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Hire an AI data annotation service provider
that ships clean labels at scale

Abaka pairs 1M+ specialized annotators with Abaka Forge workflows to deliver 99% accuracy across text, image, video, and 3D—without slowing your model roadmap.

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When labeling becomes the critical path, your model roadmap slips even if compute and talent are ready. A single taxonomy mismatch can force weeks of rework, while inconsistent guidelines quietly reduce accuracy and inflate evaluation noise. Teams often lose 2–3 weeks per release cycle reconciling edge cases, fixing schema drift, and re-annotating “almost correct” data. The result is slower iteration, unreliable benchmarks, and higher costs—especially when each annotator’s throughput is capped at 500 files/day and quality gates are inconsistent.

Abaka operates as your AI data annotation service provider with a production mindset: clear specs, calibrated annotators, multi-layer QA, and secure pipelines that keep IP provenance intact. Using Abaka Forge, we standardize labeling playbooks, automate routine checks, and route complex cases to scholar-network reviewers (math, coding, medicine, law, languages). You get predictable delivery, audit-ready documentation, and datasets that train and evaluate cleanly—so your team can focus on modeling, not label triage.

The AI Data Annotation Service Provider Bottleneck

01

Quality Decay

Annotation quality typically declines over time without tight calibration, gold tasks, and dispute resolution. Small guideline ambiguities compound into inconsistent labels that can erase gains from better architectures. Abaka counters this with multi-stage QA, reviewer escalation, and structured adjudication—so you can sustain 99% accuracy targets instead of drifting. We cap throughput expectations (e.g., 500 files/day per annotator maximum) to prevent “speed-first” shortcuts and keep edge cases consistently handled across weeks of production.

02

Volume Walls

Most internal teams hit a volume ceiling fast: hiring, training, and ramping annotators is slow, while data demand spikes with each new model iteration. Even with strong ops, scaling from hundreds to tens of thousands of items per day can take weeks—especially for multimodal work like video and 3D. Abaka provides elastic capacity via 1M+ specialized annotators across 50+ countries, letting you increase throughput without sacrificing consistency or forcing your ML team into daily queue management.

03

Compliance Friction

As annotation programs grow, compliance becomes a throughput killer: NDAs, access control, provenance, and audit trails must be enforced across every step. One weak link can invalidate downstream training data and introduce legal risk. Abaka runs segregated secure pipelines aligned with SOC 2, ISO 27001, GDPR, and CCPA expectations, with strict NDAs and full IP provenance—so collected or labeled data carries 0% copyright risk. Your team stays fast without improvising governance every sprint.

01

Text labeling for search, support, and LLM training

Build reliable text datasets for LLM training and enterprise NLP. We deliver intent classification, entity tagging, sentiment, retrieval relevance judgments, and instruction-following datasets—run in Abaka Forge with calibrated guidelines and reviewer adjudication. Your team can standardize schemas across product lines (support, search, compliance) and ship consistent outputs in JSONL/CSV. For domain-heavy work, we route tasks to scholar-network specialists in law, medicine, business, science, and languages.

02

LLM RLHF pipelines for preference and safety outcomes

Scale RLHF with human preference rankings, pairwise comparisons, rubric-based grading, and safety-focused evaluations. Abaka Forge supports structured rubrics, gold-task calibration, and disagreement tracking so your reward model sees consistent signals. We cover reasoning, coding, and math—including Lean4—plus instruction following and creative writing. Outputs are delivered as JSONL with metadata, rater IDs (as permitted), and audit logs to support repeatable training runs and model governance.

03

Image annotation for detection, segmentation, and captioning

Produce high-quality image labels for perception and content understanding: bounding boxes, polygons, instance/semantic segmentation, keypoints, dense captioning, and image editing workflows. Abaka Forge enforces label taxonomy, validates geometry, and supports reviewer feedback loops for hard classes. We handle retail shelf imagery, healthcare imaging workflows (non-HIPAA claims avoided), security cameras, and manufacturing defects—delivering COCO-style JSON, masks, and CSV exports that plug into your training pipelines.

