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Scale reliable training data with an
ML Data Labeling Agency you can trust

Abaka delivers QA-backed, vertically specialized annotation across text, image, video, and 3D—so your team ships models faster without trading off accuracy, security, or provenance.

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When labeling becomes the bottleneck, your entire ML roadmap slows down. Model iterations slip by weeks because datasets arrive late, labels disagree across annotators, and edge cases never make it into the queue. The result is avoidable retraining cost—teams often spend 30–50% of their time debugging data instead of improving models. Worse, inconsistent labeling can inflate offline metrics while production performance stalls, forcing expensive re-collections and re-annotations. If you’re operating across multiple regions, privacy and IP provenance gaps also create compliance risk that blocks deployment.

Abaka is a trustworthy data partner for frontier AI—built to help you scale labeling without sacrificing quality or control. We combine vertically specialized annotators, multi-layer QA, and secure, segregated pipelines to deliver consistent labels across modalities. Your team gets clear acceptance criteria, adjudication workflows, and reproducible guidelines so each iteration improves the dataset—not just the model. With Abaka Forge, you can standardize tasks, audits, and exports to your training stack, while keeping your data exclusively yours—never repurposed, resold, or shared.

The ML Data Labeling Agency Bottleneck

01

Quality Decay

Label quality drifts when guidelines evolve but feedback loops don’t. A 1–2% increase in systematic label error can cascade into larger performance drops on rare classes, especially in safety-critical tasks. Teams then spend weeks re-auditing, re-labeling, and re-running training jobs to isolate the source of regression. Abaka prevents quality decay with calibrated rubrics, adjudication, and multi-layer QA—so every batch is measured against acceptance thresholds before it reaches your training pipeline.

02

Volume Walls

Even strong in-house labeling programs hit throughput limits. When one annotator can reliably process only a bounded daily volume (often capped around 500 files/day for high-accuracy work), adding more tasks increases coordination overhead, not speed. That’s how a “small backlog” turns into a multi-week delay. Abaka scales with 1M+ specialized annotators across 50+ countries, while keeping throughput predictable via standardized task design, sampling plans, and batch-level QA gates.

03

Compliance Friction

Data labeling is where privacy, IP, and access control issues surface—often late. If you discover missing provenance, unclear rights, or weak vendor controls after work starts, you lose weeks renegotiating scope and reprocessing datasets. Abaka reduces compliance friction with SOC 2 and ISO 27001-aligned practices, GDPR/CCPA readiness, strict NDAs, segregated secure pipelines, and full IP provenance—targeting 0% copyright risk on collected data and clean audit trails for your reviewers.

01

Task scoping, rubrics, and acceptance criteria

We translate your model objectives into labeling specs that annotators can execute consistently—definitions, edge cases, gold sets, and measurable acceptance thresholds. Your team gets rubric-driven guidelines for tasks like entity extraction, classification, segmentation, and instruction following. We support common ML stacks and workflows (batching, sampling, error taxonomy, adjudication) so you can compare datasets across iterations and reduce rework when you change class definitions or add new labels.

02

Multi-layer quality assurance and adjudication

Abaka runs multi-layer QA with reviewer escalation and adjudication to resolve ambiguity—especially on long-tail and safety-critical examples. We calibrate annotators against gold data, run spot checks and targeted audits, and maintain a feedback loop so guideline updates propagate quickly. This approach is designed to reach high accuracy targets (up to 99% on suitable tasks) while preserving traceability—so your team can review disagreements, track root causes, and prevent regressions.

03

Text labeling for production and frontier training

We label and structure text for classification, NER, retrieval, summarization, and domain corpora cleaning. For GenAI, we support instruction following datasets, reasoning-focused tasks, and scholar-network domains like medicine, law, mathematics, and coding. Deliverables include cleaned corpora, taxonomy-aligned labels, and evaluation-ready sets. Outputs can be delivered in JSONL/CSV formats suitable for training and evaluation pipelines.

