How much does a training data generation company cost?
Pricing depends on modality, complexity, and QA depth, but we can anchor estimates with real unit rates. For example, LLM Math/Coding annotation is $18/hr, STEM Generalist work is $12/hr, Dense Captioning is $6/hr, and Road Lane annotation is $3/km. Platform usage can also be run through Abaka Forge credits at $0.20 USD each. After a short scoping call, we propose a priced pilot (often 2–3 weeks) with clear acceptance criteria and optional scale tiers.
How fast can you deliver a first batch of training data?
Most teams begin with a pilot that takes about 2–3 weeks to finalize guidelines, calibrate reviewers, and validate exports. In the first days (Day 0–3), we align on formats and acceptance criteria, then start production with QA instrumentation. The exact timeline depends on modality and the amount of policy/rubric design required (common for RLHF and evaluation). After pilot sign-off, we can scale volume quickly while keeping the same QA thresholds and reporting cadence.
What data types and output formats can you deliver?
We support text, RLHF preference data, images, video, 3D/4D point clouds, LiDAR + camera fusion, and audio. Output formats are tailored to your stack and typically include JSONL and Parquet for LLM training, COCO-style JSON and masks for vision, and synchronized manifests/metadata for sensor data. We also deliver supporting artifacts such as taxonomies, guideline versions, QA reports, and sampling documentation so your team can reproduce results and compare runs accurately over time.
What accuracy levels can you commit to for labels and evaluations?
We commonly target up to 99% accuracy depending on task definition, ambiguity, and measurable acceptance criteria. Achieving high accuracy requires calibrated guidelines, gold sets, adjudication for disagreements, and multi-layer QA—not just more annotators. During the pilot, we establish the exact metric (e.g., per-class precision/recall, rubric adherence, inter-rater agreement bands) and the sampling plan for auditing. If the task is inherently subjective, we focus on consistency and documented rationale rather than forcing misleading precision.
How do you handle security and compliance requirements?
Abaka operates with SOC 2 and ISO 27001 controls and aligns with GDPR and CCPA requirements. We use strict NDAs, segregated secure pipelines, and role-based access to limit exposure to only the people and systems required for delivery. For collected data, we provide full IP provenance and a 0% copyright risk posture on collection. We also support audit-friendly documentation—dataset versioning, access logs, and delivery manifests—so security reviews don’t become a surprise blocker.
Can you generate training data in multiple languages?
Yes. We support multilingual programs across 50+ countries, which helps with both language coverage and regional context. For LLM instruction tuning and RLHF, we can localize prompts, rubrics, and policy constraints to maintain consistent intent across languages. We also separate language fluency from domain expertise when needed—for example, pairing strong bilingual reviewers with math/coding specialists. Deliverables include language metadata and QA sampling results so your team can spot uneven performance and correct it quickly.
How are you different from other data labeling vendors?
Two differences matter most for teams building frontier systems: trust and rigor. Abaka never builds models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. Operationally, we pair specialist annotators with multi-layer QA, adjudication, and versioned guidelines in Abaka Forge, so quality doesn’t decay as requirements change. Many vendors optimize for raw throughput; we optimize for stable evaluation signal, audit readiness, and repeatable weekly delivery.
How do you manage change requests when our labeling guidelines evolve?
We expect guidelines to evolve—especially for new tasks like agent behavior scoring or safety policy evaluation. In Abaka Forge, we version instructions, track change requests, and isolate which batches were produced under which guideline version. When changes are significant, we run a short recalibration (updated gold sets and reviewer training), then resume production with measurable acceptance checks. If backfills are needed, we can selectively rework only affected segments rather than relabeling the entire dataset.
Can we start with a pilot before committing to a larger engagement?
Yes—most teams should. A pilot (typically 2–3 weeks) validates your spec, your acceptance criteria, and the export formats before scaling. You’ll receive a representative batch, QA metrics, defect analysis, and a recommended ramp plan. The pilot also surfaces hidden complexity early, like ambiguous class boundaries, missing metadata fields, or rubric edge cases in RLHF. After sign-off, we scale with the same workflows, so the pilot work is not wasted—it becomes the foundation for production.
Who owns the data and can it be reused for other customers?
You own your data outputs, and we do not repurpose, resell, or share your data—ever. Abaka’s trust differentiator is explicit: we never build models that compete with you, and we maintain segregated pipelines with strict NDAs. For collected datasets, we provide full IP provenance and documentation to support your internal governance. If you require additional contractual clauses on exclusivity, retention, or deletion, we can align them during scoping so your legal team is comfortable before production begins.
What tools do you use to manage annotation and QA?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, evaluation, and production handoff. Forge supports text, RLHF, image, video, and 3D/4D point cloud workflows, with dataset versioning, role-based access, and audit logs. Large-model automation can accelerate routine steps up to 50x, while humans focus on judgment-heavy tasks. Exports are automated and repeatable, so your pipeline stays stable as volume grows and guidelines change.
What is the minimum dataset size or budget to get started?
Minimums depend on modality and what you need to validate. For many LLM projects, a useful pilot starts with a few thousand high-quality items (or a smaller set of complex, rubric-heavy evaluations) to establish reliable QA signals. For vision and sensor work, we often start with a smaller but diverse sample that spans conditions and edge cases. We’ll recommend a minimum that’s large enough to measure quality and model impact without over-committing budget before the spec is proven.