04

Video labeling for tracking, actions, and temporal events

Turn video into model-ready supervision with temporal segmentation, object tracking, action/event labeling, and multi-camera consistency checks. Abaka Forge helps manage frame sampling strategies, interpolation rules, and reviewer escalation when motion blur or occlusion creates ambiguity. Common outputs include per-frame boxes/masks, event intervals, and clip-level labels in JSON/CSV. This is ideal for robotics, retail analytics, and autonomous driving perception validation where edge cases dominate model error.

05

3D/4D point cloud annotation for perception stacks

Label 3D/4D point clouds with 3D cuboids, segmentation, track IDs, and scene-level attributes to support autonomy and robotics perception. Abaka Forge workflows include spatial consistency checks and reviewer tools for difficult geometries and sparse returns. We align taxonomies across datasets so training doesn’t break when sensors or sites change. Outputs are delivered in common 3D JSON schemas with associated metadata, enabling consistent training and evaluation for detection and tracking in dynamic scenes.

06

LiDAR + camera fusion labeling for sensor-aligned ground truth

Create sensor-fused ground truth by aligning LiDAR and camera labels with shared IDs, timing, and visibility rules. Abaka Forge supports synchronized review and conflict resolution across modalities, reducing misalignment that causes training instability. We deliver camera 2D boxes/masks alongside 3D cuboids and track-level metadata, suitable for automotive and robotics programs. This fusion approach helps your perception stack learn robustly across lighting changes, partial occlusions, and long-tail road scenarios.

07

Audio labeling for ASR, intent, and speech quality

Build audio datasets for speech and voice systems with transcription, diarization, intent tags, and quality scoring. Abaka Forge structures annotation with pronunciation guidance, timestamp policies, and reviewer spot checks to keep consistency high across languages. We support multilingual workflows across 50+ countries and deliver outputs as JSON/CSV, with time-aligned transcripts where required. This is ideal for contact centers, in-car voice, and assistant UX research that depends on clean labels.

08

Multi-layer QA, adjudication, and audit-ready reporting

Quality isn’t a final step—it’s a system. We implement multi-layer QA with gold tasks, inter-annotator agreement monitoring, and adjudication for ambiguous cases. Abaka Forge maintains task histories, reviewer notes, and versioned guidelines to prevent schema drift. Your team receives batch-level reports, error taxonomies, and remediation plans that reduce rework. This approach helps you keep datasets stable across iterations so training and evaluation results are attributable to model changes, not label noise.

Why Outsource AI Data Annotation Service Provider Work

01

Faster Delivery

Compress labeling timelines without cutting corners. Abaka can stand up calibrated teams quickly and deliver predictable throughput using Abaka Forge workflows and reviewer routing. Typical programs reach steady-state in 2–3 weeks with clear specs and QA gates, so your model experiments don’t stall behind ops work.

02

Direct Savings

Reduce the true cost of labeling—rework, tool sprawl, and internal coordination. With standardized playbooks, automation checks, and managed QA, you avoid repeated cycles of “fix and relabel.” Many teams also eliminate the overhead of recruiting and training specialized annotators for each new domain.

03

Risk Reduction

Operate with governance built in. Abaka supports strict NDAs, segregated secure pipelines, and compliance expectations aligned with SOC 2, ISO 27001, GDPR, and CCPA. Full IP provenance and 0% copyright risk on collected data help you ship datasets that withstand security and legal review.

04

Elastic Scalability

Scale up or down as your backlog changes—without rebuilding a workforce every quarter. With 1M+ specialized annotators across 50+ countries, you can expand volume for a launch and then shift capacity to evaluation or red-teaming work as priorities change.

05

Domain Expertise

Get the right humans for high-stakes labels. We route complex tasks to scholar-network domains—math, coding, medicine, law, languages, science, and business—so your guidelines are interpreted correctly and edge cases are resolved consistently, not guessed.

