How much does a supervised learning data agency cost?
Pricing depends on modality, complexity, and the level of domain expertise required, but we keep rates concrete and scope-based. Examples: LLM math/coding annotation can be $18/hr, STEM generalist work can be $12/hr, dense captioning can be $6/hr, and road lane annotation can be $3/km. For supervised learning programs, we typically propose a pilot batch first, then scale once QA targets and throughput are validated. Talk to an Expert with your sample files for an exact quote.
How fast can you start and deliver the first batch?
Most teams can launch a supervised learning pilot in 2–3 weeks, depending on security onboarding and how defined your taxonomy is. Day 0–3 is usually scoping and workflow setup, then Week 1–2 is calibration and guideline tuning, followed by a production ramp. If you already have stable label definitions and sample data, we can move faster; if the task is ambiguous, we prioritize calibration to prevent expensive relabel cycles later.
What modalities and output formats do you support for supervised learning?
We support text, LLM RLHF-style preference data, images, video, 3D/4D point clouds, LiDAR + camera fusion, and audio. Output formats are tailored to your pipeline and commonly include JSONL/CSV/Parquet for text and RLHF data, COCO/YOLO/VOC and mask formats for images, timestamped JSON/CSV for video, and PCD/LAS/JSON for point clouds. Abaka Forge helps keep schemas versioned so the dataset remains consistent as requirements evolve.
What accuracy can you achieve for supervised learning labels?
We target high accuracy through multi-layer QA and calibrated adjudication, and for many tasks we can operate at 99% accuracy when guidelines are explicit and acceptance tests are well-defined. Accuracy depends on ambiguity, class balance, and input quality. We make quality measurable using gold sets, disagreement tracking, and targeted audits on high-impact classes. During a pilot, we identify where the spec needs tightening so accuracy is sustainable in production.
How do you handle security and compliance for sensitive datasets?
We support strict NDAs, segregated secure pipelines, and privacy-aware access controls. Abaka operates with SOC 2 and ISO 27001-aligned practices, and supports GDPR/CCPA requirements where applicable. We also maintain full IP provenance for collected data (0% copyright risk) and keep your data exclusively yours—never repurposed, resold, or shared. If you have special controls (VPC, restricted access, additional auditing), we scope them during onboarding.
Can you label multilingual supervised learning data?
Yes. Abaka supports multilingual labeling through global coverage across 50+ countries and language-capable annotators and reviewers. We can deliver consistent taxonomies across languages, handle locale-specific guidelines, and run QA that checks both semantic correctness and cultural/linguistic nuance. For multilingual programs, we often start with a calibration batch per language to confirm edge-case policy and to prevent “translation drift” where the same class is interpreted differently across locales.
How are you different from other data labeling companies?
Abaka is built for frontier AI teams that need both scale and trust. We combine vertically specialized annotators, scholar-grade adjudication for hard domains (math, coding, law, medicine), and an all-in-one platform (Abaka Forge) that supports versioning and QA visibility across modalities. We also never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. This reduces strategic risk for supervised learning programs.
What if we need guideline changes or taxonomy updates mid-project?
Change is expected in supervised learning. We manage updates with versioned guidelines, controlled rollouts, and targeted relabeling where needed. Abaka Forge keeps an audit trail of what changed, when, and why, so you can reproduce experiments and avoid silent dataset drift. We’ll typically run a small calibration batch on the new policy, confirm QA targets, then migrate production with clear cutover rules to keep your training and evaluation splits consistent.
Can we run a pilot before committing to a larger contract?
Yes—pilots are the fastest way to validate labeling specs, QA gates, and delivery formats. A typical pilot includes guideline creation or refinement, a defined batch size, gold-set calibration, and a measurable QA report. At the end, you’ll have usable supervised learning data plus a clear plan for scaling: throughput assumptions, reviewer ratios, and acceptance criteria. This reduces risk and makes full production pricing and timelines more predictable.
Who owns the labeled data and can you reuse it?
You own the labeled data. Abaka’s operating model is explicit: your data is exclusively yours and is never repurposed, resold, or shared. We also support full IP provenance for collected data to reduce copyright risk and ensure your team can document lineage. If you need specific contractual language or additional safeguards, we align during onboarding and execute under strict NDAs.
What tools do you use and can you integrate with our stack?
We use Abaka Forge as the core platform for collection, cleaning, annotation, and production workflows across modalities. We can deliver in formats that integrate with your training pipeline, data lake, and evaluation harness, and we can align schema checks with your internal validators. If you have existing tooling, we can adapt delivery to your preferred formats while keeping QA, versioning, and audit trails consistent within our workflow.
What is the minimum project size for supervised learning data work?
There isn’t a single minimum that fits every team, but most successful engagements start with a pilot batch that is large enough to expose edge cases and measure QA—often hundreds to thousands of items depending on modality. For very small datasets, we can still help if the work is high complexity (e.g., domain expert adjudication, guideline design, or audit). Share your target model and sample data, and we’ll recommend a right-sized starting scope.
