How much does a model training data vendor cost?
Pricing depends on modality, complexity, and QA depth, but we can anchor scope with real rate cards. For example: LLM Math/Coding annotation can be $18/hr, STEM Generalist work $12/hr, Dense Captioning $6/hr, Image Editing $8/hr, and Road Lane labeling $3/km. We’ll recommend the most cost-effective mix of specialist vs. generalist reviewers, plus the right QA sampling plan. Talk to an Expert with your target volume and formats to receive a concrete estimate and timeline.
How fast can you start delivering training data?
Most engagements begin with Day 0–3 scoping and security setup, followed by a Week 1–2 pilot batch to validate guidelines, exports, and QA thresholds. After pilot sign-off, we typically ramp production in Week 2–3 and then ship weekly delivery drops. If you already have a stable rubric and schemas, we can compress timelines by reusing your acceptance tests and starting with a focused calibration set to align annotators quickly.
What modalities and file formats can you deliver?
We support text, LLM RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion workflows, and audio. Common exports include JSONL, CSV, Parquet, COCO-style JSON, segmentation masks, per-frame video annotations, and project-specific schemas with validation. If your pipeline requires a custom structure, we’ll align on a schema contract during scoping and deliver sample exports during the pilot to ensure ingestion works before scale-up.
How do you ensure annotation accuracy and consistency?
Accuracy is driven by clear rubrics, calibrated annotators, and multi-layer QA—not by volume alone. We use guideline versioning, gold sets, spot checks, audits, and disagreement review to detect drift early and correct it before it contaminates the dataset. We also enforce throughput discipline (e.g., 500 files/day per annotator max) to protect quality. For high-stakes tasks, we add expert reviewers from our scholar-network domains to validate tricky edge cases.
What security controls do you offer for sensitive training data?
Abaka operates with SOC 2 and ISO 27001 controls and supports GDPR and CCPA-aligned requirements. We use strict NDAs, segregated secure pipelines, role-based access, and audit-friendly workflows so you can demonstrate governance internally. We can also structure delivery so only necessary fields are exposed to annotators, and we support project separation to prevent cross-contamination between datasets, customers, or model lines.
Can you handle multilingual training data and global coverage?
Yes. Abaka supports multilingual data programs across 50+ countries, with reviewers calibrated to your language-specific rubrics and style requirements. We can run parallel guideline versions per locale when needed (for example, region-specific policy or terminology) and still maintain consistent schemas for ingestion. For multilingual RLHF and instruction tuning, we align evaluation rubrics across languages while explicitly documenting where cultural or linguistic differences require localized criteria.
How are you different from other training data vendors?
Abaka is a trustworthy data partner for frontier AI: we never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. We combine secure pipelines, full IP provenance, and multi-layer QA with Abaka Forge workflows across modalities. Instead of one-off labeling, we run an iterative data program: pilots, calibration, weekly delivery drops, and change-control so your dataset improves alongside your model.
What if our guidelines change mid-project?
Change is expected in real training loops. We manage updates through versioned guidelines, schema change logs, and controlled backfills—so you don’t have to restart production. When requirements shift, we’ll propose the lowest-cost path: incremental re-annotation for affected slices, targeted audits of impacted classes, and calibration refreshes for annotators. Weekly check-ins ensure new failure modes discovered in evals are translated into clearer rubrics and prioritized data fixes.
Can we run a pilot before committing to a larger engagement?
Yes—pilots are the default path for de-risking quality and integration. In Week 1–2, we produce a representative batch, validate export formats, and measure consistency with calibration gold sets. You’ll get QA reporting and a clear read on edge-case handling before scaling. After the pilot, we align on acceptance criteria and a production cadence (often weekly drops) so the larger program starts with proven guidelines and predictable throughput.
Who owns the training data and annotations you produce?
You do. Abaka’s policy is that your data is exclusively yours—never repurposed, resold, or shared. We operate under strict NDAs and maintain full IP provenance for collected data so rights are clear and auditable. If you provide source data, we treat it as your confidential asset and keep it segregated in secure workflows. Deliverables are exported to your storage and systems in the agreed formats and schemas.
What tooling do you use to manage labeling and QA workflows?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, and production delivery across text, RLHF, image, video, and 3D/4D point cloud. It supports structured task routing, QA sampling, audit logs, and export validation against your schema contracts. Where appropriate, large-model automation accelerates repetitive steps up to 50x while keeping human reviewers in the loop, so you get speed without losing control over quality.
What is the minimum project size to work with Abaka?
There’s no one-size minimum; we support both focused expert pilots and large-scale ongoing programs. A practical starting point is a pilot sized to validate your rubric and integration—enough volume to test edge cases and measure consistency, but small enough to iterate quickly. If you’re unsure, share your target model goal, modality, and desired output formats. We’ll recommend a right-sized pilot and a scale plan that matches your timeline and budget.