04

LLM RLHF: preferences, ranking, and safety audits

We run RLHF pipelines including preference ranking, pairwise comparisons, rubric-based grading, and safety/bias audits aligned to your policies. For model evaluation and red-teaming, we can execute objective benchmarks, model-as-judge workflows, and human evaluation with clear scoring guides. Use cases include instruction following, tool/function calling, hallucination checks, and defensive coding evaluation. Work is orchestrated through Abaka Forge to standardize tasks and audit outcomes.

05

Image annotation for detection, segmentation, and OCR

We deliver bounding boxes, polygons, instance segmentation, keypoints, and OCR/transcription for retail, healthcare imaging workflows, security, and industrial inspection. We also support dense captioning and image editing tasks when you need curated training sets for multimodal models. Export formats include COCO JSON, Pascal VOC XML, and YOLO TXT—mapped to your class taxonomy and naming conventions to keep training reproducible across versions.

06

Video labeling for tracking and temporal events

For video, we label objects across frames, events over time, and scenario tags for tasks such as surveillance analytics, retail behavior understanding, and autonomous systems validation. We support temporal segmentation, multi-object tracking-style labeling (without claiming any specific dataset compatibility), and frame-level attributes. Deliverables include per-frame annotations and time-coded segments exported to JSON, CSV, and CVAT-compatible formats—built for scalable QA and sampling.

07

3D/4D point cloud labeling for robotics and AV

We annotate 3D/4D point clouds with cuboids, segmentation, and attributes for embodied AI, robotics navigation, and industrial automation. Teams can capture consistent labels across sequences, handle occlusions, and align taxonomies with your planner and perception stack. Outputs can be delivered as JSON annotations alongside point cloud formats such as PCD/PLY, with review bundles that include QA reports and disagreement logs for rapid iteration.

08

Abaka Forge workflows, audits, and export control

Abaka Forge is our all-in-one platform for collection, cleaning, annotation, training support, and production workflows—covering text, image, video, 3D/4D point cloud, and RLHF. You get standardized task templates, reviewer queues, audit trails, and controlled exports to your storage and ML stack. Forge uses large-model automation to accelerate routine steps (up to 50x faster where applicable), while keeping humans in the loop for quality-critical decisions.

Why Outsource ML Data Labeling Agency Work

01

Faster Delivery

Move from kickoff to first usable batch quickly with pre-built task design patterns, calibrated rubrics, and parallelized teams. Many teams validate scope in a 2–3 week pilot, then ramp without rebuilding processes. You keep momentum on model iterations while we handle execution details—queues, QA, and adjudication.

02

Direct Savings

Outsourcing reduces hidden costs—recruiting, training, tooling, and management time—while giving you predictable unit economics. Instead of interrupting engineers to label and debug, you convert labeling into a managed pipeline with clear acceptance criteria, measurable QA, and controllable throughput based on your release plan.

03

Risk Reduction

Labeling vendors can become a risk if security and IP provenance are unclear. Abaka operates with SOC 2 and ISO 27001-aligned controls, strict NDAs, and segregated secure pipelines. Your data is exclusively yours—never repurposed, resold, or shared—reducing compliance and IP risk during audits.

04

Elastic Scalability

Scale up for launches, backfills, and edge-case pushes—then scale down without organizational whiplash. With 1M+ annotators across 50+ countries, we can expand coverage for new geographies, languages, and corner cases while maintaining consistent guidelines and QA gates for every batch.

05

Domain Expertise

General labeling fails on expert domains. Abaka can route work to vertically specialized talent and scholar-network reviewers across medicine, law, mathematics, coding, and science. That means fewer ambiguous labels, better handling of edge cases, and datasets that match the reality of your production environment.

06

Innovation Velocity

When labeling is stable, your team can iterate on model architecture, evaluation, and deployment. We help you operationalize dataset versioning, error taxonomies, and acceptance tests so improvements compound. With Abaka Forge, you can also automate repetitive steps and focus humans on judgment-heavy tasks.

Industries We Serve

Automotive

Support perception and mapping programs with consistent image, video, and LiDAR labeling—lanes, drivable areas, signs, and scenario tags. We also help you build QA loops that catch long-tail failures and reduce rework across dataset versions. For lane work, pricing can be structured per-kilometer when appropriate.

