How much does model training data hire cost with Abaka?
Pricing depends on modality, difficulty, and the level of expertise and review you need. For reference, Abaka supports real-world rates such as LLM Math/Coding at $18/hr, STEM Generalist at $12/hr, Dense Captioning at $6/hr, and Road Lane labeling at $3/km. Platform usage in Abaka Forge can be credits-based at $0.20 USD per credit. After scoping, we propose a costed plan tied to throughput targets, QA sampling, and acceptance criteria so you can forecast spend per sprint.
How fast can you start a model training data hire engagement?
Most teams can begin with a scoped pilot in Day 0–3 and see initial outputs in Week 1–2, depending on data access, security requirements, and rubric complexity. The fastest path is to start with a representative sample and define clear acceptance criteria, then scale after calibration. For high-ambiguity tasks (e.g., safety policy grading or complex multimodal labeling), we recommend a short pilot first to lock edge cases before ramping production volume.
What data types and formats can you deliver for training data hire?
Abaka supports text, LLM RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion, and audio. Outputs commonly include JSONL for LLM training, CSV/TSV exports for analytics, COCO-style JSON and YOLO TXT for vision, timecoded video segment exports, and point cloud formats such as PCD and LAS/LAZ with JSON annotations. If you already have a house format, we can map outputs to your schema and include versioning so model comparisons remain consistent.
What accuracy can you guarantee when we hire training data through Abaka?
Accuracy depends on task definition, ambiguity, and verification protocol. Abaka is built to reach high reliability through calibration, gold sets, reviewer audits, and adjudication; for tasks with clear ground truth and validation, we can target up to 99% accuracy. For subjective tasks (e.g., preference ranking or style judgments), we focus on repeatability—stable rubrics, controlled reviewer decisions, and transparent disagreement tracking—so your training signal stays consistent across time.
How do you keep our training data secure during an outsourced hire?
Abaka uses strict NDAs, segregated secure pipelines, and governance aligned to SOC 2 and ISO 27001, with GDPR and CCPA alignment. Access is controlled by role and project, and we maintain audit trails in Abaka Forge so you can trace decisions and reviewer actions. We also maintain clear IP provenance and do not repurpose or resell your data. This reduces the risk of leakage and makes it easier for your security team to approve the workflow.
Can you support multilingual training data hire across regions?
Yes. Abaka operates across 50+ countries and can staff multilingual annotators and reviewers for language-specific tasks like translation QA, intent labeling, safety policy checks, and multilingual transcription. We recommend starting with a calibration batch per language to confirm rubric interpretation and reduce cross-locale variance. Outputs can be delivered in consistent schemas (e.g., JSONL with language tags and metadata) so you can train and evaluate per-language performance cleanly.
How is Abaka different from typical data labeling companies or marketplaces?
Abaka combines managed delivery, domain-specialized talent, and platform execution in Abaka Forge. You get structured QA (gold sets, audits, adjudication), compliance-ready operations, and clear provenance. A key differentiator is incentives: Abaka never builds models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. That alignment matters when your training data is a long-term competitive asset, not a one-off batch.
What if we need to change labeling guidelines mid-project?
Change requests are expected as models evolve. We support rubric versioning, targeted rework, and controlled rollouts so updates don’t break continuity. Typically, we’ll run a short re-calibration batch, update examples and edge-case rules, and then resume production with reviewer monitoring to ensure the change is applied consistently. If you need back-compatibility (e.g., comparing older runs), we can preserve prior versions and deliver deltas so your team can retrain or evaluate precisely.
Can we start with a small pilot before a full training data hire?
Yes—pilots are the fastest way to validate rubrics, formats, and QA metrics before scaling. A pilot typically includes a representative sample, a calibration round, and a short QA report on disagreement patterns and edge cases. You can use the pilot to confirm whether the task should be split (e.g., detection vs. fine-grained attributes) and to estimate throughput. After sign-off, we ramp workforce and delivery cadence while keeping the same acceptance criteria.
Who owns the data and the outputs created during the engagement?
You do. Abaka’s operating principle is that your data is exclusively yours—never repurposed, resold, or shared. We maintain strict NDAs and clear provenance across the pipeline so ownership and traceability are preserved. If you provide source data, it remains yours; if we collect data on your behalf, we deliver it with documented provenance and 0% copyright risk on collected data. We can also align on retention and deletion requirements as part of your governance plan.
What tools will our team use to manage and review the work?
Most projects run in Abaka Forge, which supports text, RLHF, image, video, and 3D/4D point cloud workflows with role-based access and auditability. Your team can review samples, approve adjudications, and export in your preferred formats. If you already have internal tooling, we can integrate via exports and structured handoff points. The goal is to keep your ML pipeline stable while making production, QA, and reporting repeatable.
Is there a minimum project size for model training data hire?
There’s no one-size minimum; the practical minimum depends on whether the task needs calibration, specialist reviewers, or custom workflows. Many teams start with a pilot batch to validate rubrics and formats, then scale once acceptance criteria are clear. If you only need a small amount of highly specialized work (e.g., math/coding evaluation), we can structure the engagement around hourly production. If you need sustained throughput, we’ll design a ramp plan and cadence that fits your sprint schedule.
