How much do data curation experts cost?
Pricing depends on modality, domain difficulty, and QA requirements, but we keep costs transparent and tied to measurable outputs. For human expert work commonly used in curation programs, reference rates include STEM generalists at $12/hr and LLM math/coding specialists at $18/hr; for some vision tasks, image editing can be $8/hr and dense captioning $6/hr. Platform work in Abaka Forge can be credit-based at $0.20 per credit. After scoping, we provide a plan with milestones and a budget range.
How fast can you deliver a curated dataset?
Most teams start with a pilot and first production-ready drop in 2–3 weeks, depending on data readiness and acceptance criteria. Day 0–3 is typically scoping, access setup, and schema alignment; Week 1–2 establishes the curation workflow and guidelines; Week 2–3 scales production and multi-layer QA. After the first release, many teams move into weekly or biweekly refreshes with versioned change logs so training can iterate continuously.
What formats can you curate and export for my pipeline?
We support text, images, video, RLHF artifacts, and 3D/4D point cloud workflows, with exports matched to common ML stacks. Typical outputs include JSONL, CSV/TSV, Parquet, COCO JSON, YOLO labels, PNG masks, and structured manifests for multimodal datasets. We also deliver dataset cards, schema documentation, and change logs so your team can reproduce results and keep downstream training and evaluation stable across releases.
How do you measure curation accuracy and quality?
We define acceptance criteria up front—schema validity, leakage prevention rules, sampling rates, defect taxonomy, and escalation paths. Quality is enforced through multi-layer QA: automated checks in Abaka Forge, second-pass human review, and expert escalation for difficult domains. Where the task allows clear ground truth or rubric-based judgment, we can target up to 99% accuracy. Every delivery includes QA reporting so you can track defect trends and guideline improvements over time.
Can you curate sensitive or confidential data securely?
Yes. Abaka operates under strict NDAs with segregated secure pipelines and least-privilege access controls. We support enterprise expectations around SOC 2, ISO 27001, GDPR, and CCPA practices. For curation programs, we document handling rules, maintain audit-friendly artifacts (guidelines, change logs, provenance notes), and restrict access to approved contributors. This helps security and compliance teams review the process without slowing delivery or forcing your engineers into ad-hoc workarounds.
Do you support multilingual data curation?
Yes—Abaka supports multilingual curation through global coverage across 50+ countries and language-aware workflows. We handle language detection, locale-specific policy filters, and reviewer calibration so guidelines remain consistent across languages. For multilingual corpora, we also help manage balanced sampling and leakage prevention (e.g., translated duplicates across splits). Your team receives standardized schemas and exports that keep multilingual training predictable rather than fragmented.
How are you different from typical data labeling vendors?
Traditional vendors often focus on throughput, while curation requires repeatable process, provenance, and evaluation-aware dataset design. Abaka combines a scalable workforce with Abaka Forge workflows, multi-layer QA, and scholar-grade expertise for hard domains. We also provide compliance-ready operations (SOC 2/ISO 27001 practices, NDAs, segregated pipelines) and a clear trust position: we never build models that compete with you, and your data is never repurposed or resold.
What if we need to change guidelines or schema mid-project?
Change requests are expected in real programs. We version guidelines and schemas, then route updates through a controlled process: impact assessment (what must be reworked), targeted backfills, and release notes so your training team knows what changed. Abaka Forge workflows make it easier to apply rule updates consistently and to re-run QA gates. We’ll recommend when to backfill historical data versus applying changes only to new increments, balancing speed and comparability.
Can we start with a small pilot before scaling?
Yes. A pilot typically includes a limited batch, finalized schema, draft guidelines, and a QA plan with measurable acceptance gates. We use the pilot to surface edge cases early, validate export formats, and confirm that curated outputs improve your training or evaluation results. Once the pilot is accepted, scaling is straightforward: we increase throughput, maintain reviewer calibration, and move into recurring deliveries with change logs and weekly reporting.
Who owns the curated datasets and derived outputs?
You do. Abaka’s model is designed for trust: we never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. We maintain provenance documentation and deliver artifacts (schemas, guidelines, change logs) that support internal ownership and reuse. If you have specific IP clauses or storage requirements, we align the engagement and pipeline to your legal and security posture.
What tools do you use for data curation and QA?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, training workflows, and production. It supports text, image, video, RLHF, and 3D/4D point cloud data, with automation that can make workflows up to 50× faster while keeping humans in control via review and acceptance gates. We configure checks for schema validity, duplication, policy compliance, and sampling-based QA, then export to the formats your training stack expects.
What is the minimum dataset size you can work with?
We can start small—often with a pilot batch sized to validate schemas, guidelines, and QA gates—then scale once the process is stable. Minimum practical size depends on the modality and task, but even a few hundred to a few thousand items can be enough to prove the workflow and reveal edge cases. For larger programs, we recommend building toward steady weekly increments with versioned releases so improvements compound without disrupting training.
