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Ship trustworthy labels for
Supervised Learning Data Solutions

Abaka delivers high-precision labeled datasets with multi-layer QA, secure pipelines, and flexible throughput—so your team trains, evaluates, and iterates faster across text, vision, and multimodal models.

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When supervised datasets drift, your model metrics drift with them. A few percent of label noise can turn into weeks of wasted debugging, retraining, and failed releases—especially when your evaluation set shares the same bias. Teams often discover issues late: schema ambiguity, inconsistent edge-case handling, and undocumented guideline changes. The result is expensive rework, blocked launches, and brittle performance in production. If you keep patching the model instead of fixing the data, you can burn 2–3 weeks per iteration cycle and still ship a system that underperforms on real users.

Abaka helps you treat labeled data as an engineered product—scoped, versioned, audited, and measurable. We combine vertically specialized annotators, scholar-network reviewers, and a QA-first process to deliver supervised learning datasets you can trust. Using Abaka Forge for workflow control and large-model automation, we keep instruction clarity, sampling, and acceptance metrics consistent from pilot through scale. You get secure, segregated pipelines (SOC 2, ISO 27001, GDPR, CCPA) and full IP provenance—so your team can iterate confidently without data leakage or compliance surprises.

The Supervised Learning Data Solutions Bottleneck

01

Quality Decay

Supervised learning breaks when labeling rules aren’t stable. If your guidelines shift mid-project or edge cases aren’t adjudicated, inter-annotator agreement drops and label noise compounds across training and evaluation. Abaka combats this with calibrated onboarding, gold-set gating, and multi-layer QA so acceptance criteria stay consistent as volume grows. We cap throughput at 500 files/day per annotator to avoid rushed work and maintain 99% accuracy targets where the task allows. You get versioned instructions, audit trails, and clear escalation paths for hard examples.

02

Volume Walls

Internal teams often hit a ceiling: the moment you need thousands of labels per day, coordination overhead explodes—batching, sampling, QC, and rework all multiply. Even if labeling is straightforward, the pipeline is not. Abaka scales with 1M+ specialized annotators across 50+ countries, while keeping QA consistent via centralized rubric design and reviewer layers. With Abaka Forge workflows and automation, you can ramp volume without sacrificing reliability—so your supervised training data arrives on schedule instead of slipping by weeks.

03

Compliance Friction

Supervised datasets frequently include sensitive or proprietary inputs—customer conversations, internal documents, medical imagery, or incident logs. Without strong governance, you risk leaks, unclear IP provenance, and blocked procurement. Abaka operates with strict NDAs, segregated secure pipelines, and compliance controls aligned to SOC 2, ISO 27001, GDPR, and CCPA. We also provide full IP provenance for collected data with 0% copyright risk, enabling you to ship models without downstream rights surprises. The outcome is faster approvals and fewer security exceptions.

01

Task-specific labeling for supervised model training

Get production-grade supervised labels across text, image, video, audio, and 3D workflows—classification, extraction, segmentation, keypoints, and temporal events. Abaka pairs vertically specialized annotators with rubric-driven QC, then validates with reviewer layers for consistency on edge cases. We support real formats like JSONL, CSV, COCO, and NER span exports, and we can tailor guidelines for domains like automotive perception, healthcare imaging, finance document understanding, and security logs.

02

Multi-layer QA with measurable acceptance criteria

Move beyond spot checks. Abaka implements gold sets, blinded re-labeling, adjudication queues, and structured disagreement resolution—so you can quantify label quality, not guess. We align KPIs to your training objective (precision vs recall tradeoffs, class imbalance handling, boundary tolerance for segmentation). With a cap of 500 files/day per annotator and calibrated reviewers, you keep quality stable while scaling volume. Deliverables include QC reports, error taxonomies, and guideline change logs.

03

Rubric design and edge-case policy standardization

Ambiguous instructions create inconsistent supervision. Abaka helps you design labeling rubrics that reduce interpretation variance: decision trees, negative examples, counterexamples, and escalation rules. We run calibration rounds to converge on consistent handling for borderline cases (e.g., partial occlusion, sarcasm in text, low-SNR audio). The result is cleaner training signal and a dataset you can safely version across model iterations, teams, and geographies.

