How much does a training data generation agency cost?
Pricing depends on modality, complexity, and QA depth, but we can anchor estimates with real unit rates. For example, LLM Math/Coding annotation can be $18/hr, STEM Generalist work can be $12/hr, dense captioning can be $6/hr, image editing tasks can be $8/hr, and road lane labeling can be $3/km. Abaka Forge can also be run on credits at $0.20 USD each for platform usage. We typically propose a small pilot first, then scale once quality gates and throughput are validated.
How fast can you deliver training data for my model?
Many teams validate a workflow in a 2–3 week pilot, then move into ongoing weekly deliveries. Speed depends on how quickly we lock the rubric, how much edge-case ambiguity exists, and how many modalities are involved. Abaka accelerates delivery by standardizing guideline development, calibration rounds, and QA gates in Abaka Forge, then scaling capacity only after acceptance tests are met. If you already have stable specs and examples, we can often start producing pilot batches within the first week.
What modalities and output formats do you support for training data generation?
Abaka supports text, LLM RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion, and audio. Output formats commonly include JSONL for LLM datasets and preferences, CSV/TSV for tabular labels, COCO-style JSON and mask outputs for vision, timestamped annotations for video, and structured JSON plus manifests for 3D and fused sensor datasets. We can also deliver dataset splits, metadata fields, and QA reports so the data is immediately usable in training and evaluation pipelines.
What accuracy can you guarantee for labels and generated data?
Accuracy depends on task clarity and ground-truth definability, but Abaka can reach up to 99% accuracy on suitable tasks using multi-layer QA, calibrated reviewers, and adjudication for ambiguous cases. We avoid vague guarantees by defining acceptance tests during scoping—gold sets, error taxonomies, and sampling-based audits per batch. For creative or open-ended generation tasks, we focus on rubric adherence and consistency rather than pretending there is a single “correct” answer, and we make quality measurable with reviewer agreement and defect tracking.
How do you handle security, NDAs, and sensitive data?
Abaka operates with strict NDAs, segregated secure pipelines, and compliance alignment including SOC 2, ISO 27001, GDPR, and CCPA considerations. Access is controlled by roles, and datasets maintain full IP provenance so ownership is unambiguous. We also follow a key trust principle: we never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. During kickoff, we align on data handling rules, access boundaries, and any additional requirements your security team needs.
Can you generate multilingual training data, and in which languages?
Yes. Abaka supports multilingual data generation and annotation across 50+ countries, which helps with locale-specific language, cultural nuance, and regional formats. We can generate prompts, conversations, and domain Q&A in multiple languages, and we can validate outputs with native-level reviewers using rubric-based QA. For multilingual work, we recommend defining language-specific acceptance tests (tone, register, policy constraints, formatting) and including small calibration batches per locale to ensure consistency before scaling.
How is Abaka different from other data labeling companies?
Abaka is optimized for frontier AI workflows, not just generic labeling. You get reviewer-led quality systems (calibration, gold sets, adjudication), domain-specialized annotators, and Abaka Forge for standardized operations across modalities. On trust and governance, Abaka never builds models that compete with you, and your data is exclusively yours—never repurposed or resold. We also emphasize IP provenance and secure, segregated pipelines so your team can use sensitive prompts, policies, and captures without introducing avoidable vendor risk.
What happens if I need changes to guidelines or labels mid-project?
Change requests are expected in real model iteration cycles. We manage them through versioned rubrics and controlled backfills: your team approves the updated guideline, we run a small recalibration batch to confirm reviewer alignment, then we scale the change. If prior data needs updating, we perform targeted rework using defect tags and sampling to avoid re-labeling everything. This approach keeps datasets comparable across releases while still letting you respond quickly to new failure modes and product requirements.
Can we start with a small pilot before committing to scale?
Yes—pilots are the recommended path. A pilot validates three things: rubric clarity, measurable quality gates, and delivery cadence that matches your training loop. We typically start with a representative sample covering both common and edge cases, then share QA analytics and defect categories so your team can confirm the data aligns with how the model is evaluated. Once the pilot passes acceptance tests, we ramp capacity with the same calibrated workflow so scaling doesn’t introduce drift.
Who owns the training data and can it be reused elsewhere?
You own your data. Abaka’s trust differentiator is that we never build models that compete with you, and your datasets are exclusively yours—never repurposed, resold, or shared. We also maintain full IP provenance for collected data so you can document how assets were sourced and processed. If you need specific contractual language for ownership, retention, and deletion, we align during onboarding and can support strict NDAs and segregated environments for sensitive projects.
What tools do you use to manage annotation and data generation workflows?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, training support, and production operations. It supports text, RLHF, image, video, and 3D/4D point cloud workflows, with automation that can speed pipelines up to 50x via large-model assistance. Abaka Forge provides role-based access controls, audit trails, and structured reporting so your team can track throughput, quality, and drift over time. We also deliver in standard export formats to plug into your existing ML stack.
What is the minimum project size you can support?
There isn’t a single minimum because the right starting point depends on your risk tolerance and how defined the task is. Many teams begin with a pilot sized to cover representative scenarios and edge cases—large enough to validate quality gates, small enough to iterate quickly on rubrics. If you only need a few hundred items, we can still run a structured workflow with calibration and QA. For larger goals, we design a phased plan so you can scale reliably without committing upfront to a massive volume.
