and success metrics so it’s a true signal—not a one-off sample.
Who owns the labeled data and derived datasets?
You do. Abaka’s policy is that your data is exclusively yours—never repurposed, resold, or shared. We deliver the labeled outputs, guideline versions, and QA reports as project artifacts, and we can align on retention, deletion, and access terms based on your security requirements. If you provide prompts, model outputs, or proprietary taxonomies, those remain your IP as well. This clarity helps teams move faster with legal and procurement stakeholders.
What tooling do you use, and can you integrate with our stack?
We use Abaka Forge—an all-in-one platform for collection, cleaning, annotation, and production workflows across text, RLHF, images, video, and 3D/4D. We can export to your required formats (JSONL, CSV, COCO, XML, masks, etc.) and support repeatable delivery schedules. If your team has internal validation scripts or dataset registries, we align exports and metadata fields to reduce manual post-processing and keep your training pipeline deterministic.
What is the minimum project size for an AI annotation specialist engagement?
There’s no single minimum, but the best starting point is a pilot large enough to expose edge cases and measure consistency. Small pilots work well when schema and rubrics are still forming; larger pilots make sense when you already have a stable taxonomy and need throughput validation. We’ll recommend a minimum batch size based on the number of classes, expected ambiguity, and the QA confidence you need before production. The goal is to learn quickly without over-investing up front.