06

Innovation Velocity

Move beyond basic labeling into higher-value supervision like RLHF, reasoning datasets, and robust evaluation. Abaka Forge enables faster iteration with automation-assisted workflows and audit-friendly reporting, so your team spends more time improving models and less time coordinating annotation.

Industries We Serve

Automotive

Support ADAS and autonomy programs with lane, drivable area, signage, and multi-sensor perception labels. We handle video and LiDAR workflows with consistent taxonomies and reviewer escalation for rare road behaviors. For road-lane work, pricing can be structured per kilometer, helping you forecast spend while maintaining quality gates across long-tail scenarios.

GenAI / Foundation Models

Build instruction-following, reasoning, and safety-aligned datasets for frontier LLMs. We run RLHF preference ranking, rubric grading, and targeted evaluations across coding, math, and domain-heavy Q&A. Abaka Forge keeps rater calibration and guideline versions traceable so your reward model learns stable signals across iterations.

Embodied AI / Robotics

Train robots to perceive and act with multimodal supervision: video events, 3D scene attributes, and task success criteria. We support temporally consistent labels and review loops for occlusions, motion blur, and changing environments. For agent training, we can complement datasets with custom RL environment design for real-world capabilities.

Healthcare

Enable medical AI research workflows with careful taxonomy management, expert review routing, and privacy-aware pipelines. We support text labeling for clinical notes (when provided by you), imaging metadata tagging, and evaluation rubrics for assistant behavior—without making unsupported compliance claims. QA and provenance documentation help your team defend dataset integrity.

Retail

Improve product search, recommendations, and shelf intelligence with image and text annotation. We label attributes, categories, substitutions, and visual shelf elements, and we produce dense captions for richer multimodal models. Abaka Forge enforces consistent schemas across catalogs and geographies so training data doesn’t fragment by region or vendor.

Finance

Build trustworthy NLP and assistant datasets for classification, extraction, and evaluation. We label entities, risk categories, and policy-specific outcomes, and we support human evaluation for factuality and instruction adherence. Secure pipelines and strict NDAs help reduce operational risk when handling sensitive internal documents and annotations.

Geospatial

Create geospatial ground truth with imagery annotation, change detection labels, and structured metadata. We support polygon segmentation, object inventories, and cross-time consistency checks for monitoring and mapping workflows. Outputs integrate with analytics stacks via standard JSON/CSV exports and audit logs for traceability.

Security / Defense

Support perception and analysis pipelines with controlled-access annotation workflows and provenance tracking. We label imagery and video events, build evaluation sets for robustness and reliability, and maintain strict role-based access practices. Abaka’s compliance posture (SOC 2, ISO 27001, GDPR, CCPA) and segregated pipelines help meet security review expectations.

Agriculture / Industrial

Improve inspection and automation with defect detection, equipment tracking, and crop/field imagery labels. We handle edge cases like glare, dust, and seasonal variance through reviewer escalation and consistent guidelines. Abaka Forge ensures labeling stays stable across sites so models generalize as your data expands to new facilities and regions.

How It Works

1) Day 0–3 — Scope, schema, and acceptance tests

We align on your task definition, label taxonomy, edge-case policy, and output schema (JSONL/COCO/CSV, etc.). Together we define acceptance tests—precision targets, QA sampling, and dispute handling—so quality is measurable. Security onboarding includes NDAs, access controls, and secure data transfer setup.

2) Week 1–2 — Pilot batch and calibration

We run a pilot batch inside Abaka Forge using your guidelines, then measure error modes and disagreement. We refine the playbook, add gold tasks, and lock review thresholds. For domain-heavy work, we route tasks to scholar-network specialists (coding, math, medicine, law, languages) and document decisions for repeatability.

3) Week 2–3 — Production ramp with multi-layer QA

After calibration, we ramp volume while keeping QA gates stable: spot checks, double-pass review where needed, and adjudication for edge cases. Abaka Forge supports workflow automation to accelerate routine validations and reduce manual overhead. You receive batches with versioned guidelines and audit trails to simplify downstream training.

