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Scale supervised learning training data
with verified ground truth you can trust

Abaka delivers high-accuracy supervised labels across text, vision, audio, and 3D—backed by multi-layer QA, secure pipelines, and delivery plans that match your model roadmap.

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When supervised training data is inconsistent, your model quality becomes a moving target: precision drops, edge cases slip through, and releases stall in rework loops. Teams often lose 2–3 weeks per iteration chasing label drift, reviewer disagreement, and unclear definitions, while costs rise as you relabel the same samples. In regulated or safety-critical settings, a single ambiguous label policy can trigger audit friction, delays in procurement, or production rollbacks. The result is slower learning curves, lower confidence in metrics, and wasted engineering time spent debugging data instead of improving the model.

Abaka is your supervised learning data provider for durable ground truth—designed for repeatable training, not one-off labeling. We align on a measurable spec, build domain-calibrated guidelines, and run multi-layer QA so labels stay stable across batches and over time. Your team gets clear acceptance criteria, audit-ready provenance, and a delivery cadence that supports continuous retraining. With Abaka Forge, we combine human intelligence and large-model automation where appropriate, so you can scale volume without sacrificing consistency across text, image, video, and 3D workloads.

The Supervised Learning Data Provider Bottleneck

01

Quality Decay

Supervised learning pipelines break when “ground truth” changes between batches. As guidelines evolve, inter-annotator agreement can drift, and you end up with silent label shift that looks like model regression. Abaka prevents quality decay by locking a versioned labeling spec, running calibration rounds, and auditing disagreement hotspots before scale. We implement multi-layer QA with targeted re-review of high-impact classes and hard negatives, so precision stays stable as volume grows. Teams typically cap at 500 files/day per annotator for sustainable throughput—our process is built to keep that pace without sacrificing consistency.

02

Volume Walls

Even strong internal teams hit volume ceilings: hiring, training, and managing reviewers is slow, and labeling backlogs block retraining schedules. If you need tens of thousands of labeled examples, the bottleneck quickly becomes operations rather than modeling. Abaka scales with 1M+ vertically specialized annotators across 50+ countries, so you can ramp quickly while keeping domain fit. We also use Abaka Forge to streamline task routing, guideline enforcement, and QC sampling—reducing time lost to manual coordination and helping you hit 2–3 week delivery windows for many supervised datasets.

03

Compliance Friction

Supervised datasets often include sensitive fields, regulated content, or proprietary edge cases. Without strong governance, projects slow down under security reviews, NDAs, and unclear IP provenance—especially when multiple vendors or freelancers touch the data. Abaka operates under SOC 2 and ISO 27001 controls with GDPR/CCPA alignment, secure segregated pipelines, and strict NDAs. We preserve full IP provenance and ensure 0% copyright risk on collected data, so your supervised learning program can pass procurement and audit steps without repeated delays or rework.

01

Label specs that survive retraining and iteration

We translate your model goals into a versioned supervised labeling spec—definitions, edge-case rules, class hierarchy, and acceptance criteria—so datasets remain comparable across time. Abaka teams run calibration exercises and disagreement analysis before scaling. You get artifacted guidelines, reviewer notes, and decision logs your ML and compliance stakeholders can reference. This is especially valuable for fraud, medical triage, autonomous driving perception, and enterprise search relevance where subtle ambiguity creates label shift.

02

Multi-layer QA for consistent ground truth at scale

Abaka runs multi-layer QA with structured sampling, adjudication, and targeted re-review of high-impact slices (rare classes, boundary cases, long-tail). We can staff scholar-network reviewers in domains like Medicine, Law, Mathematics, and Coding when correctness requires more than generic labeling. Quality is measured, not assumed—your team receives QC summaries and dataset-level validation outputs. Where helpful, Abaka Forge accelerates review with large-model automation while keeping humans responsible for final ground truth.

