How much do data curation solutions cost with Abaka?
Pricing depends on what “curation” includes—cleaning, de-duplication, light annotation, RLHF packaging, or evaluation prep—and the mix of modalities and QA rigor. For work that includes human review or annotation, typical rates map to task types such as STEM Generalist at $12/hr or LLM Math/Coding at $18/hr, with specialized items like Road Lane at $3/km where relevant. Abaka Forge platform usage is available via credits at $0.20 USD each. After a short scoping call, we propose a clear statement of work and unit assumptions.
How long does it take to deliver a curated dataset?
Most engagements start with Day 0–3 scoping and acceptance tests, followed by a 2–3 week cycle for ingestion, cleaning, de-duplication, QA calibration, and versioned delivery. Timing depends on the number of sources, the amount of exception handling, and whether you need additional labeling or RLHF packaging. For ongoing programs, we shift to a steady cadence with weekly releases or refreshes, so your model team always has a predictable stream of training-ready data.
What data types and output formats do you support for data curation solutions?
We support text, LLM RLHF artifacts, images, video, 3D/4D point clouds, LiDAR + camera fusion, and audio. Outputs are tailored to your pipeline and commonly include JSONL, CSV, Parquet, COCO-style JSON, sensor manifests, and signed URL lists for media. We also deliver versioning artifacts—manifests, checksums, and split definitions—so training runs are reproducible and changes are easy to audit across releases.
How do you ensure dataset quality and accuracy during curation?
We use a multi-layer QA approach: schema validation, automated checks for corruption and missing metadata, de-duplication/near-dup clustering, and targeted human review for edge cases. When annotation is involved, we define explicit guidelines and acceptance tests, run calibration rounds, and use spot audits plus adjudication to converge on consistent judgments. Where tasks are well-defined with clear gold standards, Abaka can target 99% accuracy on annotation QA, with transparent reporting and rework loops when thresholds are not met.
How do you handle security and compliance for sensitive training data?
Abaka operates under strict NDAs and uses segregated secure pipelines designed to support SOC 2 and ISO 27001 aligned controls, along with GDPR and CCPA expectations. Access is restricted to approved personnel, and deliveries can be structured to match your internal governance (role-based access, secure transfer, and audit artifacts). We also maintain full IP provenance and aim for 0% copyright risk on collected data, so you can move through security reviews with clearer documentation and fewer surprises.
Do you support multilingual curation and locale-specific rules?
Yes—Abaka supports multilingual data curation across 50+ countries, including locale-specific normalization (dates, currencies, measurement units), language identification, and consistent metadata tagging. For multilingual instruction and evaluation sets, we can curate balanced coverage by language family, region, or user segment and enforce per-language rubrics so quality doesn’t vary by locale. Outputs can be delivered with language tags and standardized fields to keep downstream sampling and training consistent.
How are Abaka’s data curation solutions different from typical data labeling vendors?
Traditional labeling vendors often focus on per-item output without building a repeatable curation system. Abaka combines Abaka Forge automation, schema-first normalization, de-duplication, and versioned dataset delivery—so the dataset is stable across iterations, not just “labeled once.” We also emphasize provenance and security artifacts for enterprise deployment, and we never build models that compete with you—your curated data remains exclusively yours and is never repurposed or resold.
Can we request changes after the first curated dataset delivery?
Yes—change requests are expected, and we manage them through a structured change log. We track schema updates, rubric revisions, and new edge-case rules, then roll them into the next versioned release with release notes so your team understands what changed and why. For larger changes (e.g., new taxonomies, new modalities, or new acceptance thresholds), we propose a re-calibration step to keep QA consistent and to avoid introducing uncontrolled drift across train/val/test splits.
Do you offer a pilot for data curation solutions before a long-term engagement?
We typically start with a pilot that covers a representative subset: a few sources, a defined schema, and a limited set of acceptance tests. The pilot validates throughput, QA gates, and delivery formats, and it produces a first versioned release your team can train on. After the pilot, we expand scope to full volume and establish an operating cadence (weekly or biweekly) with clear metrics and a backlog for improvements.
Who owns the curated dataset and derived artifacts?
You do. Abaka’s position is that your data is exclusively yours—never repurposed, resold, or shared. We deliver curated outputs, manifests, and documentation to your specified storage destination and can align retention and deletion policies to your requirements. We also provide provenance artifacts so ownership and source lineage remain clear across versions, which helps with internal governance and downstream commercial use.
What tools do you use to run data curation workflows?
We run workflows in Abaka Forge—an all-in-one platform for collection, cleaning, annotation, training, and production across data types including text, image, video, 3D/4D point cloud, and RLHF. Abaka Forge supports large-model automation to accelerate repetitive checks and transformations while keeping human review and QA steps auditable. For your team, this means clearer workflow tracking, fewer manual handoffs, and consistent outputs across releases.
What is the minimum dataset size or project scope you can support?
We support both small pilots and large production pipelines. The practical minimum is a scope that allows clear acceptance tests—typically enough samples to represent your edge cases and your target distribution across sources and modalities. If you only have a small initial batch, we can still help by defining schemas, cleaning rules, and versioning so your process scales as volume grows. During scoping, we’ll recommend the smallest pilot that produces training-relevant signal.
