How much do model training data solutions cost with Abaka?
Pricing depends on modality, complexity, and QA depth, but we provide concrete unit rates so you can estimate quickly. Examples: LLM Math/Coding annotation is $18/hr, STEM Generalist is $12/hr, Dense Captioning is $6/hr, Image Editing is $8/hr, and Road Lane annotation is $3/km. For platform usage, Abaka Forge runs on credits at $0.20 USD each. After a short scoping call, we propose a priced pilot with clear deliverables and acceptance criteria so you can validate quality before scaling.
How fast can you deliver a first batch for model training data solutions?
Most teams start with a pilot that proves guidelines, QA, and export formats, then scale into production. Typical timelines are Day 0–3 for scoping and workflow setup, then Week 1–2 for a calibrated pilot, and Week 2–3 to scale the first production delivery. Timing varies by modality (e.g., 3D and video can require more review) and by how mature your taxonomy is. We keep schedules predictable by enforcing throughput guardrails and using versioned rubrics so quality stays stable as volume increases.
What data modalities and output formats do you support?
Abaka supports text, RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion, and audio. We deliver training-ready exports aligned to common pipelines, including JSONL, CSV, Parquet, COCO-style JSON, segmentation masks, and format-compatible 3D label outputs. Beyond files, we include manifests, metadata (timestamps, IDs, and tags), and version notes so your team can reproduce training runs. If you have a custom schema, we can map outputs to your spec and validate with a pilot before scaling.
What accuracy can you achieve for training data labels?
Accuracy depends on task ambiguity, label granularity, and source quality, but Abaka programs can target and achieve up to 99% accuracy where applicable through multi-layer QA. We use calibrated gold sets, reviewer adjudication, and slice-based auditing to measure quality where it matters most (long-tail classes, safety prompts, rare objects). For inherently subjective tasks, we define rubrics and acceptance tests that focus on consistency and inter-annotator agreement, then iterate guidelines until your model results stabilize rather than fluctuate between batches.
How do you secure sensitive training data and prevent leakage?
Abaka operates with strict NDAs, segregated secure pipelines, and compliance aligned to SOC 2 and ISO 27001, with GDPR and CCPA considerations for applicable datasets. Access is controlled by role, and workflows are designed to minimize data exposure while preserving QA transparency. We also provide full IP provenance for collected data (0% copyright risk on collected data) so governance extends beyond security into licensing and ownership. If your organization has additional controls, we can align the workflow and documentation during scoping.
Can you support multilingual training data at scale?
Yes. Abaka works across 50+ countries, enabling multilingual collection, translation QA, and region-specific labeling that reflects local context and terminology. For language tasks, we can staff native speakers and domain reviewers, then apply consistent rubrics across locales to avoid “same label, different meaning” drift. Deliverables can include language metadata, locale tags, and standardized schemas so your training pipeline stays uniform. We typically start with a multilingual pilot to validate guidelines, then expand to additional languages once acceptance criteria are met.
How are you different from typical data labeling companies?
Abaka is built for frontier AI workflows, not commodity labeling. You get domain-specialized annotators and scholar-network reviewers, RLHF and evaluation capabilities, and an end-to-end platform (Abaka Forge) that supports multimodal work with versioning and QA reporting. We also emphasize governance: segregated pipelines, compliance alignment (SOC 2 / ISO 27001), and full IP provenance on collected data. Importantly, we never build models that compete with you—your data is exclusively yours and is never repurposed or resold.
What happens if we need to change the label schema mid-project?
Change requests are normal as your model evolves. Abaka manages schema updates through versioned guidelines, controlled rollouts, and compatibility planning so you don’t break downstream training jobs. We’ll propose whether to backfill prior data, relabel only targeted slices, or create a new dataset version for clean comparisons. In Abaka Forge, we keep audit trails for changes, including which batches used which rubric version. This approach keeps iteration fast while preserving reproducibility for experiments and production releases.
Can we start with a small pilot before committing to scale?
Yes—pilots are the recommended starting point. A pilot lets your team validate label rubrics, QA thresholds, export formats, and the operational cadence before scaling volume. We typically run the pilot in Week 1–2 after Day 0–3 scoping, then review quality findings and refine guidelines. You’ll receive training-ready files plus QA reporting, disagreement analysis, and recommendations for scaling. Once the pilot meets acceptance criteria, we ramp capacity without changing the core workflow so results remain consistent.
Who owns the data and can it be reused or resold?
Your data is exclusively yours. Abaka does not repurpose, resell, or share your datasets, and we do not build models that compete with you. For collected data, we provide full IP provenance and clear documentation for ownership and licensing so your team can use it safely in training and downstream evaluation. We can also support strict NDAs and segregated secure pipelines to meet enterprise expectations. If you have specific contractual requirements, we align them during scoping.
Do you provide tooling, or do we need to use our own platform?
You can use Abaka Forge, our all-in-one platform for collection, cleaning, annotation, and production delivery across image, video, text, RLHF, and 3D/4D point cloud. Forge supports role-based access, workflow routing, QA dashboards, and export tooling aligned to common ML pipelines. If you already have internal systems, we can integrate by delivering to your required formats and metadata conventions. We’ll confirm your preferred handoff—S3-style delivery, manifests, or dataset registries—during the pilot.
What is the minimum dataset size or engagement size to get started?
There’s no one-size minimum; the right start size depends on the task and how much ambiguity exists in your taxonomy. Many teams begin with a pilot sized to expose edge cases—enough samples to measure disagreement and validate acceptance criteria—then expand once the workflow is stable. For some projects, a few thousand items are sufficient to calibrate; for others (video/3D), a smaller count with deeper QA is more informative. We’ll recommend a pilot scope that fits your timeline and budget.