03

Image and video annotation for supervised CV models

We label images and videos for detection, segmentation, tracking, keypoints, attributes, and dense captioning—supporting retail shelf analytics, medical imaging triage, industrial inspection, and security monitoring. Typical outputs include COCO JSON, Pascal VOC XML, and YOLO TXT, plus frame-level video exports. Our teams handle edge cases like occlusion, motion blur, and class imbalance with clear policies. Abaka Forge provides workflow controls, audits, and consistent task routing across large batches.

04

3D/4D point cloud labeling for perception stacks

For robotics and autonomy, we label 3D/4D point clouds with 3D boxes, semantic segmentation, and temporal consistency checks. We support scene understanding for indoor robotics, warehouse automation, and mapping. Outputs can be delivered as JSON schemas aligned to your ingestion needs, plus polygon/box metadata and timestamps. Abaka Forge supports point cloud visualization, QA sampling, and reviewer escalation so you can scale safely without losing consistency in long sequences.

05

LiDAR + camera fusion annotation for safety-critical tasks

Fusion projects require more than separate labels—they need synchronized policies across sensors. We annotate and validate LiDAR-camera aligned sequences with consistent object IDs, 2D/3D boxes, and attributes. This supports ADAS development, robotics navigation, and perimeter security. We can incorporate road-lane workflows when needed (priced per km in many programs), and we keep provenance and audit logs for each sequence. Abaka Forge helps manage synchronization, review gates, and exception handling at scale.

06

Text classification and extraction for supervised NLP

We build supervised NLP datasets for classification, NER, relation extraction, and retrieval relevance—covering customer support, legal review, finance, and compliance monitoring. We support multilingual labeling with reviewer calibration across locales. Outputs include JSONL, CSV, TSV, and span-annotation formats compatible with modern training stacks. When tasks overlap with LLM training, we can extend into instruction following and reasoning evaluation—using scholar-grade reviewers for domains like Business, Science, and Languages.

07

Bridge supervised labeling into RLHF and evaluations

Many teams start with supervised ground truth and then expand to preference data, red-teaming, or benchmark-style evaluation. Abaka supports LLM RLHF pipelines—prompt curation, ranking, pairwise preferences, and rubric-based scoring—using domain specialists for Math, Coding (including Lean4), and high-stakes reasoning. This lets you reuse taxonomy and QA practices across supervised and alignment workflows. Outputs include JSONL preference pairs and rubric score tables aligned to your evaluation harness.

08

Operate end-to-end in Abaka Forge platform

Abaka Forge is our all-in-one platform for collection, cleaning, annotation, training handoff, and production governance across Image, Video, Text, RLHF, and 3D/4D point cloud. You get centralized task management, reviewer calibration, QA sampling, and export controls. Forge accelerates work with large-model automation—up to 50x faster on suitable tasks—while preserving human accountability for correctness. Credits are available at $0.20 USD each for platform usage tied to specific workflows.

Why Outsource Supervised Learning Data Provider Work

01

Faster Delivery

Hit retraining deadlines without building an operations team. Abaka ramps quickly with domain-calibrated annotators and QA leads, enabling many supervised datasets to ship in 2–3 weeks depending on complexity and volume. You keep engineering focused on modeling, not workflow management.

02

Direct Savings

Outsourcing reduces hidden costs—relabeling, internal reviewer time, and the opportunity cost of delayed launches. With transparent per-hour and per-unit options, you can forecast spend and avoid “trial-and-error” labeling. We also reduce rework through calibration and spec versioning.

03

Risk Reduction

Abaka is built for enterprise risk controls: SOC 2, ISO 27001, GDPR, and CCPA alignment with strict NDAs and segregated secure pipelines. You get full IP provenance and assurance that your data is never repurposed, resold, or shared—reducing compliance surprises.

04

Elastic Scalability

Need to label 5k items this week and 200k next month? Abaka scales up and down without forcing you into hiring cycles. With 1M+ specialized annotators across 50+ countries, we match staffing to your release cadence while keeping guidelines stable.

05

Domain Expertise

Supervised learning quality often hinges on expertise, not effort. Abaka’s scholar-network coverage spans Automobile, Medicine, Law, Mathematics, Coding, and more, so edge cases are judged correctly. This is critical for safety, fraud, and high-precision extraction tasks.

