How much does a training data generation provider cost?
Pricing depends on modality, difficulty, and QA depth, but Abaka offers transparent, real rate cards. Examples: STEM generalist work is $12/hr, math/coding specialist labeling is $18/hr, dense captioning is $6/hr, image editing is $8/hr, and autonomous driving road lane labeling is $3/km. For evaluation, red teaming can be $8/eval and math capabilities can be $12/eval. We typically start with a paid pilot to confirm rubric fit and then scale with predictable weekly delivery and spend controls.
How quickly can you start delivering training data?
Most engagements start with a Day 0–3 setup for scope, rubrics, and security, followed by a Week 1–2 pilot batch for calibration. Scale production typically begins in Week 2–3 once your team approves guidelines and acceptance criteria. If you already have stable schemas, we can accelerate by reusing existing rubrics and focusing the pilot on edge cases. The goal is to get you a reviewable first batch quickly while ensuring downstream consistency and measurable QA from the start.
What modalities and file formats do you support for training data generation?
We support text, LLM RLHF, image, video, audio, 3D/4D point cloud, and sensor-fusion workflows. Deliveries commonly include JSONL, JSON, CSV/TSV, Parquet, timecoded annotations, manifests, and audit reports. If you have a custom schema, we can map outputs to your required fields and provide validation checks so the dataset loads cleanly into your training and evaluation pipelines. We also maintain guideline and schema versioning to prevent drift across iterations.
How do you ensure annotation accuracy and consistency?
Accuracy comes from process control: clear rubrics, reviewer calibration, gold tasks, multi-layer QA, and disagreement resolution workflows. We also cap throughput (e.g., 500 files/day per annotator) to reduce fatigue and keep judgment stable. For specialized tasks like math, coding, medicine, or legal reasoning, we route work to domain-qualified reviewers and add targeted audits. You receive QA summaries and changelogs with each delivery so your team can trace what changed and why.
What security and compliance standards do you support?
Abaka operates with SOC 2 and ISO 27001 controls and aligns workflows to GDPR and CCPA expectations. We use strict NDAs, segregated secure pipelines, role-based access patterns, and auditable processes to reduce operational risk. We also emphasize provenance so you can demonstrate how data was sourced and labeled. If your organization has specific security requirements, we incorporate them during Day 0–3 setup and document controls for procurement and internal audits.
Can you generate multilingual training data?
Yes. Abaka supports multilingual data generation across 50+ countries, including instruction data, classification, RLHF preference ranking, and evaluation tasks. We can recruit native speakers and apply language-specific rubrics, especially for nuanced safety policy or customer-support intents. Deliveries include language tags, locale metadata, and consistent schema outputs to help you train multilingual models and evaluate them fairly. If you need domain-specific language (legal, medical, technical), we can route tasks to qualified reviewers to reduce translation drift.
How are you different from other data labeling vendors?
Abaka is designed for frontier AI teams that need governance and trust alongside throughput. We provide provenance-first delivery, secure project segregation, and a clear commitment: we never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. You also get Abaka Forge to manage workflows and audits, plus access to vertically specialized annotators and scholar-grade reviewers for complex domains like math, coding, medicine, and law.
What if we need changes to guidelines or schemas mid-project?
Change requests are expected in real training data programs. We manage them through versioned rubrics, weekly change control, and controlled rollouts so updates don’t invalidate prior work. When a definition changes, we can (1) fork the dataset version, (2) selectively relabel impacted samples, and (3) document exactly what changed in the changelog and QA summary. This keeps training runs reproducible and prevents silent drift that would otherwise show up as confusing model regressions.
Do you offer a pilot for training data generation services?
Yes—pilots are the fastest way to validate rubric clarity, edge-case handling, and delivery formats before scaling. A typical pilot runs in Week 1–2 and includes a small batch, calibration, QA reporting, and a review session to finalize acceptance criteria. If the pilot reveals ambiguity, we refine guidelines and rerun a subset until the team aligns on “correct.” After approval, we scale in Week 2–3 with predictable weekly deliveries and ongoing metrics reviews.
Who owns the data and annotations you generate?
You do. Abaka’s operating model is built around customer ownership and control: your data is exclusively yours and is never repurposed, resold, or shared. We also maintain provenance and audit trails so ownership is defensible, not just contractual. If you provide source data, we treat it as your IP; if we collect data for you, we document sourcing and deliver rights-aligned artifacts based on the agreed scope. This reduces downstream risk as your program scales.
What tooling do you use to manage training data generation?
We use Abaka Forge—an all-in-one platform for collection, cleaning, annotation, RLHF, and production delivery across image, video, text, and 3D/4D point cloud. Forge supports workflow automation (including large-model assistance for speed), reviewer routing, QA audits, and export-ready formatting. It also helps governance by keeping guidelines, schema versions, and changelogs attached to each batch. If your team has existing tools, we can integrate via agreed exports and validation checks.
What is the minimum project size to work with your team?
We support both small pilots and large-scale programs. Many teams start with a focused pilot batch (Week 1–2) to confirm rubric fit and delivery formats, then scale based on results. Minimums depend on modality and task complexity—RLHF and specialist reasoning tasks often need enough volume to calibrate reviewers, while simpler labeling can start smaller. If you share your target use case, timeline, and expected throughput, we’ll recommend a minimum pilot size that produces meaningful QA signal without overcommitting budget.
