How much does it cost to outsource text annotation?
Pricing depends on task complexity, rubric strictness, and reviewer requirements. For example, LLM Math/Coding annotation can be $18/hr, while STEM Generalist work can be $12/hr. If you need adjacent tasks like dense captioning, that can be $6/hr. We typically propose a pilot first to validate guidelines, QA gates, and export schemas, then scale with a predictable run rate. Talk to an Expert and we’ll quote based on your label map, languages, and weekly volume targets.
How long does it take to start and deliver the first batch?
Most teams can start with a secure setup and scoped rubric in Day 0–3, then receive pilot outputs in Week 1–2. After calibration, production delivery commonly stabilizes in Week 2–3. Timing varies based on how mature your guidelines are, how many languages you need, and how complex the task is (e.g., reasoning QA and RLHF require more calibration than simple classification). We use versioned rubrics and acceptance gates so later iterations stay fast even as requirements evolve.
What text formats can you annotate and export?
We support common inputs such as raw text, JSON logs, chat transcripts, documents extracted to text, and prompt/response bundles for LLM training and evaluation. Outputs are delivered in pipeline-friendly formats including JSONL, CSV/TSV, and schema-specific structures such as span indices for NER or instruction templates for supervised fine-tuning. If you have a custom schema, we can align to it and validate exports before production. Versioning ensures that schema changes don’t silently break training jobs.
How do you ensure annotation accuracy and consistency?
We combine calibrated annotators, multi-layer QA, and adjudication workflows to keep decision boundaries stable. Programs typically include guideline versioning, gold sets, sampling plans, and escalation to senior reviewers for edge cases. For complex domains, we use scholar-network reviewers so labels reflect real expertise rather than guesswork. We also cap throughput at 500 files/day per annotator to reduce fatigue-driven errors. Your team can audit examples, reviewer notes, and acceptance outcomes inside Abaka Forge.
Is outsourcing text annotation secure for sensitive data?
Yes—security is designed into operations. Abaka supports SOC 2 and ISO 27001-aligned controls, strict NDAs, and segregated secure pipelines. We can restrict access by project, role, and data sensitivity, and we maintain audit trails to support governance reviews. Importantly, Abaka never builds models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. We also provide full IP provenance with 0% copyright risk on collected data.
Can you handle multilingual text annotation at scale?
Yes. Abaka operates across 50+ countries and can staff language-native annotators and reviewers for multilingual programs. We support cross-lingual label mapping so the same ontology remains consistent across locales, and we run calibration to avoid cultural or idiomatic mismatches that can distort sentiment, safety, or intent. You can also run locale-specific rubrics when policy or terminology differs by region. Deliverables can include language IDs, normalized text fields, and consistent exports per language.
How is Abaka different from other data labeling companies?
Two differences matter most for text annotation: trust and controllability. Abaka never builds models that compete with you, and your data is never repurposed, resold, or shared. Operationally, Abaka Forge provides audit logs, guideline versioning, and QA visibility that helps your team debug label noise quickly. We also bring scholar-network expertise for high-stakes domains and reasoning-heavy tasks where generic workforces often fail. This combination reduces relabel churn and speeds up model iteration.
What if we need to change guidelines or labels mid-project?
Change requests are normal—especially for LLM products where prompts and policies evolve. We manage changes with versioned rubrics and controlled rollouts so updates don’t corrupt existing datasets. When needed, we isolate affected slices and relabel only what’s impacted, preserving prior work where possible. We also document what changed, why it changed, and how acceptance gates were updated, so your team can compare model performance across dataset versions with confidence rather than guessing.
Can we run a pilot before committing to a large contract?
Yes. A pilot is the fastest way to validate rubric clarity, QA gates, and export compatibility. We typically run a focused slice that includes both common cases and edge cases, then review outcomes with your team: disagreement reasons, adjudication patterns, and any needed guideline edits. Once the pilot meets acceptance criteria, we scale production with calibrated annotators and a stable delivery cadence. Pilots are especially useful for RLHF, complex NER schemas, and reasoning-heavy QA.
Who owns the labeled data and can you reuse it?
You own your data and outputs. Abaka’s policy is that your data is exclusively yours—never repurposed, resold, or shared. We operate under strict NDAs and segregated secure pipelines, and we maintain full IP provenance practices so you can track sources and reduce risk. If you need special contractual language around ownership, retention, or deletion, we can align to your governance requirements as part of onboarding and security review.
What tools do you use for managing text annotation projects?
Work runs in Abaka Forge, our all-in-one platform for collection, cleaning, annotation, and production workflows. Forge supports text, RLHF, image, video, and 3D/4D data types, with automation to accelerate throughput while keeping an audit trail. For text programs, Forge enables guideline versioning, task routing, reviewer escalation, gold sets, and export validation. Your team can review samples, track QA outcomes, and manage changes without losing visibility as volume scales.
What is the minimum dataset size you can support?
We support everything from small pilots to ongoing production. A practical minimum is enough volume to calibrate guidelines and measure quality—often a few hundred to a few thousand items, depending on task complexity and label cardinality. For NER with many entity types or RLHF with nuanced rubrics, starting with a structured pilot helps ensure the schema is stable before scaling. If you only have a small dataset, we can focus on expert review and high-signal labeling rather than throughput.