06

Innovation Velocity

Your team moves faster when data operations are predictable. Abaka brings mature QA playbooks, workflow automation in Abaka Forge, and optional extensions into RLHF and evaluation—so you can iterate on datasets, not reinvent infrastructure every quarter.

Industries We Serve

Automotive

Train supervised perception and ADAS models with consistent labeling across objects, lanes, and edge cases. Abaka supports image, video, and fusion workflows with versioned specs, temporal consistency checks, and audit-ready exports—so metrics remain comparable across releases.

GenAI / Foundation Models

Build supervised datasets that bootstrap instruction following, tool-use behavior, and grounded QA—then expand into RLHF and evaluation when you’re ready. Abaka provides domain specialists for reasoning, math, and coding tasks, plus secure pipelines and provenance controls.

Embodied AI / Robotics

Supervised labels for robotics perception and interaction—3D scene understanding, object affordances, and multimodal alignment—delivered with consistent taxonomy and QA. Abaka can also support custom RL environment design when you need to go beyond supervised learning.

Healthcare

Create high-precision supervised datasets for triage, coding support, and imaging workflows using calibrated reviewers and strict QA. Abaka emphasizes data governance and provenance so healthcare-adjacent ML teams can pass security reviews and maintain consistent labeling standards.

Retail

Train supervised models for product recognition, shelf analytics, search relevance, and customer intent classification. Abaka handles image/video labeling and text classification with clear class definitions, hard-negative mining support, and exports that fit your training pipelines.

Finance

Improve supervised learning for fraud detection, document understanding, and compliance monitoring with consistent labeling and domain-aware review. Abaka supports sensitive workflows with secure pipelines and clear audit trails, reducing relabeling and policy churn.

Geospatial

Label supervised datasets for mapping, change detection, land-use classification, and infrastructure monitoring across imagery and 3D sources. Abaka delivers structured outputs and QA sampling tailored to rare classes and boundary ambiguity common in geospatial ML.

Security / Defense

Build supervised datasets for detection, identification, and anomaly analysis with controlled access, strict NDAs, and segregated workflows. Abaka’s governance-first approach supports sensitive programs while maintaining consistent label policies across long timelines.

Agriculture / Industrial

Train supervised models for crop health, equipment monitoring, defect detection, and safety analytics across image, video, and sensor data. Abaka’s QA methods and clear spec versioning help keep labels stable across seasons, sites, and changing operating conditions.

How It Works

1) Day 0–3 — Scope, sampling, and label spec

We align on your supervised learning objective, target metrics, classes, and edge cases. Abaka reviews sample data, proposes a versioned labeling spec, and defines acceptance criteria and QC plan. If required, we set up secure access, NDAs, and segregated pipelines.

2) Week 1–2 — Pilot labeling and calibration

We run a pilot batch to validate taxonomy, ambiguity handling, and reviewer calibration. You receive pilot outputs, disagreement analysis, and recommended spec tweaks. This phase prevents downstream relabeling and ensures the dataset reflects how your model will be evaluated.

3) Week 2–3 — Scale production with multi-layer QA

After sign-off, production ramps with multi-layer QA: sampling, adjudication, and targeted re-review of hard slices. Abaka Forge manages task routing, reviewer performance, and audit logs. Deliverables are exported in the formats your training pipeline expects.

4) Ongoing — Iteration, drift checks, and new edge cases

As your model evolves, we help you add new classes, hard negatives, and refreshed sampling strategies without breaking comparability. We keep specs versioned and document all changes, so you can attribute metric shifts to model changes—not data drift.

5) Weekly — Reporting and stakeholder-ready updates

Each week, you get a concise operational report: throughput, QC results, disagreement hotspots, and any guideline updates. This keeps ML, product, and compliance stakeholders aligned and reduces surprises at release time—especially in long-running supervised programs.

Modality & Format Coverage

Supervised learning isn’t one modality. Abaka covers text, vision, audio, and 3D with consistent QA and export formats—so your team can train, evaluate, and retrain without rebuilding the data pipeline each time.

