How much does data curation hiring cost with Abaka?
Pricing depends on domain difficulty, QA depth, and whether you need generalist or specialist reviewers. As concrete references: STEM generalists can be staffed at $12/hr, and LLM math/coding specialists at $18/hr. Some work is priced per unit when appropriate (e.g., road lane annotation at $3/km), but we’ll recommend the pricing model that best matches your curation workflow and acceptance gates. Talk to an Expert and we’ll scope a pilot with clear deliverables, QA targets, and a not-to-exceed budget.
How fast can you start and deliver the first curated batch?
Most teams can start within Day 0–3 for scoping, security setup, and rubric alignment, then produce initial calibrated samples in Week 1–2. If your guidelines already exist, we can move faster by converting them into a versioned rubric and launching a small pod for a proof batch. Full production scale typically stabilizes by Week 2–3 once disagreement hotspots are resolved and QA gates are tuned. You’ll receive a weekly delivery cadence and dataset health reporting.
What data types and formats do you support for curation?
Abaka supports curation across text, RLHF datasets, images, video, 3D/4D point clouds, LiDAR + camera fusion, and audio. We curate both the labels and the metadata that make datasets usable—taxonomy normalization, edge-case flags, and reviewer rationale. Deliverables can be exported in common training-ready formats such as JSONL, CSV, Parquet, and COCO-style JSON for vision tasks. If you have a custom schema, we can map outputs to your ingestion requirements.
How do you ensure curation accuracy and consistency across reviewers?
We control consistency through calibration and multi-layer QA. That includes gold sets, adjudication workflows for ambiguous items, and versioned rubrics so you can trace decisions back to a specific policy and reviewer. Throughput caps help prevent speed from eroding quality, and QA reports surface top error modes so we can fix guidelines rather than repeatedly rework data. Depending on the task, targets can be set up to 99% accuracy with appropriate QA depth and domain matching.
Is Abaka secure enough for sensitive data curation projects?
Yes—security is foundational to how Abaka operates. We support strict NDAs, segregated secure pipelines, and compliance controls aligned to SOC 2 and ISO 27001, as well as GDPR and CCPA expectations. Access is scoped by project, and workflows are designed to reduce uncontrolled copying and provenance gaps. For many teams, this reduces internal security review cycles because the operating posture is established upfront rather than assembled ad hoc through contractors.
Can you support multilingual data curation hiring?
Yes. Abaka supports global delivery across 50+ countries and can staff language-specific curators and reviewers for multilingual curation and evaluation. We also support multilingual normalization tasks—consistent tagging, transliteration rules, and locale-specific edge cases—so your datasets are coherent across regions. For LLM programs, we can curate prompts and answers with policy consistency across languages, and provide structured reviewer notes to help you understand disagreement sources and improve rubrics over time.
How is Abaka different from other data labeling or staffing vendors?
Abaka is designed for frontier AI data operations, not commodity labeling. You get domain-matched curation pods, a repeatable QA system, and workflows operated in Abaka Forge—not a loose collection of contractors. We also have a clear trust posture: we never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. That matters when curated data becomes a strategic asset and governance requirements increase.
What if we need to change the rubric or make mid-project requests?
Change is expected—models evolve and edge cases emerge. We handle change requests through versioned guidelines and controlled rollout: we update the rubric, run a calibration slice, and measure the impact on disagreement and QA pass rates before scaling. For urgent changes, we can route items through adjudication while the new policy is validated. You’ll see changes reflected in weekly reporting so you can connect guideline updates to downstream model and evaluation behavior.
Can we run a pilot before committing to a larger engagement?
Yes. A pilot is often the fastest way to validate rubric clarity, QA depth, and delivery cadence. We typically start with a narrowly scoped dataset slice—representative edge cases, a defined taxonomy, and explicit acceptance gates. You receive curated outputs in your required formats plus a QA report that identifies top failure modes and guideline improvements. After the pilot, we can scale the pod size, add specialist reviewers, and lock a weekly delivery plan aligned to your roadmap.
Who owns the curated data and can Abaka reuse it?
You own the curated outputs and the work product created for your project. Abaka does not repurpose, resell, or share your data—ever. This includes raw inputs, intermediate artifacts, and curated datasets. We also maintain full IP provenance and design workflows to minimize copyright and provenance risk, particularly for collected or externally sourced materials. This ownership posture is a core reason teams choose Abaka for strategic datasets.
What tooling do curators use and can we integrate with our pipeline?
Curation work runs in Abaka Forge, our all-in-one platform for collection, cleaning, annotation, and production tracking. It supports multiple modalities and provides guideline versioning, QA workflows, and export controls. We can deliver exports in standard formats (JSONL, CSV, Parquet, COCO-style JSON) or map to your internal schema. If you already have internal tooling, we can align processes so your team can audit decisions and ingest outputs without format friction.
What is the minimum project size for data curation hiring?
There isn’t a single minimum, but the engagement should be large enough to justify calibration and QA setup—typically at least a few thousand items or a defined multi-week workflow. For smaller needs, we can run a compact pilot that focuses on hard edge cases and guideline stabilization, then expand once the rubric is proven. The goal is to avoid spending more time coordinating than curating, while still producing outputs that are training- and evaluation-ready.
