How much does a data curation provider cost?
Pricing depends on modality, complexity, and whether you need pure curation (cleaning, dedupe, schemas) or curation plus labeling/evaluation. For example, specialist LLM math/coding work is typically priced at $18/hr, while STEM generalist work can be $12/hr. If your workflow is dataset-driven rather than hour-driven, some tasks are priced per unit—e.g., road lane work at $3/km or red teaming at $8/eval. We’ll propose a scoped plan with measurable acceptance criteria and a clear cost model for each workstream.
How long does it take to deliver curated training data?
Most teams see first delivery in 2–3 weeks, assuming access and scope are clear. Day 0–3 focuses on intake, schema, and acceptance criteria. Week 1–2 is a pilot plus calibration to lock guidelines and validate QA thresholds. Week 2–3 scales production and delivers a versioned release with change logs and QA summaries. After that, we typically run weekly iterations so curation tracks model failures and changing data distributions without disrupting reproducibility.
What data types and formats can you curate and deliver?
Abaka supports text, images, video, audio, and 3D/4D point cloud—including synchronized LiDAR + camera workflows. We deliver in practical ML-ready formats such as JSON/JSONL, CSV/TSV, Parquet, COCO JSON, frame manifests, and timestamped sequence metadata. We also provide dataset documentation: schemas, label maps, QA summaries, and checksums. If your pipeline needs a specific structure, we can adapt exports while preserving versioning and traceability.
What accuracy can you guarantee for curated or labeled data?
Accuracy targets depend on task clarity, label ambiguity, and the availability of gold standards. Where the task allows, Abaka supports up to 99% accuracy using multi-layer QA, reviewer calibration, adjudication, and acceptance tests. For inherently subjective tasks, we focus on agreement metrics, rubric quality, and consistent decision rules rather than unrealistic guarantees. We’ll define measurable success criteria up front and report QA results in each delivery so you can trust the dataset you train on.
How do you keep our data secure during curation?
We operate with SOC 2 and ISO 27001 controls and support GDPR and CCPA-aligned processes. Projects run under strict NDAs with segregated secure pipelines, controlled access, and audit-friendly workflows. We can accommodate restricted environments and least-privilege role setups depending on your needs. Just as important, we maintain full IP provenance and clear handling documentation, so your internal security and legal teams can review the process end to end.
Do you support multilingual data curation?
Yes. Abaka’s workforce spans 50+ countries, and we curate multilingual corpora for training, translation, evaluation, and safety review. We help you normalize language metadata, filter low-quality segments, and design consistent taxonomies across locales. For multilingual RLHF or evaluations, we use calibrated rubrics and reviewer alignment to reduce locale-specific drift. Deliverables include language-tagged manifests and consistent schemas so your team can analyze performance by language and region.
How is Abaka different from other data curation companies?
Abaka focuses on trust, reproducibility, and ownership for frontier AI teams. We combine specialist human reviewers with Abaka Forge automation and deliver versioned datasets with QA evidence—not just “cleaned files.” Operationally, we are SOC 2 and ISO 27001 aligned, maintain full IP provenance, and run segregated secure pipelines. Strategically, we never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared.
Can we request changes after you deliver the curated dataset?
Yes—change requests are expected, and we structure work to make them low-friction. Each release is versioned with documented deltas, so updates don’t break reproducibility. We typically handle changes through weekly cycles: you report failure cases or new requirements, we update guidelines and acceptance tests, then deliver a new version with QA summaries. For major schema shifts, we’ll propose a migration plan to preserve comparability between old and new datasets.
Can we start with a pilot before committing to a large engagement?
A pilot is the recommended starting point. In 1–2 weeks we curate a representative subset, validate schemas and guidelines, and measure QA outcomes. This lets you verify that the outputs match your pipeline and that the acceptance criteria are realistic. After the pilot, we scale the same process—same specs, same QA controls—so you don’t lose time reinventing workflows when volume increases.
Who owns the curated data and derived artifacts?
You do. Abaka’s policy is that your data is exclusively yours and is never repurposed, resold, or shared. We do not build models that compete with you, and we maintain clear provenance for curated outputs and artifacts. Deliverables—schemas, label maps, curated datasets, QA reports—are provided as project outputs for your internal use. If you have specific IP clauses your legal team requires, we can align within NDAs and statements of work.
What tools and platforms do you use for data curation?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, training workflow support, and production delivery. It supports text, image, video, RLHF, and 3D/4D point cloud, with automation that can accelerate pipelines up to 50× while preserving review controls. For integrations, we can work with your storage and MLOps stack via structured exports and agreed handoff procedures. We also provide audit logs and versioning to support governance.
What is the minimum dataset size for a data curation engagement?
There’s no single minimum, but engagements work best when there’s enough volume to justify stable specs and QA—often a few thousand items for text or a few hundred assets for complex multimodal tasks. For smaller needs, we can still run a scoped audit: define taxonomy, clean a targeted subset, and produce an evaluation-ready slice. We’ll recommend a minimum pilot size based on modality, ambiguity, and the acceptance criteria you need to trust results.