ModalityAnnotation TypesToolsOutput Formats
Textclassification (single/multi-label), NER spans, relation extraction, retrieval relevance grading, structured data extractionAbaka ForgeJSONL, CSV/TSV, BIO/IOB tagging, span-offset JSON, Parquet
LLM RLHFpairwise preferences, rubric scoring, instruction following checks, safety/bias review, tool/function-call evaluationAbaka ForgeJSONL preference pairs, rubric score tables (CSV), conversation transcripts (JSON), eval reports (CSV/JSON)
Imagebounding boxes, polygons/segmentation masks, keypoints, attributes, dense captioningAbaka ForgeCOCO JSON, Pascal VOC XML, YOLO TXT, mask PNGs, JSON exports
Videoframe-by-frame boxes, tracking IDs, action labels, event timestamps, temporal segmentationAbaka Forgeframe JSON, COCO-style video JSON, CSV timelines, MP4 sidecars, sequence manifests
3D/4D Point Cloud3D bounding boxes, semantic segmentation, instance IDs, temporal consistency review, scene attributesAbaka ForgeJSON annotations, PCD/PLY sidecars, sequence manifests, timestamps CSV, QA logs
LiDAR + Camera fusion2D/3D synchronized boxes, consistent object IDs, lane/scene attributes (when required), sensor alignment validationAbaka Forgesynchronized JSON, per-sensor annotation exports, sequence manifests, timestamps CSV, QA audit logs
Audiotranscription, speaker diarization, intent classification, keyword spotting labels, timestamped segmentsAbaka ForgeJSONL, CSV, TextGrid, RTTM, timestamped transcript JSON

Success Story

A leading enterprise ML platform team
Challenge

The team was training supervised models across multiple business units, but ground truth inconsistency was blocking reliable evaluation. Labels were produced by different internal groups with mismatched definitions, leading to noisy metrics and repeated relabeling. Each new dataset batch introduced subtle class drift, and reviewers disagreed on boundary cases. The result was slowed releases and decreased trust in offline improvements—engineers spent time diagnosing data issues instead of iterating on the model. They needed a supervised learning data provider who could standardize the spec, enforce QA, and deliver at scale with clear governance.

Approach

Abaka led a structured reset: we co-authored a versioned labeling spec with explicit edge-case rules, created calibration tasks, and established an adjudication process for disagreement hotspots. We staffed domain-matched annotators and QA leads, then ran a pilot to validate definitions and acceptance criteria before scaling. Production was executed in Abaka Forge with controlled task routing, multi-layer QA sampling, and audit logs for every change. We also implemented weekly reporting so stakeholders could see throughput, QC performance, and spec updates without waiting for end-of-project summaries.

Results

With standardized definitions and multi-layer QA, the team stabilized dataset quality and restored trust in evaluation. They eliminated recurring relabeling loops, improved consistency across batches, and shipped training-ready exports on a predictable cadence. The program scaled across modalities while maintaining governance and provenance, enabling faster retraining and fewer regressions attributed to data drift. Outcomes included 99% accuracy on agreed QC checks, a pilot-to-production ramp completed within 2–3 weeks, and sustained throughput aligned to the 500 files/day per-annotator operating cap for quality-controlled delivery.

99%
QC accuracy on agreed checks
2–3 weeks
Pilot-to-production ramp
50+
Countries to support scale and locale coverage

By the Numbers

2019
Founded — trustworthy data partner for frontier AI
1,000+
Enterprise and research customers
1M+
Vertically specialized annotators
SOC 2 + ISO 27001
Security controls with GDPR/CCPA alignment

What Customers Say

We needed supervised labels that would remain stable across retraining cycles. Abaka helped us tighten definitions, run a pilot, and scale production without quality drift. The weekly QC reporting made it easy to align stakeholders and keep momentum.

