How much does a data curation vendor cost?
Pricing depends on the mix of automation vs. expert review, the modalities involved, and the QA rigor you need. For human-in-the-loop work, common baselines include $12/hr for STEM generalists and $18/hr for LLM math/coding specialists; image editing can be $8/hr and dense captioning $6/hr. For platform usage, Abaka Forge uses credits priced at $0.20 USD each. We’ll propose a scoped plan with measurable deliverables and a weekly burn view so you can control cost while protecting quality.
How fast can you deliver curated, training-ready data?
Most engagements start with a small pilot in Day 0–3 to lock acceptance criteria and validate outputs, then scale into production. Once the pipeline is approved, teams commonly target 2–3 weeks for a first full curated release depending on volume and modality. After that, refreshes can be scheduled on a predictable cadence (weekly or biweekly) because schemas, filters, split rules, and QA gates are already operationalized in Abaka Forge.
What file formats and dataset structures do you support?
We deliver training-ready exports tailored to your stack, including JSONL for LLM/RLHF, CSV/Parquet for analytics and training pipelines, COCO-style JSON for vision annotations, and frame-indexed manifests for video. For 3D, we support common point-cloud formats like PCD and PLY paired with JSON labels and scene catalogs. We also include schema definitions, label maps, and change logs so releases are versioned, auditable, and easy to ingest.
What accuracy or quality level can you guarantee for curated data?
We set quality targets at the start of the project, then operationalize them through sampling plans, gold sets, and multi-layer QA. For curated/annotated outputs, Abaka commonly targets 99% accuracy on audited samples, with defined error taxonomies and escalation paths for edge cases. We also cap throughput to protect quality (up to 500 files/day per annotator) and provide QA summaries per release so you can verify outcomes before training.
How do you handle security and sensitive datasets?
Abaka supports strict NDAs, segregated secure pipelines, and access controls designed for enterprise security reviews. Our operations align with SOC 2 and ISO 27001 practices and support GDPR and CCPA requirements. We also provide full IP provenance and ensure 0% copyright risk on collected data. If your project requires additional controls—like restricted reviewer pools, region constraints, or dedicated environments—we scope that during Day 0–3 and document it in the delivery plan.
Can you curate multilingual datasets and non-English content?
Yes. Abaka supports multilingual curation via distributed reviewer pools across 50+ countries. We can apply language identification, locale-specific filtering, and domain tagging, then curate and QA outputs with calibrated rubrics per language. Deliverables remain consistent across locales—same schema, same split rules, and comparable reporting—so your multilingual training and evaluations are easier to manage. We can also build targeted slices for high-risk languages or regions to strengthen safety and robustness testing.
How are you different from typical data labeling companies or marketplaces?
As a data curation vendor, Abaka focuses on end-to-end dataset readiness—normalization, dedup/leakage control, enrichment, QA gates, and secure delivery—rather than only producing labels. Abaka Forge provides a repeatable pipeline with automation and auditability, while our scholar-network reviewers handle specialized judgment tasks (coding, math, medicine, law, languages). We also never build models that compete with you, and your data is exclusively yours—never repurposed or resold.
What if we need to change the schema or rubric mid-project?
Change requests are expected in real-world curation. We manage updates as versioned spec changes: we assess impact on upstream ingestion, downstream training compatibility, and historical comparability. Then we update validation rules, labeling guidelines, and QA checks in Abaka Forge so the new requirements are enforced consistently. You’ll get a clear migration plan—whether that means forward-only releases, partial backfills, or full reprocessing—and a change log that makes differences explicit.
Can we start with a small pilot before committing?
Yes. We typically start with a pilot batch that is representative but limited in scope, so you can validate schema decisions, filters, split rules, and QA reporting. The pilot includes an ingest-ready export plus QA summaries and edge-case analysis. After review, we scale the same pipeline into production with calibrated reviewer pools and multi-layer QA. This approach reduces risk and gives your team concrete artifacts to evaluate before you expand volume or modalities.
Who owns the curated dataset and derived outputs?
You do. Abaka’s operating principle is that your data is exclusively yours—never repurposed, resold, or shared. We do not build models that compete with you, and we deliver curated outputs with clear provenance and versioning. If you need specific contractual language around IP, retention, or deletion, we align it during onboarding and implement it operationally through segregated pipelines and access controls.
What tooling do you use to manage curation workflows?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, and production workflows across text, image, video, and 3D/4D point cloud. It supports scalable operations with large-model automation (up to 50× faster on suitable tasks), reviewer management, adjudication, audit logs, and export tooling. This ensures your curation process is repeatable and measurable, not a set of one-off scripts that break when requirements change.
What is the minimum dataset size you can work with?
We can start small—often a few hundred to a few thousand samples are enough for a meaningful pilot—so long as the sample represents the real distribution and edge cases you care about. For production, minimums depend on modality and QA design, but our approach scales from compact evaluation sets to large training corpora. If you’re unsure what size is “enough,” we’ll help you define a pilot that validates schema, filters, and acceptance tests before scaling.
