How much does a model training data company cost per hour or per task?
Pricing depends on modality, difficulty, and required expertise, but we use clear, real rate cards and task units. Examples: LLM Math/Coding work is $18/hr, STEM Generalist labeling is $12/hr, Dense Captioning is $6/hr, Image Editing is $8/hr, and Road Lane annotation is $3/km. For model evaluation, Red Teaming can be $8/eval and Defensive Coding $15/eval. We’ll scope your rubric, sample size, and QA depth, then provide a predictable quote.
How fast can you deliver training data after kickoff?
Most teams start with a calibrated pilot, then move into production. Depending on scope, the first production batch commonly lands in 2–3 weeks after kickoff, with earlier pilot samples available sooner for rubric feedback. Timing is driven by task complexity, volume, and review requirements (expert adjudication vs. general labeling). We’ll define acceptance tests on Day 0–3 so speed doesn’t come at the cost of inconsistent labels or unusable exports.
What modalities and output formats do you support for model training data?
We support text, images, video, audio, RLHF datasets, and 3D/4D point cloud—including LiDAR + camera fusion workflows. Common outputs include JSONL/CSV/Parquet for language and RLHF, COCO-style JSON and YOLO TXT for vision, timestamped frame manifests for video, and KITTI-style generic label bundles for 3D pipelines. If your stack needs custom schemas, we can version and document them, and deliver conversion scripts or validation checks when appropriate.
How do you ensure labeling accuracy and consistency over time?
We use a multi-layer QA approach: calibrated rubrics, gold sets, reviewer onboarding, ongoing audits, and adjudication for disagreements. For many tasks we target 99% accuracy where definitions are testable, and we track error categories so your team can see what is improving and what remains ambiguous. We also stabilize rater pools for continuity and maintain change logs when schemas evolve, helping you avoid silent drift between dataset versions.
Can you meet enterprise security requirements for sensitive data?
Yes. Abaka operates with SOC 2 and ISO 27001-aligned practices and supports GDPR and CCPA requirements. We run strict NDAs, segregated secure pipelines, role-based access controls, and controlled delivery mechanisms. We also provide full IP provenance and governance artifacts to support security reviews. If your team needs additional constraints—on-prem, limited geography access, or specialized redaction rules—we can scope an implementation plan during kickoff.
Do you support multilingual training data and non-English evaluation?
Yes. Abaka supports multilingual collection and labeling across 50+ countries, including translation QA, locale-specific instruction following, and culturally grounded evaluation where needed. For LLM workloads, we can create consistent rubrics across languages while still capturing local nuance (tone, politeness norms, domain vocabulary). Deliverables can include language tags, dialect metadata, and reviewer notes so you can debug failures by locale rather than guessing what went wrong.
How are you different from other data labeling vendors or marketplaces?
Three differences matter: trust, specialization, and governance. Abaka is a trustworthy data partner for frontier AI and we never build models that compete with you—your data is exclusively yours and never repurposed. We staff vertically specialized reviewers (coding, math, medicine, law) instead of generalist crowds for hard tasks. And we pair human intelligence with Abaka Forge workflows—so you get auditability, consistent QA, and training-ready exports, not just raw labels.
What if we need changes after the pilot—can you handle change requests?
Yes. We expect schemas and rubrics to evolve as your model learns. After the pilot, we manage change requests through versioned instructions, impact analysis, and targeted relabeling rather than blanket rework. You’ll see what changed, why it changed, and which batches are affected. This makes iteration predictable: you can refine edge cases, add new classes, or adjust evaluation criteria without losing continuity across dataset versions.
Can we start with a paid pilot before committing to a large program?
Absolutely. Most engagements begin with a pilot batch designed to validate rubric clarity, reviewer agreement, and export compatibility. The pilot lets your team test training impact and operational fit while keeping scope controlled. After pilot sign-off, we scale production with multi-layer QA and weekly reporting. If the pilot shows misalignment, we’ll propose rubric revisions, additional examples, or a narrower target slice to improve signal before scaling.
Who owns the datasets and labels produced during the project?
You do. Abaka’s operating principle is that your data is exclusively yours—never repurposed, resold, or shared. We also maintain full IP provenance for collected data to reduce copyright risk and provide traceability for governance. If your organization needs specific contractual language around ownership, retention, deletion, or audit rights, we can align during procurement and security review.
What tooling do you use, and can we integrate it with our pipeline?
We use Abaka Forge for collection, cleaning, annotation, and production delivery across modalities. Integration typically happens at the export boundary: we deliver training-ready formats (JSONL/Parquet/COCO/YOLO and more) along with validation checks and schema documentation. For teams with strict internal tooling, we can also align to your storage, naming conventions, and metadata requirements. The goal is to reduce glue-code and make dataset updates repeatable.
What is the minimum project size to work with your model training data company?
We can support small, high-value pilots and large-scale production programs. Minimum size depends more on complexity than raw volume: expert-heavy tasks (e.g., coding/math RLHF or medical review) can start with a focused batch, while vision pipelines may benefit from larger runs to capture the long tail. We’ll recommend a minimum viable dataset that can show measurable model lift, along with a ramp plan to scale once the rubric and outputs are validated.