Director of Applied MLEnterprise Software Company

Our biggest issue was inconsistent edge-case handling across annotators. Abaka’s calibration and adjudication flow reduced disagreements and made our metrics trustworthy again. Delivery was predictable and the exports fit our training pipeline cleanly.

Head of Data OperationsFinancial Services Company

Security review was a blocker for us with other vendors. Abaka’s governance-first approach, segregated workflows, and clear provenance made onboarding straightforward. We could ship supervised datasets without compromising on controls.

Security & Compliance LeadRegulated Enterprise

We work across text and vision, and it’s rare to find a provider that can handle both with consistent QA. Abaka standardized our specs and helped us keep label quality high while scaling volume and adding new classes over time.

ML Platform ManagerMultimodal AI Product Company

Why Choose Abaka

01

A supervised learning data provider built for repeatable ground truth

Abaka delivers supervised datasets your team can trust across iterations—versioned specs, calibrated reviewers, and multi-layer QA that prevents silent label drift. We operate with SOC 2 and ISO 27001 controls, GDPR/CCPA alignment, strict NDAs, and segregated secure pipelines. Your data is exclusively yours—never repurposed, resold, or shared—and we never build models that compete with you. The result is predictable dataset delivery that supports retraining, evaluation, and long-term governance.

02

99% accuracy workflows

We design QC and adjudication processes to hit high-precision targets, including 99% accuracy on agreed checks, with clear acceptance criteria and reporting your stakeholders can trust.

03

Domain-matched reviewers

From Medicine and Law to Coding and Mathematics, Abaka can staff scholar-grade reviewers when correctness matters more than speed—especially for edge cases and long-tail classes.

04

Abaka Forge for operational control

Run labeling, QA sampling, and governance in Abaka Forge—supporting text, image, video, RLHF, and 3D/4D point cloud. Automation accelerates throughput while humans remain accountable for final ground truth.

05

Security and provenance by default

SOC 2 and ISO 27001 controls, GDPR/CCPA alignment, strict NDAs, and segregated secure pipelines reduce procurement friction. Full IP provenance supports audit readiness and 0% copyright risk on collected data.

06

No conflict business model—your data stays yours

Abaka is self-funded and profitable with no acquisition pressure. We never build models that compete with you, and we never reuse your data. That alignment matters when supervised datasets contain proprietary product signals, sensitive workflows, or safety-critical edge cases.