4) Ongoing — Continuous improvement and edge-case handling

As your model and product evolve, new edge cases appear. We maintain a living guideline, track error taxonomies, and update rubrics without breaking prior data. When schemas change, we plan migrations and backfills to keep datasets consistent across time, preventing evaluation drift and retraining surprises.

5) Weekly — Reporting, governance, and roadmap sync

Each week, we review throughput, QA metrics, and blocker categories with your team. You get clear reporting on defect types, reviewer notes, and suggested guideline updates. This cadence keeps data work aligned with model experiments, release deadlines, and compliance needs—without your engineers becoming full-time label managers.

Modality & Format Coverage

Your models rarely stay single-modality. Abaka supports end-to-end annotation across text, RLHF, vision, video, 3D, sensor fusion, and audio—delivered in formats that fit your training, evaluation, and governance pipelines.

ModalityAnnotation TypesToolsOutput Formats
TextNER & entity linking, intent/class labels, retrieval relevance judgments, instruction-following datasets, long-form reasoning QAAbaka ForgeJSONL, CSV, TSV, Parquet, UTF-8 TXT
LLM RLHFPairwise preference ranking, rubric grading, safety & policy compliance reviews, model-vs-model comparisons, structured critique writingAbaka ForgeJSONL, CSV, rubric scorecards, conversation transcripts, audit logs
ImageBounding boxes, polygons, instance/semantic segmentation, keypoints, dense captioningAbaka ForgeCOCO JSON, masks (PNG), JSON, CSV, YOLO TXT
VideoObject tracking, temporal event segmentation, action labels, multi-camera consistency, frame-level QA samplingAbaka ForgeJSON, CSV, per-frame annotations, clip manifests, timecode intervals
3D/4D Point Cloud3D cuboids, point-level segmentation, track IDs over time, scene attributes, occlusion/visibility tagsAbaka Forge3D JSON schemas, CSV metadata, sequence manifests, QA reports, versioned label sets
LiDAR + Camera fusion2D–3D alignment with shared IDs, synchronized tracking, cross-sensor adjudication, calibration checks, visibility rulesAbaka ForgeJSON, COCO-style 2D exports, 3D cuboid exports, sensor-sync manifests, CSV metadata
AudioTranscription, speaker diarization, intent tagging, keyword spotting labels, speech quality scoringAbaka ForgeJSON, CSV, time-aligned transcripts, RTTM-like diarization tables, WAV/segment manifests

Success Story

A frontier model lab scaling RLHF and multimodal evaluation
Challenge

The team’s training runs were bottlenecked by inconsistent preference labels and a growing backlog of “hard” prompts that required domain expertise. Different internal groups used slightly different rubrics, so reward-model training signals drifted over time. Meanwhile, evaluation sets were noisy—making it difficult to tell whether a new checkpoint actually improved factuality and instruction following. They needed a single AI data annotation service provider to standardize rubrics, scale volume safely, and keep audit trails for governance.

Approach

Abaka implemented a structured RLHF program in Abaka Forge: unified rubrics, calibrated raters, gold tasks, and adjudication for disagreement clusters. We routed specialized subsets to scholar-network reviewers (math, coding, science, law, languages) and documented edge-case decisions as versioned guidelines. In parallel, we built a repeatable evaluation workflow using human evaluation plus rubric scorecards, ensuring the lab could compare checkpoints without changing measurement standards midstream. Secure pipelines and strict NDAs supported controlled access and provenance tracking.

Results

Within the first 2–3 weeks, the team reached steady-state throughput with consistent scoring behavior and lower disagreement on critical categories. The reward model trained on cleaner preference signals, and the evaluation set became stable enough to detect meaningful deltas between checkpoints. Across weekly cycles, the lab reduced rework and improved label consistency while maintaining a 99% accuracy target through multi-layer QA and adjudication. The program delivered faster iteration, clearer go/no-go decisions, and governance-ready audit trails with measurable throughput improvements.

