How much does a model training data agency cost?
Pricing depends on modality, domain difficulty, and QA depth, but we provide concrete unit economics during scoping. For example, LLM math/coding annotation can be $18/hr, STEM generalist work $12/hr, dense captioning $6/hr, image editing $8/hr, and road lane labeling $3/km when applicable. Abaka Forge platform credits are $0.20 USD each for workflow and automation usage. After a small pilot, we can forecast cost per batch and recommend the best trade-off between accuracy targets and throughput.
How long does it take to deliver training data from kickoff?
Most teams see meaningful usable output in 2–3 weeks, depending on complexity and review requirements. Day 0–3 is typically scoping, security setup, and sampling design; Week 1–2 focuses on a pilot and calibration against gold sets; Week 2–3 scales production with multi-layer QA and structured exports. If you already have stable guidelines, timelines compress; if you’re defining a new taxonomy or RLHF rubric, we invest more time in calibration to avoid costly relabeling later.
What data modalities and formats can you deliver for model training?
We support text, LLM RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion, and audio—managed through Abaka Forge. Typical outputs include JSONL for instruction tuning and preferences, COCO-style JSON for vision tasks, timestamped JSON for video, and sensor-aligned annotation bundles for fused autonomy datasets. If you have a custom schema, we can map to it as long as acceptance criteria are defined. We also include train/val/test splits and QA metadata to make datasets directly usable.
What accuracy can you achieve for labeled training data?
Accuracy depends on task ambiguity, guideline maturity, and reviewer depth, but Abaka supports targets up to 99% accuracy through calibration and multi-layer QA. We don’t rely on a single pass: we use gold sets, adjudication for disagreements, sampling-based audits, and error analysis to identify systematic confusion. For expert domains (math, coding, medicine, law), we can assign trained specialists and scholar-network reviewers. We’ll align accuracy definitions upfront so your team knows what “99%” means in practice.
How do you protect sensitive data and meet enterprise security requirements?
Abaka operates with SOC 2 and ISO 27001 controls, supports GDPR and CCPA alignment, and uses strict NDAs plus segregated secure pipelines. Access is role-based, workflows are auditable, and we can implement least-privilege policies for sensitive datasets. We also emphasize provenance: you maintain clear chain-of-custody for what was labeled, by whom, and under which guideline version. Abaka never builds models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared.
Do you support multilingual training data and regional coverage?
Yes. Abaka operates across 50+ countries and can staff multilingual programs for both labeling and evaluation. We support language-specific guidelines, locale-aware annotation rules, and reviewer calibration to avoid inconsistent judgments across regions. This is useful for translation evaluation, intent classification, culturally sensitive safety policies, and multilingual instruction-following. We can also build balanced sampling plans so your dataset reflects target markets rather than over-representing a single locale.
How are you different from other data labeling vendors or marketplaces?
Abaka is built for frontier AI reliability: measurable acceptance criteria, multi-layer QA, domain specialists, and a secure operating model. We don’t just “provide workers”—we run a controlled pipeline with versioned guidelines, adjudication, and weekly reporting. We also differentiate on trust: Abaka never builds models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. With Abaka Forge, you also get unified multimodal workflows instead of stitching together tools and vendors.
What happens if we need to change guidelines after work has started?
Change requests are expected—models evolve, and so do definitions. Abaka versions guidelines, quantifies which slices are affected, and performs targeted backfills instead of relabeling everything. We update gold sets and calibration checks so the new policy is applied consistently moving forward. You’ll receive side-by-side samples and QA reports to confirm the change is implemented correctly. This approach keeps delivery predictable and prevents “silent drift” where different batches encode different meanings.
Can we start with a pilot before committing to a larger program?
Yes—most engagements begin with a pilot designed to de-risk quality and workflow. We’ll scope a representative sample, define acceptance metrics, run calibration with trained annotators and reviewers, and deliver exports in your preferred schema. The pilot validates guideline clarity, throughput, and error patterns before scaling. After the pilot, we can propose a production plan with a clear weekly cadence, estimated capacity, and a QA framework aligned to your evaluation needs.
Who owns the labeled data and derived datasets?
You do. Abaka’s operating principle is that your data is exclusively yours—never repurposed, resold, or shared. We do not use your datasets to train our own models, and we never build models that compete with you. Contracts are designed to preserve your IP ownership, and we provide provenance and audit trails so you can document how the dataset was created. If you require additional contractual terms (e.g., strict sublicensing constraints), we can align during scoping.
What tools and platforms do you use for annotation and QA?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, and production delivery across text, RLHF, image, video, and 3D/4D. Forge supports versioned guidelines, role-based access, audit trails, adjudication workflows, and export pipelines for common training formats. It also applies large-model automation to accelerate repeatable steps (up to 50x faster) while keeping humans in the loop for verification and edge cases. If you have internal tooling, we can align exports and workflows to integrate cleanly.
What is the minimum project size to work with your model training data agency?
There’s no single minimum, but the best starting point is a pilot sized to validate quality and throughput—often a few thousand items for text or a representative set of clips/scenes for vision and 3D. For RLHF, a pilot can be sized around a focused task family with clear rubrics and adjudication. If your need is smaller (e.g., a single evaluation set), we can still help, but we’ll recommend the smallest scope that produces stable metrics and reduces the risk of rework.