Frequently Asked Questions

How much does a supervised learning data provider cost?
Pricing depends on modality, complexity, and reviewer expertise. For supervised labeling, common baselines include STEM Generalist work at $12/hr and LLM Math/Coding work at $18/hr, with specialized options like Dense Captioning at $6/hr and Image Editing at $8/hr. For certain autonomy workflows, Road Lane labeling can be priced at $3/km. We’ll scope your taxonomy, QA requirements, and export formats, then propose a plan that balances accuracy targets and delivery speed. Talk to an Expert for a fast estimate based on a sample batch and your acceptance criteria.
How fast can you deliver supervised learning training data?
Many supervised datasets can move from pilot to scaled production in 2–3 weeks, depending on volume, ambiguity, and the number of edge cases that require calibration. We typically start with Day 0–3 scoping and spec drafting, then run a pilot in Week 1–2 to validate guidelines and QC. After sign-off, we ramp production with multi-layer QA and deliver on a predictable cadence. If you have a hard deadline, we’ll design staffing and QC sampling to meet it without sacrificing consistency.
What modalities and file formats do you support for supervised learning datasets?
We support text, image, video, audio, 3D/4D point cloud, and LiDAR + camera fusion. Outputs commonly include JSONL, CSV/TSV, COCO JSON, Pascal VOC XML, YOLO TXT, mask PNGs, and custom JSON schemas aligned to your ingestion pipeline. For video and 3D sequences, we deliver sequence manifests, timestamps, and consistent IDs where required. If you already have a training loader, we’ll match its expectations and validate exports during the pilot so production runs don’t introduce formatting surprises.
What accuracy can you achieve on supervised labels?
Accuracy depends on label complexity, ambiguity, and the clarity of the spec. Abaka is set up to target high-precision outcomes—often up to 99% accuracy on agreed QC checks—by combining calibration rounds, multi-layer QA, and adjudication for disagreement hotspots. We define measurable acceptance criteria up front (including how QC is sampled and scored), then report results weekly so you can catch drift early. For high-stakes domains, we can add scholar-network reviewers to improve correctness on nuanced edge cases.
How do you keep my training data secure?
Abaka operates under SOC 2 and ISO 27001 controls with GDPR and CCPA alignment, strict NDAs, and segregated secure pipelines. We can support controlled access workflows and minimize exposure through role-based task routing. We also maintain full IP provenance, and your data is exclusively yours—never repurposed, resold, or shared. Importantly, we never build models that compete with you, which reduces strategic risk when the dataset contains proprietary product signals or sensitive operational details.
Can you label multilingual data for supervised learning?
Yes. Abaka supports multilingual supervised labeling through geographically distributed teams spanning 50+ countries, with calibration and QA designed to keep definitions consistent across locales. We can run language-specific pilots, create locale-aware guidelines, and compare disagreement patterns across regions to reduce drift. For workflows like intent classification, extraction, or sentiment, we align on a single ontology and document locale-specific exceptions so your model learns the right generalizations rather than country-specific noise.
How is Abaka different from other data labeling companies?
Abaka is designed for frontier AI teams that need repeatable, governed ground truth—not one-off labeling. We combine domain-matched annotators, multi-layer QA, and Abaka Forge workflow controls, plus security controls (SOC 2, ISO 27001) and full provenance. Your data is exclusively yours—never repurposed, resold, or shared—and we never build models that compete with you. That alignment helps reduce risk while improving dataset stability across retraining cycles and long-running programs.
What if we need changes to the labeling spec mid-project?
Change is normal in supervised learning—new edge cases appear once you train and evaluate. Abaka manages this by versioning the spec and documenting all updates, so you can keep comparability across dataset releases. We’ll recommend whether changes require partial relabeling, targeted re-review, or simply new sampling for hard negatives. Abaka Forge supports controlled rollouts so only the intended batches are affected. You get weekly visibility into what changed, why it changed, and how it may impact metrics.
Can we start with a pilot before committing to full-scale labeling?
Yes—pilots are the default starting point for most supervised programs. In Week 1–2, we label a representative subset, run calibration, quantify disagreement hotspots, and validate export formats. You get a clear view of label clarity, expected throughput, and QA effectiveness before scaling. This reduces the likelihood of expensive relabeling later and helps align internal stakeholders on what “correct” means. After pilot sign-off, we ramp production with the agreed QC plan and delivery cadence.
Who owns the labeled dataset and derived outputs?
You do. Abaka’s position is that your data is exclusively yours—never repurposed, resold, or shared. We also maintain full IP provenance to support governance and audit needs. Contractually, we can align to your requirements for ownership, confidentiality, and deletion/retention policies. If your program includes sensitive or proprietary content, we can implement segregated secure pipelines and role-based access so only approved personnel handle the dataset throughout labeling and QA.
What tools or platforms do you use for supervised labeling?
We operate in Abaka Forge—our all-in-one platform for collection, cleaning, annotation, and production governance across text, image, video, RLHF, and 3D/4D point cloud. Forge supports workflow controls like task routing, calibration, QC sampling, adjudication, and export validation. It also enables large-model automation where appropriate to accelerate throughput while keeping humans accountable for correctness. If you have internal tooling, we can align exports to your ingestion requirements and validate compatibility in the pilot.
What’s the minimum dataset size you can support?
We support both small, high-precision pilots and scaled production runs. Minimum size depends more on complexity than raw count: a 500-item expert-reviewed dataset can be more demanding than a 50k-item simple classification job. We typically recommend starting with a pilot batch large enough to surface edge cases and disagreement patterns, then scaling once the spec is stable. Talk to an Expert and we’ll suggest a pilot size, QC plan, and timeline based on your classes, modalities, and target metrics.

Ready to Get Started?

Label the Present. Train the Future.

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