2–3 weeks
Time to calibrated production workflows
99%
Target accuracy with multi-layer QA
50+
Countries supporting multilingual scale

By the Numbers

2019
Founded — trustworthy data partner for frontier AI
1,000+
Enterprise and research customers served
1M+
Specialized annotators available on demand
99%
Accuracy target with multi-layer QA programs

What Customers Say

We came in expecting “more labelers.” What we got was a system: clear acceptance criteria, calibrated raters, and a workflow that kept edge cases from derailing the entire batch. Our training metrics stopped bouncing due to label noise, and we could finally attribute improvements to model changes.

Director of Applied MLEnterprise AI Platform Company

The biggest win was consistency. Abaka helped standardize our taxonomy across teams and kept an audit trail for every guideline change. That made our evaluation sets dependable and reduced the time we spent debating what a label “should” mean.

Head of Data OperationsFrontier Model Lab

Security review was straightforward: strict NDAs, segregated pipelines, and a clear story on data provenance. We were able to scale volume without opening new risk, and the weekly reporting made it easy to keep stakeholders aligned on progress and quality.

Security Program ManagerRegulated Technology Company

We had a mix of text, images, and video. Managing three vendors was painful. Consolidating into one provider with Abaka Forge improved our turnaround time and reduced rework. The escalation path for difficult samples was fast and the final exports matched our schemas cleanly.

Staff Machine Learning EngineerRobotics Company

Why Choose Abaka

01

A data partner built for frontier AI—not a generic labeling shop.

Abaka is a trustworthy data partner for frontier AI—founded in 2019, self-funded and profitable, with offices in Singapore, Paris, and Silicon Valley. We support teams that need scholar-grade judgment, secure pipelines, and reliable production delivery. We never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. Combined with Abaka Forge workflows, you get scalable annotation with governance and IP provenance built in.

02

99% accuracy programs

Multi-layer QA, calibrated guidelines, gold tasks, and adjudication keep label quality stable over time. You get consistent supervision that improves training and reduces evaluation noise.

03

1M+ specialized annotators

Elastic capacity across 50+ countries means you can ramp quickly for launches or new model cycles—without rebuilding an internal workforce or sacrificing consistency.

04

Abaka Forge operational control

Run collection, cleaning, annotation, and production workflows in one platform. Automation-assisted checks accelerate routine validation, while reviewer tooling handles edge cases without losing auditability.

05

Compliance and provenance by default

SOC 2 and ISO 27001-aligned controls plus GDPR and CCPA expectations support enterprise governance. Strict NDAs, segregated pipelines, and full IP provenance keep your dataset defensible.

06

We never compete with your models

Your data stays yours—exclusively. Abaka does not repurpose, resell, or share customer data, and we avoid conflicts that can arise when a vendor is also training competing models. This trust posture removes friction in security reviews and enables deeper collaboration on high-value RLHF and evaluation work.