04

Secure data ingestion, cleaning, and de-duplication

Supervised learning data solutions fail if ingestion is brittle. Abaka supports secure transfer, schema validation, PII-aware handling, and de-duplication to reduce train-test contamination. For collection-driven programs, we provide 0% copyright risk and full IP provenance. You can standardize naming, metadata, and splits, then export in formats ready for training pipelines. Abaka Forge ties the workflow together so your team can audit, reproduce, and re-run jobs without manual spreadsheets.

05

Supervision that complements RLHF and evaluations

Many teams blend supervised fine-tuning with preference data and evaluation. Abaka supports instruction-following datasets, multi-turn conversation labeling, and human evaluation aligned to your rubric. We can pair supervised labels with targeted evaluation sets to detect regressions early, and we can bring domain specialists from scholar-network tracks (medicine, law, mathematics, coding, languages) when correctness matters. Outputs integrate cleanly into your training and evaluation loop without reinventing process each sprint.

06

Multilingual supervised datasets across 50+ countries

If your product is global, your supervision must be global. Abaka staffs multilingual annotators and reviewers to handle locale-specific semantics, policy, and formatting. We support language identification, translation QA, sentiment and intent labeling, and culturally aware category definitions. You receive consistent label schemas across languages, plus documentation for regional variations. This reduces deployment risk where a model performs well in one market but fails in another due to subtle labeling drift.

07

Abaka Forge workflows with large-model automation

Abaka Forge is our all-in-one platform for collection, cleaning, annotation, training handoff, and production operations across text, image, video, 3D/4D point cloud, and RLHF. Large-model automation can accelerate throughput up to 50x while keeping humans in the loop for edge cases and QA gates. You get queue design, reviewer routing, audit logs, and export controls—so supervised learning data solutions run like a repeatable pipeline, not a one-off project.

08

Versioned dataset delivery with reproducible splits

Abaka delivers supervised datasets with explicit versions, documented guidelines, and consistent train/val/test splits. We can maintain long-running programs where new data arrives weekly, labels are updated under change control, and evaluation sets remain stable for trend tracking. Deliverables include label schemas, QC summaries, and recommended sampling strategies to target rare classes. This lets your team compare model improvements honestly and avoid hidden leakage or moving-goalpost evaluation.

Why Outsource Supervised Learning Data Solutions

01

Faster Delivery

Get to a reliable dataset faster by avoiding internal hiring, training, and workflow tooling from scratch. Abaka can start with a scoped pilot, then scale to full production using established QA gates and reviewer routing. With 50+ countries of coverage and specialized roles, you reduce schedule risk and keep delivery steady when requirements evolve.

02

Direct Savings

Outsourcing reduces the hidden cost of coordination—PM overhead, rework cycles, and late-stage relabeling. Abaka’s structured process and automation in Abaka Forge minimize duplicate effort and prevent label churn from ambiguous guidelines. Your team spends less time managing annotation and more time improving the model and product.

03

Risk Reduction

Security and compliance constraints can stall data programs. Abaka operates with SOC 2 and ISO 27001-aligned controls, plus GDPR and CCPA practices, strict NDAs, and segregated pipelines. We also provide full IP provenance for collected data with 0% copyright risk, reducing legal and reputational exposure.

04

Elastic Scalability

Supervised workloads are spiky—new launches, incident response, or new markets can require fast ramp-ups. Abaka scales with 1M+ specialized annotators while preserving QA consistency via calibrated rubrics and reviewer layers. You can expand volume without over-hiring or burning out internal experts.

05

Domain Expertise

Many supervised tasks require more than generic labeling—medical terminology, legal reasoning, automotive scene understanding, or multilingual nuance. Abaka draws from scholar-network domains including medicine, law, mathematics, coding, languages, and business. This improves correctness on hard examples and reduces downstream model errors.

06

Innovation Velocity

As your modeling approach changes—new architectures, new evaluation criteria, new failure modes—your data program must adapt. Abaka supports supervised labels, RLHF-adjacent supervision, and human evaluation, enabling tighter iteration loops. You get a partner that can reshape guidelines, sampling, and QC as the product evolves.