GenAI / Foundation Models

Build instruction-following, reasoning, and preference datasets for LLM training and evaluation. We support RLHF-style rankings, rubric scoring, safety/bias audits, and domain expert reviews (math, coding, law, medicine). Deliverables are evaluation-ready JSONL with traceable guidelines and adjudication notes.

Embodied AI / Robotics

Train embodied agents with datasets that reflect real tasks—object/scene labeling, 3D point cloud annotation, and action/state tagging for imitation learning and evaluation. We can also support RL environment design needs and provide structured annotations that map cleanly to policy training pipelines.

Healthcare

Enable medical AI workflows with careful QA, reviewer escalation, and privacy-aware processes. Common projects include text de-identification labeling, imaging annotation (when provided), and clinical document classification. We emphasize traceability, consistent rubrics, and secure access controls to support internal reviews and compliance needs.

Retail

Improve search, recommendations, and inventory visibility with product taxonomy labeling, attribute extraction, OCR, and shelf image annotation. For multimodal retail assistants, we deliver paired image-text labels and preference data to improve response quality. Exports map to your taxonomy versions to avoid churn.

Finance

Support document AI and risk workflows with high-precision labeling for entity extraction, table understanding, and classification. For GenAI in finance, we help with rubric-based evaluation for factuality and policy compliance, plus red-team style prompts where needed—keeping clear audit trails for reviewers.

Geospatial

Label satellite and aerial imagery for land-use classification, feature extraction, change detection, and asset mapping. We can combine polygon-based annotation with attribute tagging and produce COCO-style JSON or GIS-friendly exports (GeoJSON where appropriate). QA is designed to reduce boundary inconsistency across annotators.

Security / Defense

Build robust perception and monitoring datasets with strict access controls and segregated pipelines. Projects often include video event tagging, object detection, and multimodal evaluation for decision-support systems. We prioritize traceability, reviewer escalation, and secure collaboration patterns aligned with NDA-driven engagements.

Agriculture / Industrial

Improve inspection and yield workflows with image/video labeling for defects, crop health indicators, equipment states, and safety conditions. For robotics in industrial sites, we support 3D labeling and scenario tags. Outputs integrate with your MLOps stack and include QA reports to reduce costly relabel cycles.

How It Works

1) Day 0–3 — Scope, security, and success criteria

We align on your model goal, taxonomy, edge cases, and acceptance criteria (accuracy targets, sampling plan, and error taxonomy). Security requirements—NDAs, access controls, segregated pipelines—are confirmed up front. You provide seed data and examples; we return a task spec and pilot plan your team can approve.

2) Week 1–2 — Pilot labeling with calibrated QA

We run a pilot batch to validate guidelines, ambiguity handling, and throughput. Annotators are calibrated using gold examples; reviewers adjudicate disagreements and log failure modes. You receive pilot outputs plus a QA report that shows what’s stable, what needs rubric updates, and how we’ll scale without quality drift.

3) Week 2–3 — Scale-up and workflow hardening

After pilot acceptance, we ramp volume while keeping the same rubric, reviewer layers, and audit strategy. We standardize exports to your preferred formats and integrate handoff checkpoints into your training schedule. If you’re running multimodal work, we coordinate consistent ontology and naming across text, vision, and 3D.

4) Ongoing — Batch delivery, audits, and continuous improvement

We deliver labeled batches on a predictable cadence with built-in audits, targeted rework queues, and clear change logs. When your model reveals new edge cases, we incorporate them into guidelines and gold sets. The goal is compounding dataset quality—so every iteration reduces the time your team spends debugging data.

5) Weekly — Reporting, cost controls, and roadmap alignment

Each week, you get operational metrics: throughput, QA scores, disagreement patterns, and top error categories. We review your upcoming releases and adjust staffing to match demand. If you need cost controls, we can prioritize high-impact classes, adopt sampling strategies, and align annotation depth to model maturity.

Modality & Format Coverage

Your models rarely rely on one modality. Abaka supports end-to-end labeling across text, RLHF, vision, video, 3D, and audio—with consistent rubrics, QA, and exports your training pipeline can rely on.

