How much does an ML data labeling solution cost?
Pricing depends on modality, complexity, and QA depth, but we can anchor scope using known unit rates. For example, LLM math/coding annotation is $18/hr, STEM generalist work is $12/hr, image editing is $8/hr, dense captioning is $6/hr, and road lane annotation can be priced at $3/km. We’ll propose a plan that matches your acceptance criteria (e.g., 99% audited accuracy), delivery timing, and security constraints, and then confirm costs after a small pilot batch that validates guidelines and edge cases.
How long does it take to launch a data labeling project?
Most teams can launch quickly because we start with scope and samples, then move into labelbook + calibration before production. A typical path is Day 0–3 for requirements and acceptance criteria, Week 1–2 for labelbook and a pilot batch, and Week 2–3 to ramp into steady production with multi-layer QA. If your task is highly specialized (new taxonomies, multi-sensor fusion, or strict access controls), timelines can extend, but we keep progress predictable with weekly milestones and versioned deliverables.
What data types and output formats do you support for ML labeling?
We support text, LLM RLHF, images, video, 3D/4D point cloud, LiDAR + camera fusion, and audio. Output formats are tailored to your pipeline and commonly include JSONL, CSV/TSV, COCO JSON, YOLO, PNG/RLE masks, timestamped transcripts (SRT/VTT), and per-frame exports for video and sensor data. If you have a custom schema, we can implement validation checks so every delivery is consistent, and we maintain versioned schemas to keep training runs reproducible over time.
How do you ensure labeling accuracy and consistency?
Accuracy comes from clear definitions and measurable QA, not a single review pass. We build a versioned labelbook, run calibrations to align reviewers, and use multi-layer QA including gold sets, consensus checks on ambiguous samples, and adjudication workflows to resolve disagreements. We also track error taxonomies so the same confusion doesn’t repeat in later batches. When you target 99% audited accuracy, we align sampling plans and acceptance tests to that goal, and share QA reports with each delivery.
Is Abaka secure enough for sensitive or proprietary datasets?
Yes. Abaka operates with enterprise controls including SOC 2 and ISO 27001-aligned practices, GDPR and CCPA readiness, strict NDAs, and segregated secure pipelines. Access can be restricted by role and project, and we maintain audit trails for work performed. We also provide full IP provenance and 0% copyright risk on collected data, which supports internal audits. Importantly, we never build models that compete with you, and your data is never repurposed, resold, or shared.
Can you label multilingual data and region-specific edge cases?
We support multilingual labeling across 50+ countries, including region-specific terminology, formatting conventions, and culturally dependent edge cases. For text and RLHF tasks, we match reviewers to language proficiency and domain needs (e.g., business, law, medicine, mathematics). We recommend starting with a calibration set per language to validate guidelines and ensure consistent interpretations. Deliverables can include language tags, locale metadata, and separate acceptance criteria by language if your model performance requirements vary across markets.
How is Abaka different from other data labeling vendors?
Two differences matter most in practice: quality systems and incentives. Abaka pairs domain-aligned reviewers with multi-layer QA and adjudication so ambiguity becomes clarified rules, not recurring noise. And we never build models that compete with you—your data remains exclusively yours and is never repurposed, resold, or shared. Abaka is self-funded and profitable (no VC, no acquisition pressure), which keeps focus on long-term delivery, security posture, and consistent outputs rather than short-term volume at the expense of quality.
What if we need to change the labeling schema mid-project?
Schema changes are common as you learn from model errors. We handle change requests through versioned labelbooks and controlled rollout: we update definitions, retrain reviewers, and run a small validation batch to confirm the change behaves as intended. When needed, we can backfill earlier batches or create mapping rules so you can maintain comparability across dataset versions. We also document what changed and why, so downstream training and evaluation results remain explainable to stakeholders.
Do you offer a pilot or trial for ML data labeling?
Yes—a pilot is the fastest way to de-risk ambiguity and confirm throughput. We typically run a small representative batch that includes normal cases and known edge cases, then measure agreement, QA findings, and time-to-delivery. The pilot produces a finalized labelbook, an acceptance test plan, and export samples in your required formats. After the pilot, we ramp to production with calibrated reviewers and predictable weekly reporting, so you can scale without losing consistency.
Who owns the labeled data and can you reuse it?
You own your data and the labeled outputs. Abaka does not repurpose, resell, or share your datasets, and we never use customer data to train a competing model. We maintain full IP provenance and operate under strict NDAs and segregated secure pipelines. If we collect data on your behalf, we ensure 0% copyright risk on collected data and document provenance so you can use the assets confidently in training, evaluation, and internal governance reviews.
What tools do you use and can you integrate with our pipeline?
We use Abaka Forge as the core platform for collection, cleaning, annotation, QA, and delivery workflows across text, RLHF, image, video, and 3D/4D point cloud. We can export to standard formats (JSONL, COCO, YOLO, CSV, masks, timecoded transcripts) and align fields to your training schema. If you need custom validations or batch packaging conventions, we implement them as part of the delivery process so your team can ingest datasets without manual transformation or fragile scripts.
What is the minimum project size for an ML data labeling solution?
There’s no one-size minimum, but the best results come when you can run a pilot batch large enough to capture edge cases and measure agreement. Many teams start with a few thousand items for text/classification or a smaller curated set for complex modalities like video and 3D. From there, we scale to production volumes based on your roadmap and evaluation cadence. If you only need a small evaluation set, we can scope a focused engagement with stronger QA density to maximize usefulness.