Industries We Serve

Automotive

Train perception and driver-assistance models with consistent supervision for lanes, objects, and scene attributes. Abaka supports image/video labeling plus LiDAR and fusion workflows, with reviewer adjudication for rare scenarios. For mapping and lane work, we can align deliverables to your internal schema and provide Road Lane labeling priced per km when needed.

GenAI / Foundation Models

Build supervised fine-tuning sets for instruction following, domain Q&A, and tool-usage readiness—paired with human QA so you can trust correctness. Abaka’s scholar-network reviewers help on math, coding, and specialized domains. You get versioned datasets and stable evaluation splits to track improvements across releases.

Embodied AI / Robotics

Improve robot perception and decision-making with supervised annotations for object states, affordances, trajectories, and task outcomes. Abaka can combine labeled sensor data with RL-adjacent supervision and targeted evaluation sets. Workflows support image/video plus 3D/4D point cloud, enabling consistent ground truth across environments and time.

Healthcare

Create supervised datasets for medical imaging and clinical text tasks where correctness and consistency matter. Abaka provides rubric design, calibrated reviewers, and secure pipelines aligned to SOC 2 and ISO 27001 practices, plus GDPR and CCPA. You can label imaging findings, document entities, and structured fields while maintaining auditability and change control.

Retail

Power search, personalization, and catalog intelligence with supervised labels for product attributes, taxonomy mapping, and intent classification. Abaka supports image tagging, text extraction, and multilingual classification to help you expand across markets. You receive consistent schemas and QC reporting so downstream models don’t drift when catalog content changes.

Finance

Train document understanding and risk workflows with supervised labels for forms, statements, and communications—classification, extraction, and entity linking. Abaka provides strict NDAs, segregated secure pipelines, and audit-ready exports. With domain-aware reviewers, you reduce critical mistakes in high-impact categories and keep evaluation sets stable for compliance reporting.

Geospatial

Generate supervised ground truth for land use, infrastructure mapping, and change detection using imagery, video, and 3D data. Abaka supports segmentation, object delineation, and temporal event labeling with measured QA. Deliverables can include COCO-style exports and GIS-friendly formats so your team can train and validate geospatial models faster.

Security / Defense

Build supervised datasets for threat detection, sensor fusion, and analyst-assist tooling with careful governance. Abaka operates under strict NDAs, segregated pipelines, and compliance controls aligned to SOC 2 and ISO 27001, with GDPR/CCPA practices where applicable. You get reliable labeling and auditable processes without exposing sensitive operational context.

Agriculture / Industrial

Train supervised models for quality inspection, equipment monitoring, and crop/field intelligence. Abaka supports image/video labeling and sensor-adjacent metadata workflows with consistent rubrics for defect taxonomies and severity. This reduces false alarms and missed defects, and helps your team scale datasets seasonally without losing consistency.

How It Works

1) Day 0–3 — Scope, rubric, and success metrics

We align on the supervised task definition, label schema, edge-case policy, and acceptance metrics (e.g., per-class precision/recall targets, boundary tolerance, disagreement thresholds). You share samples and constraints; we propose a rubric, QC plan, and delivery cadence. Security and access are set up with strict NDAs and segregated pipelines as required.

2) Week 1–2 — Pilot labeling + calibration rounds

Abaka runs a pilot batch to validate guidelines and surface ambiguity early. We conduct calibration rounds with gold sets, re-labeling checks, and adjudication on disagreements. You receive sample exports in your preferred formats (JSONL/CSV/COCO, etc.) plus an error taxonomy and updated rubric so the full run starts with stable instructions.

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

We ramp throughput while keeping quality stable—annotator throughput is capped (up to 500 files/day per annotator) and reviewed through structured QA layers. Abaka Forge provides workflow control, audit logs, and export consistency. You get weekly quality reports and dataset versions so model training can start before the final batch completes.

4) Ongoing — Versioning, refresh, and drift control

As your product changes, we update guidelines under change control and maintain dataset versions. We can refresh hard classes, rebalance distributions, or add new label types without breaking evaluation continuity. Secure handling remains consistent through segregated pipelines and compliance-aligned controls, keeping procurement and security reviews smooth.