ModalityAnnotation TypesToolsOutput Formats
TextClassification, NER/entity linking, summarization grading, data cleaning & normalization, taxonomy mappingAbaka ForgeJSONL, CSV, TSV, Parquet, UTF-8 text bundles
LLM RLHFPairwise preference ranking, rubric-based scoring, safety/bias audits, tool/function-call evaluation, human evaluationAbaka ForgeJSONL, rubric score tables (CSV), conversation trees (JSON), eval reports (CSV/PDF)
ImageBounding boxes, polygons, instance segmentation, keypoints, OCR/transcription, dense captioningAbaka ForgeCOCO JSON, Pascal VOC XML, YOLO TXT, CVAT XML, PNG/JPEG masks
VideoTemporal segmentation, frame-level labels, object tracks across frames, event tagging, scenario attributesAbaka ForgeJSON, CSV timecodes, CVAT exports, per-frame annotation bundles, MP4 sidecars
3D/4D Point Cloud3D cuboids, point-level segmentation, sequence annotation, object attributes, occlusion/truncation flagsAbaka ForgeJSON annotations, PCD/PLY sidecars, sequence manifests (JSON), QA logs (CSV)
LiDAR + Camera fusionCross-sensor association, synchronized labeling, 2D–3D correspondence checks, lane/drivable area tagging, scenario metadataAbaka ForgeJSON, sensor-sync manifests, per-sensor exports, QA discrepancy reports (CSV)
AudioTranscription, speaker diarization tags, intent labeling, sentiment/emotion tags, keyword spotting labelsAbaka ForgeJSONL, CSV, TextGrid, SRT/VTT, WAV sidecars with timestamps

Success Story

A leading GenAI / Foundation Models AI team
Challenge

The team needed a reliable ML data labeling agency to expand training and evaluation datasets across multiple domains while keeping strict quality controls. Their internal reviewers were overwhelmed by disagreement resolution, and new edge cases were taking too long to turn into consistent guidelines. They also required clear provenance and security controls so data could be shared with only approved contributors. The immediate goal was to stabilize dataset quality, reduce relabel cycles, and create a repeatable process for weekly model iteration.

Approach

Abaka designed a rubric-driven pipeline with multi-layer QA, gold calibration, and adjudication for ambiguous examples. We staffed domain-appropriate annotators (including scholar-network reviewers where needed), then executed a pilot batch to validate the taxonomy, measure disagreement, and refine edge case handling. Using Abaka Forge, we standardized task templates, reviewer queues, and export formats so the customer could plug outputs into their training and evaluation workflow. We also implemented audit trails and controlled access patterns aligned with strict NDAs.

Results

Within 3 weeks, the team moved from inconsistent outputs to stable batch delivery with repeatable QA gates. Disagreement hot spots were converted into rubric updates and gold examples, cutting rework loops and making weekly iteration predictable. The customer increased labeled volume without sacrificing review quality and maintained a clear audit trail for every decision. Outcomes included 99% accuracy targets on suitable tasks, faster acceptance of new label types, and steady weekly batch delivery that supported continuous model releases.

2–3 weeks
Pilot-to-scale timeline for initial workflows
99%
Accuracy targets supported with multi-layer QA
50+
Countries available for language and locale coverage

By the Numbers

2019
Founded — trustworthy data partner for frontier AI
1,000+
Enterprise & research customers
1M+
Vertically specialized annotators
50+
Countries supported for delivery

What Customers Say

We came in with a messy taxonomy and inconsistent labels from multiple sources. Abaka helped us rewrite the rubric, set acceptance criteria, and stand up a QA process that our engineers could trust. The difference was not just higher quality—it was predictability. We could plan model iterations around weekly deliveries and spend far less time debugging data.

Director of Applied MLEnterprise AI Platform Company

Their adjudication workflow is what changed everything for us. When annotators disagree, we don’t get a random outcome—we get a traceable decision, a guideline update, and better future batches. That closed the loop between training failures and labeling improvements. It felt like a real extension of our team rather than a black-box vendor.

