How much does a model training data service provider cost?
Pricing depends on modality, domain difficulty, and QA requirements, but Abaka provides concrete rate cards so you can forecast spend. For example, LLM Math/Coding annotation can be $18/hr, STEM Generalist work $12/hr, Dense Captioning $6/hr, and Image Editing $8/hr. For autonomous driving lane labeling, pricing can be $3/km. We’ll confirm sampling, acceptance criteria, and expected throughput, then propose a scoped pilot so you can validate quality and cost before scaling.
How fast can you deliver training datasets after kickoff?
For well-scoped work, teams commonly see a pilot in Week 1–2 and production deliveries in a 2–3 week window for the first batch. Exact timing depends on modality, rubric complexity, and your review bandwidth. Abaka runs calibration first to prevent rework later, then ships rolling deliveries so your team can start training earlier instead of waiting for a single final handoff.
What data types and formats do you support for training data?
Abaka supports text, RLHF, image, video, 3D/4D point clouds, LiDAR + camera fusion workflows, and audio. Outputs are delivered in practical formats your pipelines can ingest, such as JSON/JSONL, CSV/TSV, Parquet, masks for segmentation, and timestamped transcripts (SRT/VTT). We agree on an export contract up front—fields, schema, naming, and metadata—so integration is predictable.
How do you ensure annotation accuracy and consistency at scale?
We combine multi-layer QA with reviewer calibration, gold sets, and adjudication lanes for hard edge cases. Abaka also controls throughput (up to 500 files/day per annotator) to prevent speed-first labeling that harms quality. In Abaka Forge, instructions are versioned and checklists are enforced, making changes traceable and ensuring that new reviewers follow the same rubric as the initial pilot.
Can you meet security requirements like SOC 2 and ISO 27001?
Yes. Abaka operates with SOC 2 and ISO 27001 alignment and supports GDPR and CCPA requirements. We use strict NDAs, segregated secure pipelines, and role-based access controls so only approved contributors can view specific datasets. We also maintain full IP provenance and audit trails to simplify security reviews and help your team demonstrate governance over sensitive training data.
Do you support multilingual training data and global coverage?
Yes. Abaka’s network spans 50+ countries, enabling multilingual data creation, labeling, and evaluation for global products. We can localize prompts and rubrics, apply language-specific QA checks, and ensure consistent taxonomy mapping across locales. This is especially important for instruction following and RLHF, where subtle phrasing differences can change intent and lead to inconsistent preference signals.
How is Abaka different from other data labeling vendors?
Abaka is positioned as a trustworthy data partner for frontier AI with strong governance and a non-compete stance: we never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. You also get Abaka Forge for end-to-end workflow control across modalities, plus access to specialist domains (math, coding, medicine, law) when generic labeling isn’t enough for your quality bar.
How do you handle change requests if our taxonomy or rubric changes mid-project?
Change requests are expected in real projects, so we treat specs like versioned products. In Abaka Forge we version instructions and track exactly which items were labeled under which rubric. When definitions change, we can run targeted re-labeling on impacted subsets instead of restarting the entire dataset. You’ll get a clear impact assessment—cost, timeline, and risk—so your team can choose between backward compatibility or a clean break.
Can we start with a pilot before committing to a larger program?
Yes. Most teams start with a pilot batch to validate rubric clarity, agreement rates, and export compatibility. The pilot is designed to surface edge cases early, tighten guidelines, and confirm acceptance criteria before scaling. After pilot sign-off, we ramp production with the same instruction versioning and QA logic, reducing the chance of unpleasant surprises when you increase volume.
Who owns the data and outputs you produce for us?
You do. Abaka’s operating principle is that your data is exclusively yours—never repurposed, resold, or shared. We maintain clear provenance and access controls, and we can support audit requests around how data was collected or labeled. This ownership clarity is critical when datasets become long-term strategic assets used across multiple model generations.
What tooling do we use to manage and review the work?
Work is managed in Abaka Forge, our all-in-one platform for collection, cleaning, annotation, and production workflows. Your team can review samples, comment on edge cases, approve instruction versions, and monitor QA metrics. Abaka Forge supports all major modalities—text, RLHF, image, video, and 3D/4D point cloud—and can accelerate supported tasks with large-model automation.
What is the minimum dataset size or project size to work with Abaka?
There isn’t a single minimum that fits every modality; we’ll recommend a starting batch size that’s large enough to calibrate reviewers and expose edge cases, but small enough to move fast. Many teams begin with a pilot sized for clear statistical signal on agreement and error patterns. If you have a tight deadline, we can prioritize a focused subset first—then scale once the rubric and export contract are locked.