5) Weekly — Review, optimize, and iterate

Each week we review throughput, acceptance rates, and disagreement drivers, then refine sampling and escalation rules. We can add domain reviewers (math, coding, medicine, law, languages) for correctness-critical segments. The goal is a predictable supervised data pipeline that improves over time, not a one-off labeling sprint.

Modality & Format Coverage

Supervised learning data solutions rarely stay single-modality. Abaka covers text, RLHF-adjacent supervision, vision, video, 3D/4D, sensor fusion, and audio—delivered in formats your training stack already supports.

ModalityAnnotation TypesToolsOutput Formats
TextClassification, NER/span labeling, summarization targets, instruction-following SFT, document field extractionAbaka ForgeJSONL, CSV, TSV, Parquet, BIO/IOB2
LLM RLHFPreference ranking, pairwise comparisons, rubric-based scoring, helpfulness/harmlessness review, human eval notesAbaka ForgeJSONL, conversational JSON, CSV, eval scorecards, prompt/response bundles
ImageBounding boxes, polygons, instance/semantic segmentation, keypoints, dense captioningAbaka ForgeCOCO JSON, YOLO TXT, Pascal VOC XML, PNG masks, JSONL
VideoTemporal events, object tracking, action labels, frame-by-frame segmentation, trajectory annotationAbaka ForgeCOCO-VID JSON, frame-indexed JSONL, MP4 sidecars, CSV timelines, mask sequences
3D/4D Point Cloud3D bounding boxes, point-level segmentation, instance IDs over time, pose/keypoints, scene graph labelsAbaka ForgeKITTI-style JSON (custom), PCD/PLY sidecars, per-point labels, JSONL, CSV
LiDAR + Camera fusionCross-sensor alignment QA, fused 3D boxes, 2D–3D correspondence, occlusion tagging, multi-view consistency checksAbaka ForgeSynchronized JSONL, per-sensor annotations, calibration metadata bundles, CSV, fused scene packages
AudioTranscription, speaker diarization, intent/sentiment labels, keyword spotting, timestamped eventsAbaka ForgeTextGrid, JSON, JSONL, SRT/VTT, CSV

Success Story

A leading enterprise AI team
Challenge

The customer needed supervised learning data solutions for a multi-modality product, but their internal labeling process could not keep guidelines consistent across teams. Disagreement rates were high, edge-case handling varied by reviewer, and model improvements were hard to validate because the evaluation set contained hidden label drift. Shipping deadlines were approaching, and each relabeling cycle was taking multiple weeks, pulling engineers into operational work instead of model development. They needed a partner that could stabilize the rubric, scale labeling reliably, and provide auditable dataset versions for repeatable training.

Approach

Abaka started with a tightly scoped pilot to lock the label schema, define adjudication rules, and create gold sets for calibration. We built a multi-layer QA workflow in Abaka Forge with reviewer routing, disagreement tagging, and guideline version control. Domain reviewers handled correctness-critical samples, while throughput was scaled using specialized annotators and large-model automation for pre-labeling where appropriate—keeping humans in the loop for final decisions. Weekly reviews aligned acceptance criteria to model objectives, and exports were delivered in the customer’s preferred structured formats with stable splits.

Results

Within 2–3 weeks, the customer had a production-ready supervised dataset with consistent guidelines, measurable QA outcomes, and repeatable versioning for future refreshes. The stabilized supervision reduced relabeling churn and enabled faster iteration on model changes because training and evaluation were no longer moving targets. The team transitioned from ad hoc labeling to a managed pipeline with audit logs, clear escalation for edge cases, and predictable weekly drops—resulting in 99% accuracy targets on agreed task definitions and a materially shorter release cycle.

2–3 weeks
From pilot to scaled production delivery
99%
Accuracy target with multi-layer QA
50+
Countries available for multilingual coverage

By the Numbers

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

What Customers Say

We came in with a vague labeling brief and a backlog of edge cases. Abaka helped us turn it into a rubric with clear decision rules, then kept quality stable as we scaled volume. The dataset versions and QA reporting made it obvious what changed between drops, so training and evaluation finally became repeatable.

