How much does a model training data provider cost with Abaka?
Pricing depends on modality, complexity, and QA depth, but we provide clear, referenceable rate cards so you can budget. Examples include $18/hr for LLM Math/Coding, $12/hr for STEM Generalist work, $6/hr for Dense Captioning, and $3/km for Road Lane labeling. For platform usage, Abaka Forge credits are $0.20 USD each. We’ll recommend the lowest-cost setup that meets your acceptance criteria and delivery timeline—Talk to an Expert to get a scoped estimate.
How fast can you deliver the first batch of training data?
Most teams receive an initial pilot batch within 1–2 weeks after scope and security requirements are finalized, depending on modality and guideline maturity. If you already have stable rubrics and sample “gold” examples, we can move faster; if guidelines need development and calibration, we’ll prioritize correctness before scale. After pilot sign-off, production cadence typically shifts to weekly shipments with measurable QA gates and documented changes.
What modalities and file formats do you support for training data delivery?
Abaka supports text, RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion, and audio—managed through Abaka Forge. Deliverables commonly include JSONL for text/RLHF, COCO JSON or YOLO TXT for vision, MP4 plus sidecar JSON for video, PCD/PLY with JSON annotations for 3D, and SRT/VTT or JSON for audio. If your pipeline requires a custom schema, we can map outputs to your spec and maintain versioning.
What accuracy levels can you achieve for training data annotation?
Accuracy depends on task ambiguity, ontology complexity, and the strength of your guidelines. Abaka runs calibration rounds, inter-rater checks, and multi-layer QA to target high consistency—often aiming for 99% accuracy on well-specified subsets with clear acceptance tests. For open-ended tasks (e.g., creative writing, safety judgments), we focus on rubric clarity, reviewer training, and adjudication workflows to maximize repeatability and reduce label noise.
How do you handle data security and compliance requirements?
Abaka operates with enterprise-grade controls including SOC 2 and ISO 27001-aligned practices, plus GDPR and CCPA readiness. We use strict NDAs, segregated secure pipelines, and role-based access to limit exposure to only the minimum required personnel. We also maintain full IP provenance for collected data, so you can demonstrate sourcing and reduce copyright risk. If you require additional constraints (air-gapped workflows, restricted geographies), we can scope a compliant delivery plan.
Can you produce multilingual training data and evaluations?
Yes. Abaka supports multilingual collection, annotation, and evaluation across 50+ countries, including locale-specific language variants and domain terminology. We can run language-specific guidelines, native-speaker QA, and consistent rubric translations to reduce drift between languages. For multilingual LLM work, we often include calibration sets per language and cross-lingual review checks to ensure your evaluation signal remains comparable across regions and release cycles.
How is Abaka different from other data labeling companies?
Abaka is built for frontier AI workflows that require deep domain expertise, multi-modal coverage, and rigorous traceability. You get one partner across collection, annotation, and model evaluation—plus Abaka Forge for versioning, QA governance, and audit logs. A key differentiator is trust: Abaka never builds models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. This reduces strategic risk for teams building proprietary model advantages.
What if we need changes after delivery (ontology updates or relabeling)?
Change requests are expected in real programs. We handle ontology updates, rubric revisions, and targeted relabeling through a controlled versioning process in Abaka Forge, so stakeholders can track exactly what changed and why. Typically we’ll propose a patch strategy: relabel only the affected slices, keep old versions available for reproducibility, and provide a delta report. This prevents “moving target” datasets and keeps training and evaluation comparisons valid over time.
Can we start with a pilot before committing to a larger contract?
Yes—pilots are the recommended starting point. A pilot lets you validate guideline clarity, edge-case handling, throughput, and QA reporting with minimal risk. We’ll define acceptance criteria upfront (quality thresholds, turnaround time, and sample coverage) and deliver a small but representative batch. After pilot review, we’ll propose a scale plan with weekly cadence, tooling setup in Abaka Forge, and an agreed path for ongoing improvements.
Who owns the data and labels created during the project?
You do. Abaka’s positioning is explicit: your data is exclusively yours—never repurposed, resold, or shared. We operate under strict NDAs and maintain provenance records so you can demonstrate ownership and sourcing. If you provide source data, it remains your property; if Abaka collects data on your behalf, we deliver it with documented provenance and assign it to your program. This keeps your training advantage proprietary and defensible.
What tools do you use to manage annotation and delivery?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, evaluation, and production delivery. It supports Image, 3D/4D Point Cloud, RLHF, Text, and Video, with workflow controls such as guideline versioning, audit logs, role-based access, and QA gates. Abaka Forge also supports large-model automation for faster throughput on suitable steps, while keeping humans in the loop for quality-critical judgments.
What is the minimum project size to work with a model training data provider?
There’s no single minimum, but we recommend starting with a pilot sized to validate your highest-risk uncertainty—usually guideline ambiguity or edge-case coverage—rather than starting too small to measure quality. In practice, that might be a few thousand text items, a few hundred images/videos, or a limited set of 3D sequences, depending on modality. We’ll help you choose a pilot size that produces statistically meaningful QA findings and a clear go/no-go decision for scale.
