How much does a model training data solution cost?
Pricing depends on modality, complexity, and the level of expertise required (e.g., general labeling vs. scholar-grade math/coding). As concrete reference points, Abaka programs can be staffed with LLM Math/Coding annotators at $18/hr, STEM Generalists at $12/hr, Dense Captioning at $6/hr, and Image Editing at $8/hr; some automotive lane work is priced at $3/km. For Abaka Forge usage, credits are $0.20 USD each. Talk to an Expert and we’ll scope a pilot with a clear per-deliverable quote.
How fast can you start delivering training data?
Most engagements begin with a short discovery and pilot setup, then ramp into production. For many teams, the operational rhythm from pilot to predictable deliveries is reached within 2–3 weeks, depending on modality and guideline maturity. We front-load risks—taxonomy clarity, acceptance metrics, security requirements—so you avoid late rework. If you already have guidelines and a gold set, timelines can be faster; if you need new rubrics or sourcing, we’ll plan milestones with explicit handoffs.
What modalities and formats do you support for model training data?
Abaka supports text, RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion, and audio workflows. Deliverables are tailored to your pipeline and can include JSONL, CSV, Parquet, mask assets, and per-sensor bundles for fusion use cases. If you have an internal schema, we can map outputs to it and version changes over time. The goal is to reduce integration overhead so data drops are ingestible on day one, not after weeks of conversion.
What accuracy levels can you achieve for training data labeling?
For many programs, Abaka targets up to 99% accuracy using multi-layer QA, calibration tasks, and specialist reviewers for hard domains. Accuracy is defined against your acceptance criteria—class definitions, edge-case rules, and sampling plans—so we agree on what “correct” means before scaling. We also cap throughput at 500 files/day per annotator where applicable to protect attention. When tasks are inherently ambiguous, we use escalation paths and guideline updates to reduce disagreement rather than hiding it.
How do you keep our data secure during labeling and RLHF?
Abaka operates with strict NDAs and segregated secure pipelines, and aligns to SOC 2 and ISO 27001 practices, as well as GDPR and CCPA requirements. Access is role-controlled, workflows are auditable, and deliveries can be packaged to match your internal governance needs. We also maintain full IP provenance for collected data, with 0% copyright risk on collected data. If you require additional controls (network restrictions, special handling), we’ll scope them during Day 0–3.
Do you support multilingual training data and global coverage?
Yes. Abaka operates across 50+ countries and supports multilingual text, evaluation prompts, and audio workflows (including multilingual TTS evaluation where appropriate). We can create locale-specific guidelines, run language-specific QA, and manage consistent taxonomy mapping across regions. This is especially useful when you’re expanding a model into new markets and need both linguistic fidelity and cultural appropriateness. Tell us your target languages and quality bar, and we’ll propose a staffing and review plan.
How is Abaka different from traditional data labeling vendors?
Abaka is designed for frontier AI programs that need governance, provenance, and specialist judgment—not just low-cost labels. We provide Abaka Forge for end-to-end workflows, large-model automation to accelerate parts of the pipeline (up to 50x), and access to scholar-network domains like coding, mathematics, medicine, and law. We also never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. The focus is predictable, auditable datasets that withstand rapid iteration.
What if we need to change guidelines or request relabeling mid-project?
Change is expected—new model behaviors, new edge cases, and new product constraints. We handle updates through controlled versioning: guideline diffs, taxonomy version bumps, targeted relabeling of affected slices, and updated QA sampling plans. This keeps historical comparisons meaningful and avoids “silent” drift. We’ll recommend whether to relabel fully, partially, or create an adapter layer in exports, depending on the blast radius. Weekly reporting ensures change requests are incorporated without derailing delivery cadence.
Can we run a pilot before committing to a long-term program?
Yes. Pilots are the default way to validate task definitions, tooling fit, and quality thresholds. In a pilot, we deliver a representative batch, measure reviewer agreement, and identify edge cases that require policy decisions. You’ll receive sample exports aligned to your training stack so integration can be tested early. After pilot acceptance, we ramp to production with calibrated teams and multi-layer QA. Talk to an Expert and we’ll propose a pilot plan with clear scope, timeline, and acceptance gates.
Who owns the data and the labels you produce?
You do. Abaka’s operating principle is that your data is exclusively yours—never repurposed, resold, or shared. We work under strict NDAs, and deliveries can include documentation that supports IP provenance and chain-of-custody. If you provide raw data, outputs are returned in your preferred formats with versioning. If Abaka sources or collects data, we ensure provenance documentation is included so you can confidently use it for training, evaluation, and audits.
What tools will my team use to manage the project?
Work is executed in Abaka Forge—our all-in-one platform for collection, cleaning, annotation, and production delivery across text, RLHF, image, video, and 3D/4D. Forge supports workflow templates, reviewer queues, audits, and structured exports. We can also coordinate with your internal ticketing and dataset management processes via agreed handoffs and reporting. If you already have internal tooling, we’ll align exports and processes so you don’t have to rebuild your pipeline.
What is the minimum dataset size or minimum engagement to start?
There’s no fixed minimum size; we can start with a small pilot designed to surface ambiguity and validate quality before scaling. Many teams begin with a few hundred to a few thousand items (or an equivalent RLHF/evaluation batch) to pressure-test guidelines and export formats. From there, we scale based on model needs, release cadence, and modality complexity. If you’re unsure what minimum makes sense, we’ll recommend a pilot size tied to measurable acceptance metrics and edge-case coverage.