Head of Data OperationsGenAI Product Company

Security and provenance were non-negotiable. Abaka’s segregated pipelines and audit trails made it straightforward to collaborate with our internal reviewers without expanding access broadly. The team was disciplined about scope, documentation, and exports. We were able to scale volume while keeping our internal compliance stakeholders comfortable.

ML Engineering ManagerRegulated Industry Technology Company

We needed multimodal labeling—text plus vision—and the hard part was consistency across formats and versions. Abaka delivered clean exports, stable naming conventions, and QA reporting we could actually use. The weekly check-ins focused on throughput, error trends, and what to change next. That made the dataset improve every sprint.

Senior Research ScientistApplied Research Lab

Why Choose Abaka

01

A trustworthy ML data labeling agency that never competes with you

Abaka is built for teams that care about control, provenance, and long-term partnership. We never build models that compete with you—your data is exclusively yours and is never repurposed, resold, or shared. With SOC 2 and ISO 27001-aligned practices, GDPR/CCPA readiness, strict NDAs, and segregated secure pipelines, your labeling program can scale without opening compliance or IP risks. You get repeatable rubrics, QA gates, and audit trails that make each dataset iteration more reliable.

02

99% accuracy targets with multi-layer QA

Reach high accuracy targets on suitable tasks using calibrated gold sets, reviewer escalation, and adjudication. We track disagreement patterns and turn them into rubric updates so quality improves over time instead of drifting.

03

Vertically specialized annotators at global scale

Access 1M+ annotators across 50+ countries, with options for scholar-network expertise in medicine, law, mathematics, coding, and science. This helps your team handle edge cases and domain nuance without stalling throughput.

04

Abaka Forge for standardized workflows and exports

Run labeling through Abaka Forge to standardize task templates, audits, reviewer queues, and exports across text, RLHF, vision, video, and 3D. Large-model automation accelerates repetitive steps while humans handle judgment-critical decisions.

05

Compliance-first operations with clear IP provenance

Operate with SOC 2 and ISO 27001-aligned controls, GDPR/CCPA readiness, and strict NDAs. We maintain clear audit trails and full IP provenance and aim for 0% copyright risk on collected data—so approvals don’t become your bottleneck.

06

From pilot to production without re-architecting your process

Start with a focused pilot to prove rubric clarity and QA performance, then scale volume without changing the underlying workflow. Weekly reporting keeps your team aligned on throughput, error trends, and upcoming needs, so labeling stays synchronized with model iteration and release cadence.