Director of Applied MLEnterprise AI Platform Company

Their team caught failure modes our internal process missed—especially around disagreement handling and class definitions. Once the gold sets and adjudication workflow were in place, we stopped doing costly relabeling passes. We were able to reallocate engineers from ops work back to modeling and deployment.

Head of Data ScienceGlobal Retail Technology Company

Security and governance were non-negotiable for us. The segregated pipeline approach and auditability reduced time spent on procurement and reviews. We also appreciated the clear stance on data ownership—our data stayed ours, with no repurposing or resale.

Security & Compliance LeadFinancial Services Company

We needed multilingual supervised labels quickly without sacrificing consistency. Abaka’s calibration rounds and reviewer layers kept the schema aligned across languages, and their weekly cadence helped us ship improvements steadily. The workflow felt like an extension of our team rather than a black box vendor.

Product ML ManagerConsumer Applications Company

Why Choose Abaka

01

Data you can defend—quality, compliance, and IP provenance built in.

Abaka is built for teams that need supervised datasets they can stand behind in production. You get multi-layer QA, calibrated reviewers, and clear rubric versioning—so training signal stays consistent over time. Our security posture supports strict NDAs and segregated pipelines, with controls aligned to SOC 2 and ISO 27001 plus GDPR and CCPA practices. For collected data, we provide full IP provenance with 0% copyright risk. And we never build models that compete with you—your data is exclusively yours.

02

Specialists at scale

Access 1M+ specialized annotators and scholar-network reviewers across domains like medicine, law, mathematics, coding, languages, science, and business. Scale volume without turning quality into a guessing game.

03

Measured QA, not vibes

Gold sets, blinded re-labeling, adjudication, and structured error taxonomies help you quantify what’s correct and why. This reduces expensive relabeling loops and makes model improvements attributable to real data changes.

04

Abaka Forge operational control

Run supervised learning data solutions in Abaka Forge with workflow routing, audit logs, and consistent exports across text, vision, video, and 3D. Large-model automation can accelerate throughput up to 50x while keeping humans in the loop for edge cases.

05

Compliance-ready delivery

Abaka supports secure transfer, segregated environments, and documentation that helps your team pass procurement and security reviews faster. You keep ownership and control—no repurposing, reselling, or sharing of your data.

06

A partner aligned with your outcomes

Founded in 2019, self-funded and profitable, Abaka is designed to be a long-term, trustworthy data partner for frontier AI. With offices in Singapore, Paris, and Silicon Valley, we support global programs with predictable cadence and transparent quality signals. We help you scope, pilot, scale, and maintain supervised datasets as versioned assets—so your team ships reliably and iterates faster.