Frequently Asked Questions

How much does an AI data annotation service provider cost?
Pricing depends on modality, complexity, and required expertise, but we can anchor budgets with real unit rates. For example: LLM Math/Coding annotation can be $18/hr, STEM Generalist work $12/hr, Dense Captioning $6/hr, Image Editing $8/hr, and Road Lane labeling $3/km. For evaluation, Red Teaming can be $8/eval and Defensive Coding $15/eval. We’ll scope your taxonomy, QA level, and weekly throughput, then provide a transparent estimate tied to acceptance tests and delivery milestones.
How long does it take to start and deliver the first batch?
Most teams can expect a fast kickoff followed by a calibration phase. We typically complete scoping and acceptance tests in Day 0–3, then run a pilot batch and calibration during Week 1–2. Production ramp often starts in Week 2–3 once guidelines, gold tasks, and adjudication rules are stable. Exact timing depends on modality (text vs video vs 3D), the number of classes, and edge-case density, but we plan for predictable, repeatable weekly delivery.
What data types and output formats do you support?
We support text, LLM RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion, and audio. Outputs are delivered in practical formats for training pipelines—commonly JSONL, CSV/TSV, COCO-style JSON for vision, masks (PNG) for segmentation, timecoded interval exports for video, and 3D JSON schemas with sequence manifests for point clouds. We align the export schema to your ingestion requirements and keep versioned documentation so future batches remain compatible.
How do you ensure annotation accuracy and consistency?
We treat accuracy as a process, not a promise. Programs use calibrated guidelines, training tasks, gold samples, and ongoing spot checks. For difficult or ambiguous items, we escalate to reviewers and run adjudication to resolve disagreements with documented decisions. We also track error categories so we can improve guidance instead of repeatedly “fixing labels.” This reduces drift across weeks of production and supports sustained 99% accuracy targets for teams that need dependable training and evaluation data.
Can you meet enterprise security requirements for sensitive data?
Yes—security is built into how we run programs. We operate with strict NDAs, segregated secure pipelines, and access controls designed for enterprise review. Our compliance posture aligns with SOC 2 and ISO 27001 expectations and supports GDPR and CCPA requirements. We also maintain full IP provenance so you can prove where data came from and how it was handled. If your team requires specific workflow constraints (e.g., restricted roles, isolated projects), we scope them during onboarding.
Do you support multilingual annotation and global coverage?
Yes. Abaka supports multilingual programs through a global workforce across 50+ countries, with language-specialized annotators and reviewers. We can run language-specific guidelines, localized taxonomies, and region-aware edge-case policies. For multilingual datasets, we recommend calibration per language and periodic cross-language QA sampling to maintain consistency. Deliverables include language tags, rater metadata (as permitted), and standardized schemas so your training pipeline can merge multilingual batches without breaking evaluation.
How is Abaka different from other data labeling companies?
Three differences matter for frontier AI teams. First, we combine scalable human operations (1M+ specialized annotators) with Abaka Forge workflows that support automation-assisted checks and audit-ready reporting. Second, we emphasize scholar-network expertise for complex tasks like coding, math, and domain Q&A—not just generic labeling. Third, we never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared—making deeper collaboration and security reviews materially easier.
What if we need to change the taxonomy or guidelines mid-project?
Change requests are normal—models evolve and so do labels. We handle updates by versioning guidelines, defining what changes apply to new batches vs backfills, and validating schema compatibility with your ingestion pipeline. When needed, we can run controlled migrations: sample audits, targeted re-labeling, and backfill plans that keep your dataset coherent across time. Weekly reporting includes emerging edge cases and recommended guideline improvements so changes are proactive rather than disruptive.
Can we start with a pilot before committing to a large contract?
Yes. A pilot is the best way to validate quality, throughput, and workflow fit. We typically start with a defined sample size and clear acceptance tests—accuracy targets, QA sampling, and escalation rules. The pilot phase is where we calibrate annotators, refine rubrics, and confirm your output formats. If the pilot meets criteria, we scale into production with minimal transition friction, using the same tooling, guideline versions, and reporting structure.
Who owns the annotated data and can it be reused?
You own your data and your resulting annotations. Abaka does not repurpose, resell, or share customer data, and we do not build models that compete with you. We maintain secure, segregated pipelines and full IP provenance to preserve ownership clarity and reduce legal risk. If you need specific contractual language around exclusivity, retention, deletion timelines, or audit access, we align those requirements during onboarding and ensure the workflow supports them operationally.
What tools do you use for annotation and QA?
We use Abaka Forge—an all-in-one platform for collection, cleaning, annotation, and production workflows across data types (text, RLHF, image, video, and 3D/4D point cloud). Forge supports structured rubrics, reviewer routing, QA sampling, and audit trails. For teams with existing pipelines, we align exports to your schema and can integrate with your storage and workflow requirements. The goal is to reduce tool sprawl while keeping transparency and control over quality.
What is the minimum project size you can support?
We support both targeted pilots and large-scale production. Minimum size depends on modality and setup needs: simpler text classification can start small, while video, 3D, and fusion projects benefit from enough volume to calibrate guidelines and measure agreement reliably. Even for small starts, we still define acceptance tests, QA rules, and output schemas so the work is production-grade. If you share your target modalities, volume, and timeline, we’ll recommend a right-sized pilot that proves value quickly.

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Label the Present. Train the Future.

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