How much does an AI data annotation firm cost per hour or per task?
Pricing depends on modality, rubric difficulty, and reviewer depth. As concrete reference points, Abaka offers LLM math/coding annotation at $18/hr, STEM generalist work at $12/hr, dense captioning at $6/hr, and road lane annotation at $3/km. For evaluation, examples include red teaming at $8/eval and defensive coding at $15/eval. We typically scope a pilot first, then convert learnings into a weekly throughput and budget plan that matches your quality targets.
How long does it take to start a project with an AI data annotation firm?
Most teams can begin with a structured pilot in 1–2 weeks, depending on security review, data access setup, and rubric complexity. Day 0–3 is usually scoping and success metrics; Week 1–2 focuses on guidelines, calibration, and a pilot batch. If the rubric is stable, production can ramp in Week 2–3 with weekly deliveries. Timing is faster when you can provide representative samples, edge cases, and clear acceptance criteria up front.
What data types and formats can you annotate and deliver?
Abaka supports text, RLHF, image, video, 3D/4D point clouds, LiDAR + camera fusion, and audio. We deliver outputs in practical training-ready formats such as JSONL, JSON, CSV/TSV, COCO-style JSON for vision, XML where needed, time-stamped subtitles (SRT/VTT) for audio/video, and structured evaluation reports for RLHF and human evaluation. If your pipeline needs a specific schema, we can align exports to your required field names and versioning conventions.
What accuracy can I expect from your annotation team?
Abaka targets high accuracy through process control: calibration rounds, gold tasks, adjudication, and multi-layer QA. For many programs, teams aim for 99% accuracy on audited samples, but the right target depends on your use case and tolerance for noise. We work with you to define what “accuracy” means (e.g., per-class precision, boundary quality, rubric adherence), then instrument QA to measure it continuously rather than relying on one-time spot checks.
How do you keep my data secure when outsourcing annotation?
We operate with enterprise compliance in mind: SOC 2 and ISO 27001-aligned controls, GDPR and CCPA support, strict NDAs, and segregated secure pipelines. Access is role-based and least-privilege, and workflows are auditable with logs and controlled exports. We also provide full IP provenance and do not repurpose, resell, or share your data. If you have additional security requirements, we scope them during onboarding and implement controls before production begins.
Do you support multilingual data annotation and global coverage?
Yes. Abaka supports multilingual annotation with coverage across 50+ countries, enabling region-specific language expertise and cultural context. We handle translation review, multilingual instruction data, and language-specific QA where direct fluency is required. For multilingual programs, we typically set up language-specific rubrics and reviewer pools, then track per-language error modes so you can see where guidelines need localization rather than assuming one global standard will work.
How are you different from other AI data annotation companies?
Three differences tend to matter most: (1) quality mechanics — calibration, gold sets, adjudication, and multi-layer QA rather than ad-hoc spot checks; (2) domain depth — scholar-network reviewers for math, coding, law, medicine, languages, and more; and (3) trust and governance — SOC 2/ISO 27001-aligned operations, segregated pipelines, full provenance, and a strict policy that we never build models that compete with you. Your data stays exclusively yours.
Can we change guidelines or request revisions after labeling starts?
Yes — change requests are normal as your model and taxonomy evolve. Abaka uses versioned guidelines and structured change control so updates don’t silently create dataset drift. We assess what changes mean for already-labeled data, estimate rework scope, and implement a plan that preserves comparability across training runs. Weekly reporting helps surface where a rubric needs refinement, so you can adjust early rather than discovering inconsistencies after the next model iteration.
Can we run a paid pilot before committing to a long-term contract?
Yes. Many teams start with a paid pilot to validate rubric clarity, throughput, and integration into their training stack. A pilot typically includes guideline drafting, calibration rounds, a first production batch, and a QA report that quantifies error themes and agreement. If the pilot meets success metrics, we transition to steady weekly deliveries with a defined staffing plan, review depth, and export schema. This reduces risk and prevents surprises at scale.
Who owns the labeled data and can it be reused by the vendor?
You own your data and the resulting labeled outputs. Abaka does not repurpose, resell, or share customer data, and we do not build models that compete with you. We maintain provenance and audit trails so you can demonstrate ownership and traceability. If you need specific contractual language around exclusivity, retention, and deletion, we align during onboarding and enforce those requirements through segregated pipelines and controlled access.
What tools do you use for annotation and project management?
Abaka runs projects on Abaka Forge — our all-in-one platform for collection, cleaning, annotation, and production workflows across text, RLHF, image, video, and 3D/4D. The platform supports role-based access, audit logs, QA workflows, and export pipelines aligned to common ML formats like JSONL, COCO JSON, and CSV. Large-model automation can accelerate repetitive steps, while humans focus on ambiguity, edge cases, and rubric adherence.
What is the minimum dataset size where outsourcing annotation makes sense?
Outsourcing can be valuable even for small pilots (a few thousand items) when tasks are complex or you need domain reviewers and rigorous QA. For production programs, teams often see the biggest benefit when they need consistent weekly throughput and reliable change control across iterations. Abaka can tailor staffing and review depth to your scope, whether you’re validating a new taxonomy, building an eval set, or scaling to large multi-modal datasets. The best starting point is a scoped pilot with clear success metrics.
