How much do training data generation services cost?
Pricing depends on modality, complexity, and QA depth, but we anchor quotes to real unit rates so you can forecast spend. For example, LLM math/coding work can be priced at $18/hr, STEM generalist work at $12/hr, dense captioning at $6/hr, and road lane annotation at $3/km. If you use Abaka Forge platform credits, credits are $0.20 USD each. We’ll propose a pilot budget first, then a production rate card tied to acceptance criteria.
How long does it take to deliver the first batch?
Most teams can reach a reviewed pilot batch within 1–2 weeks after kickoff, depending on security onboarding and how complete your initial guidelines are. We typically use Day 0–3 for scoping and acceptance tests, then Week 1–2 for calibration and pilot delivery. If you already have clear specs and examples, we can move faster; if the taxonomy is evolving, we’ll prioritize a small batch focused on edge cases to prevent drift before scaling production.
What modalities and output formats can you deliver?
We support text, RLHF, images, video, 3D/4D point clouds, LiDAR+camera fusion, and audio. Outputs commonly include JSONL for instruction tuning and preference pairs, COCO-style JSON for vision, masks for segmentation, and structured manifests for batch tracking. For 3D, we deliver common point cloud formats with accompanying JSON/CSV labels, plus consistent naming and metadata so your training pipeline can consume the data without manual transformation.
What accuracy levels can you commit to for generated training data?
Accuracy targets depend on label type and ambiguity, but we can operate with 99% accuracy targets on audited subsets where the task is well-specified and acceptance criteria are measurable. We achieve this through calibration tasks, gold sets, second-pass review, specialist escalation, and targeted audits on edge cases. For inherently subjective tasks (e.g., style preferences), we emphasize rubric clarity, adjudication, and consistency metrics rather than promising a single accuracy number that doesn’t reflect real uncertainty.
How do you handle security and compliance for sensitive datasets?
Abaka supports strict NDAs, segregated secure pipelines, and audit-friendly workflows. We operate with SOC 2 and ISO 27001-aligned programs and support GDPR and CCPA requirements. Access is role-based, datasets are compartmentalized, and we maintain decision trails so you can review what changed between batches. We also emphasize IP provenance and controlled handling to reduce downstream risk during internal security reviews or customer audits.
Can you generate multilingual training data?
Yes. We support multilingual data generation across 50+ countries, including translation QA, localized instruction data, and multilingual preference labeling. For multilingual programs, we set language-specific guidelines, calibration sets, and reviewer assignments to avoid “English rubric drift” being applied incorrectly to other languages. We also deliver consistent schemas across languages (e.g., shared JSONL fields and metadata) so you can train unified models or language-specific variants without reworking the pipeline.
How are you different from other data labeling or synthetic data vendors?
Three differences tend to matter in production: governance, specialization, and exclusivity. Abaka provides secure, segregated pipelines and clear provenance; a large workforce with domain-specialist reviewers (coding, math, medicine, law, business); and a strict policy that your data is exclusively yours—never repurposed, resold, or shared. We also provide Abaka Forge workflows for versioning and change control, so datasets remain reproducible as taxonomies evolve.
What if we need to change guidelines or request revisions mid-project?
Change is normal, but unmanaged changes create drift. We use a change-control process: you propose updates, we revise the spec, run a calibration pass, and document what changed. For significant taxonomy changes, we can reprocess impacted subsets or create mapping tables to keep older versions usable. Weekly reporting highlights drift risks and provides recommendations—so you can improve label definitions without breaking training continuity or losing comparability across experiments.
Can we start with a pilot before committing to a large program?
Yes—most customers start with a pilot. We’ll define acceptance criteria, deliver a small batch, and run a review cycle focused on edge cases and failure modes. The pilot validates label definitions, throughput expectations, and export compatibility with your training stack. Once approved, we scale production with QA gates and versioned deliveries. This approach avoids the common scenario where a large run begins before the rubric is stable, leading to expensive relabeling.
Who owns the data and can it be reused elsewhere?
You own your data. Abaka’s policy is that your datasets are exclusively yours—never repurposed, resold, or shared. We also do not build models that compete with customers, which helps align incentives for long-term programs. We maintain lineage and audit logs so ownership and provenance are clear, and we can support documentation required for internal governance, procurement, or downstream product teams consuming the datasets.
What tools do you use to manage training data generation?
We use Abaka Forge, our platform for collection, cleaning, annotation, and production delivery. It supports multiple data types (text, RLHF, image, video, 3D/4D point cloud) with role-based access, workflow templates, QA layers, and versioned exports. Abaka Forge can also accelerate supported steps with large-model automation while keeping human reviewers in the loop. This improves throughput and consistency without sacrificing oversight.
What is the minimum project size for training data generation services?
There isn’t a strict minimum, but the best fit is when you need repeatable, governed delivery—not a one-off batch. We can start with small pilots (hundreds to a few thousand items) to validate rubrics and exports, then scale to larger production volumes. If your request is very small and simple, we’ll still propose a lightweight plan with clear acceptance criteria so you don’t overpay for unnecessary QA layers, while keeping a path to scale if results are promising.