Frequently Asked Questions

How much does an ML data labeling agency cost?
Pricing depends on modality, complexity, and QA depth, but Abaka can align to real, concrete unit rates. Examples include LLM Math/Coding work at $18/hr, STEM generalist labeling at $12/hr, dense captioning at $6/hr, and road lane annotation at $3/km. Your final rate depends on guideline ambiguity, reviewer escalation requirements, and delivery cadence. We typically recommend a short pilot to validate the rubric and measure disagreement, then we lock a predictable pricing model for production batches.
How fast can you start and deliver the first labeled batch?
Most teams can start quickly once security and acceptance criteria are agreed. We typically use Day 0–3 to finalize scope, taxonomy, and QA gates, then deliver a pilot batch in Week 1–2. Many engagements reach a stable “pilot-to-scale” workflow within 2–3 weeks, depending on modalities and edge case complexity. If you already have guidelines and gold data, we can compress timelines; if not, we’ll help you formalize them so outputs remain consistent at scale.
What annotation formats do you support for ML training data?
We support common training and evaluation exports across modalities. Text and RLHF are commonly delivered as JSONL and CSV with rubric fields and traceability. For vision, we can export COCO JSON, Pascal VOC XML, YOLO TXT, and CVAT-style packages. Video work is delivered with timecoded segments and per-frame metadata in JSON/CSV. For 3D/4D, we deliver JSON annotations alongside point cloud sidecars (e.g., PCD/PLY) and sequence manifests. We’ll map outputs to your taxonomy and naming conventions.
What labeling accuracy can you guarantee?
Accuracy depends on task ambiguity and how well-defined the rubric is, but Abaka supports high-accuracy delivery with multi-layer QA and adjudication. For suitable tasks, we can target up to 99% accuracy using calibrated gold sets, reviewer escalation, and measured acceptance thresholds. Where definitions are inherently fuzzy (e.g., subjective categories), we focus on consistency metrics and disagreement reduction, then refine guidelines until agreement stabilizes. You’ll receive QA reporting and an error taxonomy so improvements are measurable.
How do you keep our training data secure?
Abaka uses strict NDAs, segregated secure pipelines, and compliance-aligned operations (SOC 2 and ISO 27001; GDPR/CCPA readiness). Access is controlled to match your requirements, and we maintain audit trails to support internal reviews. Most importantly, we never build models that compete with you—your data remains exclusively yours and is never repurposed, resold, or shared. If you have additional constraints (network restrictions, custom access patterns), we’ll scope them during Day 0–3.
Do you support multilingual data labeling and global coverage?
Yes. Abaka can staff work across 50+ countries, which helps with multilingual and locale-specific labeling needs such as taxonomy localization, cultural nuance, and region-specific edge cases. We support multilingual text labeling, audio transcription workflows, and evaluation tasks for multilingual assistants. For consistent quality, we use language-specific guidelines, calibrate annotators with gold examples, and apply reviewer escalation when ambiguity is high. Deliverables can be normalized to a single schema across languages to simplify training and evaluation.
How is Abaka different from other data labeling companies?
Abaka is designed as a trustworthy data partner for frontier AI, not a generic labeling marketplace. We combine vertically specialized talent, multi-layer QA with adjudication, and Abaka Forge workflows so delivery is repeatable and auditable. On trust, we never build models that compete with you; your data is exclusively yours and is never repurposed, resold, or shared. On compliance, we operate with SOC 2 and ISO 27001-aligned controls and GDPR/CCPA readiness, which reduces risk during procurement and deployment.
Can we request changes to guidelines after the project starts?
Yes—change requests are normal as your model reveals new edge cases. We handle updates through controlled rubric revisions, versioned guidelines, and targeted rework queues rather than redoing everything. When you change definitions or add new labels, we recommend a small recalibration batch to measure disagreement and adjust gold examples. Abaka Forge supports audit trails and batch-level reporting, so you can see exactly what changed, which items were affected, and how quality metrics shift after the update.
Can you run a small pilot before committing to a larger labeling contract?
Yes. A pilot is the best way to validate rubric clarity, QA performance, and export compatibility. Many teams run a pilot in Week 1–2 and use the results to finalize acceptance criteria, sampling, and cost controls. The pilot output includes labeled data plus a QA report that highlights disagreement patterns and edge cases. Once your team approves the rubric and results, we scale delivery without changing the underlying workflow—so production batches remain consistent with what you validated.
Who owns the labeled data and outputs?
You do. Abaka’s operating principle is that your data is exclusively yours—never repurposed, resold, or shared. We work under strict NDAs and maintain clear audit trails and IP provenance for delivered outputs. If the engagement includes data collection, we aim for 0% copyright risk on collected data through provenance controls and documented sourcing. If you need specific contract language around ownership, retention, or deletion windows, we’ll align during kickoff.
What tools and platforms do you use for data labeling?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, training support, and production workflows across text, RLHF, image, video, and 3D/4D point cloud. Forge standardizes task templates, reviewer queues, and audits, and it supports controlled exports to your systems. Where appropriate, large-model automation accelerates repetitive steps (up to 50x faster for suitable parts of the workflow), while humans remain in the loop for judgment-heavy decisions and QA.
Is there a minimum project size for your ML data labeling agency services?
We support both small pilots and ongoing production programs. Minimum size depends on modality and how much rubric and QA setup is required, but we can often start with a focused pilot batch to validate success criteria before scaling. If your dataset is small but high-stakes (e.g., safety-critical edge cases, expert domains, or evaluation sets), we can scope a reviewer-heavy approach rather than large throughput. Share your modality, target volume, and timeline, and we’ll propose the right minimum scope.

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Label the Present. Train the Future. Talk to an Expert to scope your ML data labeling agency workflow, run a pilot in 2–3 weeks, and scale with QA you can audit.

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