How much does a data curation specialist service cost?
Pricing depends on modality, domain depth, and whether you need expert review, RLHF-style grading, or intensive cleaning. For labor-based work, common reference rates include STEM Generalist reviewers at $12/hr and LLM Math/Coding specialists at $18/hr. For specific vision/AV tasks that bundle curation with labeling, road lane work can be priced at $3/km, and dense captioning at $6/hr. We’ll scope your dataset, QA targets, and weekly throughput, then propose a clear plan with deliverables and acceptance criteria.
How fast can you start and deliver the first curated dataset batch?
Most teams can start within Day 0–3 for scoping, schema, and security setup, then receive a first curated batch within Week 1–2. Timing depends on raw data readiness, number of sources, and how strict the filtering and split hygiene rules need to be. If you already have guidelines and a target format, we move faster; if you need taxonomy design and golden sets, we’ll spend more time on calibration. We can also run a pilot first to validate quality and throughput before scaling.
What modalities and output formats do you support for curated datasets?
We support text, LLM RLHF workflows, images, video, 3D/4D point cloud, LiDAR + camera fusion packages, and audio. Outputs are delivered in practical formats like JSONL, CSV, Parquet, manifests, and modality-specific structures (e.g., COCO-style JSON for vision, timestamped JSON for video, and PLY/PCD bundles with metadata for 3D). Abaka Forge keeps the pipeline auditable and versioned, so your team can reproduce a release and understand what changed between dataset versions.
What accuracy or quality levels can you achieve in data curation?
Quality is enforced through measurable acceptance criteria rather than vague “cleanup.” Abaka programs commonly target up to 99% accuracy with multi-layer QA, using calibration packs, golden sets, and adjudication for disagreements. The right target depends on task risk: safety-sensitive evaluation sets typically require stricter review and higher sampling rates than broad pretraining corpora. We also track error taxonomies (what failed and why) so guideline updates are driven by data, not opinion, and quality improves over time.
How do you keep our data secure during curation?
We operate with strict NDAs, segregated secure pipelines, and compliance practices aligned with SOC 2 and ISO 27001. Access is role-based, reviewer activity is logged, and dataset versions are traceable from raw input to curated output. We also support GDPR and CCPA-aligned processes when applicable. Importantly, we maintain full IP provenance and ensure your data remains exclusively yours—never repurposed, resold, or shared—so security and ownership are protected throughout the project.
Can you curate multilingual datasets and non-English corpora?
Yes. Abaka supports multilingual curation with reviewer coverage across 50+ countries. We can normalize language-specific metadata, enforce consistent taxonomies across locales, and apply language-aware filtering rules for safety and scope. For LLM datasets, we can curate instruction sets and evaluation prompts in multiple languages while maintaining split hygiene and provenance. If you need domain expertise (medical, legal, business) in specific languages, we’ll match reviewers accordingly and calibrate rubrics to reduce cross-locale inconsistency.
How is Abaka different from other data labeling and curation vendors?
First, Abaka is explicitly aligned with your outcomes: we never build models that compete with you, and your data is exclusively yours—never repurposed or resold. Second, we focus on governed curation (dedup, filtering, taxonomies, golden sets, split hygiene) rather than just raw throughput. Third, Abaka Forge provides an auditable workflow across modalities, enabling repeatable releases and clear change logs. Finally, our reviewer network includes specialized domains like coding, mathematics, medicine, and law for high-stakes curation decisions.
What if we need change requests after the curation guidelines are set?
Change requests are expected, especially as model behavior reveals new failure modes. We handle changes through a tracked workflow: updated label definitions, new exclusion rules, revised taxonomies, or new evaluation subsets. We’ll assess impact on previously delivered batches and propose either a forward-only change (applies to new data) or a backfill plan (reprocess historical data). Every release remains versioned with a changelog so your team can attribute performance shifts to dataset changes rather than guessing.
Can we run a small pilot before committing to a full program?
Yes. A pilot is often the fastest way to validate quality, reviewer calibration, and delivery format. We typically pilot a representative slice of your data, including edge cases, then deliver a versioned curated batch with QA summaries and recommendations. The pilot helps confirm acceptance criteria, split hygiene approach, and ongoing cadence. After the pilot, you can scale with confidence—keeping the same schema, policies, and workflows—without restarting from scratch.
Who owns the curated dataset and the derived artifacts?
You do. Abaka’s operating principle is clear: your data is exclusively yours and is never repurposed, resold, or shared. We maintain provenance and audit trails so ownership and transformation steps are traceable. Deliverables such as curated datasets, taxonomies, guidelines, and QA reports are produced for your project and delivered back to you in versioned releases. If you require additional contractual language around IP and access controls, we can align during the initial security and legal review.
What tools do you use for data curation and QA workflows?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, training support, and production workflows. For curation, Forge provides tracked queues, reviewer assignment, rubric templates, adjudication, audit logs, and versioned exports across modalities (text, RLHF, image, video, and 3D/4D). This reduces operational overhead and prevents “spreadsheet governance.” If your team has existing storage, labeling tools, or MLOps pipelines, we can integrate via structured exports and agreed delivery conventions.
What is the minimum dataset size you can help curate?
There is no strict minimum—small datasets can be the most valuable when they’re high-leverage evaluation sets or safety challenge sets. We can curate from a few hundred to millions of items, depending on modality and review depth. For small starts, we recommend focusing on a representative slice with edge cases to validate taxonomy and guidelines, then scaling. Because quality depends on calibration, we’ll include a golden-set and QA plan even for smaller engagements to keep results consistent and measurable.
