How much do ml data labeling solutions cost with Abaka?
Pricing depends on modality, complexity, and reviewer depth, but we can anchor budgets with concrete rates. For example, LLM math/coding labeling is $18/hr, STEM generalist labeling is $12/hr, dense captioning is $6/hr, and road lane annotation is $3/km. We’ll propose a pilot scope with a clear delivery plan, QA gates, and an estimated total cost based on your target volume and weekly cadence. Talk to an Expert to get a line-item quote tied to your schema and acceptance tests.
How fast can you start and deliver the first batch?
Most teams can start quickly because we use a proven kickoff process and an established workforce. Typically, Day 0–3 is used for scoping, secure access setup, and guideline drafting; Week 1–2 runs a pilot batch with calibration; and Week 2–3 scales into production delivery. If you already have stable guidelines and a gold set, we can compress the timeline by starting directly with a pilot run. Delivery cadence is then set to weekly or biweekly releases aligned with your training schedule.
What modalities and output formats do you support for data labeling?
Abaka supports text, LLM RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion, and audio workflows in Abaka Forge. Output formats are tailored to your pipeline, commonly including JSONL, CSV/TSV, COCO-style JSON, segmentation mask PNGs, timecode-based video exports, and bundled per-frame 3D annotations. We align schemas to your trainers and evaluators, and we version exports so you can reproduce experiments and compare model releases over time.
How do you ensure labeling accuracy and consistency at scale?
We prevent drift with a structured QA system: versioned guidelines, a calibration gold set, reviewer rubrics, and adjudication for ambiguous cases. Abaka also limits annotator throughput to a maximum of 500 files/day per annotator to protect quality. For high-stakes programs, we target 99% accuracy by matching tasks with domain-qualified reviewers and layering QA checks. You can also define acceptance tests—sampling rules, disagreement thresholds, and must-fix categories—so each delivery batch is measurable, not subjective.
Can you meet enterprise security and compliance requirements?
Yes. Abaka operates with SOC 2 and ISO 27001-aligned practices and supports GDPR and CCPA-aligned handling for personal data. We use strict NDAs, segregated secure pipelines, and role-based access so only authorized contributors can see sensitive datasets. We also maintain full IP provenance and commit that your data is exclusively yours—never repurposed, resold, or shared. If your team requires additional security documentation, we can provide audit-ready process artifacts during onboarding.
Do you support multilingual data labeling and global coverage?
Yes. Abaka supports multilingual labeling through a global network spanning 50+ countries, which helps match annotators to language and locale-specific context. We can build language-specific guidelines, localized taxonomies, and separate calibration sets to prevent cross-language drift. For LLM and chatbot projects, we also support multilingual instruction following and preference data, including rubric-based scoring and adjudication. Deliveries can be segmented by language, region, or market so you can train and evaluate models with clear coverage and comparability.
How are you different from other data labeling vendors?
Abaka focuses on trust, domain alignment, and reproducibility. We never build models that compete with you, and we provide full IP provenance so you can use outputs confidently. Operationally, we combine a large specialized workforce with controlled throughput (up to 500 files/day per annotator) and multi-layer QA to reduce drift across weeks of delivery. Abaka Forge adds workflow controls, versioning, and automation assistance so you get both speed and governance instead of choosing one.
Can we change the labeling schema or guidelines mid-project?
Yes—change requests are common as error analysis reveals new edge cases. We manage changes through versioned guidelines and batch boundaries, so you can decide whether to apply updates only to new data or to backfill prior batches. We’ll quantify expected impacts—cost, timeline, and comparability—before executing. Abaka Forge tracks guideline versions and links them to outputs, making it straightforward to maintain reproducible experiments while still evolving the dataset as your model and product requirements mature.
Do you offer a pilot for ml data labeling solutions before scaling?
Yes. We typically recommend a pilot to validate taxonomy, QA gates, throughput, and export compatibility. A pilot includes guideline drafting or refinement, a gold set, annotator calibration, reviewer adjudication, and a delivery readout that highlights disagreement patterns and edge-case categories. After pilot sign-off, we scale into production with a weekly cadence and the same acceptance tests. This approach reduces relabel churn and gives your team confidence before committing to larger volumes.
Who owns the labeled data and can you reuse it?
You own your labeled outputs and the datasets you provide. Abaka’s policy is explicit: your data is exclusively yours—never repurposed, resold, or shared. We also maintain full IP provenance to reduce risk when datasets are used across multiple internal products or research tracks. If you require additional contractual language for ownership, retention, or deletion, we can align terms during onboarding. Our goal is to act as an extension of your team, not a data broker.
What tooling do you use for labeling and QA workflows?
Work is executed in Abaka Forge—our all-in-one platform for collection, cleaning, annotation, and production workflows across text, RLHF, image, video, and 3D/4D point cloud. Forge supports role-based access, automation-assisted labeling, reviewer checkpoints, adjudication, and export pipelines. The platform is designed to keep guidelines, tasks, and outputs linked through versioning, so your team can audit decisions and reproduce datasets across releases. We can also align exports to your internal storage and ML pipeline conventions.
What is the minimum project size to get started?
There’s no one-size minimum; we scope based on whether the work can produce a meaningful learning signal and a measurable QA outcome. Many teams start with a pilot sized to validate guidelines and edge cases—often a few hundred to a few thousand items—then scale once acceptance tests are stable. For complex modalities like video or 3D, the minimum may be smaller but requires deeper reviewer time. Talk to an Expert and we’ll recommend the smallest scope that can confidently de-risk production scaling.