Frequently Asked Questions

How much do supervised learning data solutions cost?
Pricing depends on modality, difficulty, rubric complexity, and the level of expert review required. As reference points, Abaka pricing includes LLM Math/Coding annotation at $18/hr, STEM Generalist at $12/hr, Dense Captioning at $6/hr, and Image Editing at $8/hr. For automotive mapping tasks, Road Lane labeling is available at $3/km. We typically start with a pilot batch to confirm guidelines and QA metrics, then provide a predictable rate card for production so you can forecast cost per dataset version.
How fast can you deliver a supervised training dataset?
Most teams see meaningful delivery within 2–3 weeks, depending on scope and volume. The fastest path is a Day 0–3 scoping phase, followed by a Week 1–2 pilot and calibration round, then scale in Week 2–3. If your rubric is already stable and you have clean input data, we can move faster; if you need new guidelines, heavy edge-case adjudication, or multilingual coverage, we plan a slightly longer ramp. We also support weekly drops for ongoing programs.
What modalities and output formats do you support for supervised learning?
We support text, image, video, audio, 3D/4D point cloud, and LiDAR + camera fusion, plus RLHF-adjacent supervision where helpful. Common outputs include JSONL and CSV for text and conversational data; COCO/YOLO/VOC for image; timeline-based JSONL and frame-indexed exports for video; SRT/VTT for audio; and packaged sensor bundles for 3D and fusion workflows. If you have a custom schema, we can map labels to your internal format and maintain versioned delivery.
How do you ensure annotation accuracy and consistency?
We treat labeling as a controlled process: rubric design, calibration rounds, gold sets, and multi-layer QA. Disagreements are not ignored—they are routed to adjudication with documented decisions, and the rubric is updated under version control. We cap throughput at up to 500 files/day per annotator to reduce rushed work and maintain stable performance. For correctness-critical tasks, we add domain reviewers from scholar-network tracks (e.g., medicine, law, math, coding) so edge cases are handled consistently across batches.
How do you handle security, NDAs, and compliance requirements?
Abaka operates with strict NDAs, segregated secure pipelines, and controls aligned to SOC 2 and ISO 27001, with GDPR and CCPA practices. We can support least-privilege access, audit logging, and environment separation based on your risk posture. For collected data, we provide full IP provenance and 0% copyright risk to reduce downstream legal exposure. If your team has specific requirements (region constraints, tooling restrictions, or additional controls), we scope them during Day 0–3 and reflect them in the delivery plan.
Can you provide multilingual supervised datasets?
Yes. Abaka supports multilingual annotation across 50+ countries, including locale-specific labeling where categories and policies differ by region. We can run language-specific calibration rounds and maintain a shared global schema with documented deviations when needed. This approach helps avoid the common failure mode where labels look consistent on paper but diverge in practice due to cultural or linguistic nuance. Deliverables include consistent exports and documentation so your team can train global models without hidden labeling drift.
How are you different from other data labeling companies?
Two differences matter most for supervised learning programs: trust and process. Abaka is a trustworthy data partner for frontier AI—your data is exclusively yours and is never repurposed, resold, or shared, and we never build models that compete with you. Operationally, we emphasize rubric versioning, measurable QA, and calibrated reviewer layers rather than pure throughput. Combined with Abaka Forge workflow controls and automation, you get repeatable dataset versions your team can audit and reproduce across releases.
What if we need guideline changes or label schema updates mid-project?
Change happens—what matters is controlling it. We run changes through a versioned rubric process, document what changed and why, and define whether prior batches need backfills or whether the change starts from a specific dataset version. We can also create compatibility mappings if your training pipeline needs a stable schema. Weekly reviews help surface drift early, and adjudication outcomes become new rubric rules. This prevents silent shifts that invalidate evaluation and makes your dataset evolution explicit.
Can we start with a pilot before committing to full production?
Yes—pilots are the recommended starting point for supervised learning data solutions. A pilot validates label definitions, edge-case rules, and QA thresholds with real examples before you scale. You’ll receive sample exports in your required formats, plus a quality report and an error taxonomy that reveals where the rubric needs tightening. Once the pilot is accepted, we scale the same workflow and keep the dataset versioned so you can train immediately and expand volume without retooling.
Who owns the labeled data and can you reuse it?
You own your data and outputs. Abaka’s policy is that your data is exclusively yours—never repurposed, resold, or shared. We also do not build models that compete with you, so there is no incentive to extract value from your datasets beyond delivering the project. For collection-driven programs, we provide full IP provenance and a 0% copyright risk approach to reduce downstream exposure. Contract terms and access controls can be aligned to your internal requirements.
What tooling do you use to manage supervised labeling workflows?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, training handoff, and production operations across text, image, video, 3D/4D point cloud, and RLHF. Forge supports workflow routing, reviewer layers, audit logs, and consistent exports. Large-model automation can accelerate throughput up to 50x, while humans handle adjudication and edge cases under a controlled rubric. If your team has existing tools, we can integrate via export/import and maintain your schema.
What is the minimum project size for supervised learning data solutions?
There isn’t a single minimum, but we typically recommend starting with a pilot batch large enough to include edge cases and class imbalance—often a few hundred to a few thousand items depending on modality. This lets us measure disagreement, validate QA gates, and refine the rubric before scaling. If you’re early-stage, we can scope a smaller discovery run focused on guideline design and feasibility. If you’re at production scale, we can plan weekly drops and ongoing refresh to manage drift.

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Label the Present. Train the Future.

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