How much do model training data services cost with Abaka?
Pricing depends on modality, complexity, and QA depth, but we can anchor budgets with transparent unit rates. For example, LLM math/coding work can be staffed at $18/hr, STEM generalist labeling at $12/hr, dense captioning at $6/hr, and road-lane annotation at $3/km. For evaluation programs, red teaming can be $8/eval and defensive coding $15/eval. After scoping, we provide a fixed pilot quote and a scalable production rate card so you can plan reliably.
How fast can you deliver training data for a pilot?
Many pilot scopes can be delivered in 2–3 weeks once we align on definitions, acceptance criteria, and export formats. Day 0–3 is typically used for taxonomy, edge-case rules, and gold-set creation. From there, we run production labeling with calibrated QA and deliver train-ready exports in batches. If your use case requires custom collection or multi-modality synchronization, timelines may extend, but we’ll define milestones and first-batch dates up front.
What modalities and output formats do you support for training data?
We support text, LLM RLHF, image, video, audio, 3D/4D point cloud, and LiDAR + camera fusion workflows. Outputs can be delivered in common formats such as JSONL, CSV, Parquet, COCO JSON, YOLO labels, segmentation masks, SRT/VTT, and sensor manifests with calibration metadata sidecars. If you have an internal schema, we can map to it and validate it before delivery so your training pipeline ingests data without manual fixes.
What accuracy levels can you achieve for model training data?
Accuracy depends on task ambiguity and label complexity, but our programs are designed to target high precision with multi-layer QA, rater calibration, and adjudication for edge cases. For many annotation tasks, Abaka can reach 99% accuracy targets when definitions are stable and the task is well-specified. We recommend starting with a pilot to measure agreement, surface ambiguous guidelines, and lock acceptance criteria before scaling volume.
How do you keep our training data secure?
Security is built into our operating model: strict NDAs, segregated secure pipelines, and compliance-aligned controls (SOC 2 and ISO 27001), plus GDPR and CCPA readiness. We implement access controls, role-based reviewer permissions, and auditable workflows so you can limit who sees what and when. We also maintain provenance records and process documentation to help your security and legal teams complete reviews without slowing down delivery.
Can you provide multilingual training datasets?
Yes. Abaka supports multilingual programs across 50+ countries, including text normalization, translation-quality checks, intent labeling, and multilingual RLHF. We can staff language-native annotators and apply consistent taxonomies across regions so labels remain comparable. For LLM use cases, we can also create region-specific safety and policy evaluation sets while keeping your core schema stable, enabling you to train and evaluate across markets without fragmenting your pipeline.
How are Abaka’s model training data services different from typical labeling vendors?
We’re built for frontier AI workflows, not just generic labeling throughput. You get a platform (Abaka Forge) to manage complex pipelines, plus domain-specialist staffing for high-leverage tasks like math, coding, and regulated-domain text. We operate with compliance-first controls, strict NDAs, and full IP provenance, and we never build models that compete with you. Most importantly, we focus on repeatable quality systems—calibration, adjudication, and versioning—so results stay stable across iterations.
What if we need changes after labeling starts?
Change requests are expected—especially during pilots and early production. We handle updates through versioned guidelines, targeted rework plans, and controlled rollout to prevent drift across active workstreams. When a definition change affects already-labeled data, we’ll quantify the impact, propose a costed remediation path, and prioritize the most training-critical slices first. This keeps your team moving while maintaining a defensible audit trail of what changed and why.
Can we start with a small pilot before committing to a larger contract?
Yes. We recommend a pilot that tests your highest-risk assumptions: taxonomy clarity, rater agreement, export compatibility, and model sensitivity to label choices. The pilot typically includes a gold set, calibration, and a first train-ready batch with QA reporting. After the pilot, we review error patterns and adjust guidelines before scaling volume, so your production program is based on measured performance rather than hope.
Who owns the data and labels produced through Abaka?
You do. Your datasets are exclusively yours—never repurposed, resold, or shared. We also never build models that compete with you. Ownership and usage rights are reinforced contractually through strict NDAs and governed operational practices, and we maintain provenance documentation for collected data so you can defend your training corpus in internal and external reviews.
What tools do you use to run training data programs?
We run programs on Abaka Forge—our all-in-one platform for collection, cleaning, annotation, RLHF, and production exports. Forge supports multiple data types (text, image, video, audio, and 3D/4D point cloud) and uses large-model automation to speed up workflows while keeping humans in control for edge cases. You also get audit trails, versioning, and export validation so delivery stays consistent across iterations.
What is the minimum dataset size you can support?
We support everything from small pilots to large-scale production runs. A common minimum is a pilot sized to expose guideline ambiguity and measure agreement—often a few thousand items for text tasks or a representative set of images/videos for vision. If you need even smaller, we can run a calibration-only micro-pilot to validate rubrics and schemas. Once definitions are stable, we can scale throughput across modalities without changing the delivery contract.
