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Build reliable datasets with a
Training Data Generation Firm

Abaka delivers compliant, production-ready training data across text, image, video, and 3D—so your team ships models faster with fewer re-trains and cleaner eval wins.

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When training data is inconsistent, everything downstream slows down—prompting turns into firefighting, eval results look noisy, and teams burn weeks on re-labeling. In practice, a single label-spec change can trigger 2–3 weeks of rework across multiple vendors, while internal reviewers spend 10–20 hours/week resolving edge-case disagreements. The cost of inaction compounds: model iterations stall, safety coverage stays thin, and you end up paying twice—once for the first dataset, and again to fix it after you discover quality drift in production.

Abaka is a training data generation firm designed for frontier AI workflows—collection, cleaning, annotation, and evaluation with strict IP provenance and secure pipelines. You get vertically specialized annotators across 50+ countries, multi-layer QA targeting 99% accuracy, and a single operating system for delivery via Abaka Forge. We align on success criteria up front (formats, schemas, auditability), then run weekly calibration so your specs don’t decay over time. The result is faster iteration, lower risk, and datasets your team can trust for training and benchmarking.

The Training Data Generation Firm Bottleneck

01

Quality Decay

Early batches often look fine, but quality drifts as edge cases accumulate and instructions get reinterpreted. Without tight calibration, inter-annotator disagreement rises and your “gold” set becomes outdated in 2–4 weeks. Abaka maintains multi-layer QA, reviewer escalation, and weekly guideline refreshes so quality stays stable at 99% target accuracy. We also cap throughput to avoid rushed work—up to 500 files/day per annotator—so speed never silently trades off against correctness.

02

Volume Walls

Most teams can label a pilot, but scaling to production exposes bottlenecks in recruiting, training, and coordination. A dataset that starts as 5,000 samples can become 500,000 samples after you expand coverage, languages, or long-tail scenes. Abaka scales with 1M+ specialized annotators across 50+ countries and uses Abaka Forge automation to accelerate repetitive steps—often delivering 50× faster where large-model assistance is applicable—while keeping formats and QA consistent.

03

Compliance Friction

Data programs stall when legal, security, and procurement requirements aren’t met—especially for regulated or sensitive domains. You may lose 4–8 weeks navigating NDAs, access controls, and audit trails, only to discover vendors can’t prove provenance or isolate pipelines. Abaka operates with SOC 2, ISO 27001, GDPR, and CCPA alignment, uses segregated secure workflows, and maintains full IP provenance—meaning 0% copyright risk on collected data—so your team can ship without compliance whiplash.

01

Custom data sourcing with IP-provenance guarantees

When you need data that doesn’t exist off-the-shelf, Abaka can source or collect it with strict provenance and secure handling. We support text, image, video, and sensor-driven capture, with curated, timestamped, tagged delivery for downstream training. You get clear usage rights and audit trails, with 0% copyright risk on collected data. Teams use this for assistant tuning, domain adaptation in medicine and law, retail product understanding, and geospatial mapping tasks where ownership and traceability matter.

02

Data cleaning, deduping, and schema normalization

Abaka prepares training-ready corpora by de-duplicating, filtering, and normalizing to your schema—so model training isn’t poisoned by noise. We deliver consistent keys, label sets, and validation rules across batches, reducing data debt that usually shows up as unstable eval curves. For multimodal projects, we align text-to-image pairs and clip boundaries for video, and we can provide curated splits (train/val/test) with sampling strategies for long-tail coverage. Workflows run inside Abaka Forge for traceability.

03

High-accuracy annotation with multi-layer QA

Abaka provides end-to-end annotation for classification, detection, segmentation, transcription, dense captioning, and domain-specific labeling. You get vertically specialized annotators plus scholar-network reviewers in domains like Automobile, Medicine, Law, Mathematics, and Coding. We target 99% accuracy using calibration rounds, golden sets, and escalation on ambiguous cases. Output is delivered in your preferred formats (JSONL/CSV/COCO-like structures as needed) and versioned so your team can reproduce training runs and audits.

04

LLM RLHF pipelines: SFT, preference, and rubric grading

For aligned behavior, Abaka runs RLHF workflows including instruction following, multi-turn conversations, preference ranking, and rubric-based scoring. We support HLE-style QAs, reasoning tasks, and specialized domains (coding, math, medicine, business), with calibrated graders and reviewer arbitration. Abaka Forge tracks prompts, responses, rationales (when requested), and scoring metadata so you can debug reward-model drift. Teams commonly combine this with safety coverage and red-teaming evals to close gaps before deployment.

05

Multimodal datasets for vision, video, and 3D

Abaka builds datasets across image, video, and 3D/4D point cloud for perception and multimodal reasoning. Capabilities include dense captioning, object detection/segmentation, temporal event tagging, and 3D bounding primitives for robotics and autonomy research. We also support interleaved images and text for instruction-following and spatial reasoning tasks. Delivery includes consistent naming, frame alignment, and dataset cards describing label definitions, QA methods, and known failure modes—so your team can trust what’s inside.

06

Model evaluation and red-teaming with human judgment

Abaka evaluates models using a 6-dimension framework: Accuracy & Precision, Robustness & Reliability, Efficiency & Scalability, Safety & Bias Audits, Tool & Function Calling, and User Interaction & Usability. We combine objective benchmarks, model-as-judge where appropriate, and human evaluation for high-signal comparisons. Pricing can be per-eval (e.g., red teaming, defensive coding, math capabilities), and outputs include annotated failure taxonomies and recommended dataset expansions to fix the root causes, not just the symptoms.

07

Abaka Forge to run production data operations

Abaka Forge is the operational backbone—collection, cleaning, annotation, training handoff, and production monitoring in one place. It supports all data types: text, image, video, 3D/4D point cloud, and RLHF. Your team gets versioning, role-based access, and workflow automation that can run up to 50× faster with large-model assistance on repetitive steps. Forge credits are available at $0.20 USD each, enabling predictable scaling for teams that want a single system of record.

08

Embedded data and ML talent for sustained delivery

When you need continuity beyond a single dataset, Abaka can embed talent across annotation ops, engineering, algorithm development, and model training support. Engagements can be project-based or long-term, and can include on-site collaboration when required. This is a strong fit for enterprise teams migrating from ad-hoc labeling to a stable data factory, or for research labs that need fast iteration loops between evaluation findings and new training data. You keep control of strategy; we execute reliably.

Why Outsource Training Data Generation Firm Work

01

Faster Delivery

Spin up expert capacity without waiting on hiring cycles. Abaka can move from spec to production batches quickly, with clear milestones and weekly shipping. Many programs see usable datasets in 2–3 weeks once guidelines and QA gates are set.

02

Direct Savings

Outsourcing reduces the hidden cost of internal coordination—review queues, tooling maintenance, and rework. With consistent QA and spec management, you avoid paying twice for the same data and keep labeling spend tied to measurable outcomes.

03

Risk Reduction

Abaka operates with SOC 2, ISO 27001, GDPR, and CCPA alignment, plus strict NDAs and segregated pipelines. You get IP provenance and auditable delivery so your team can train and evaluate with confidence.

04

Elastic Scalability

Scale from a 1,000-sample pilot to production volumes without rebuilding the operation. Abaka’s annotator network spans 50+ countries, supporting multilingual expansion, long-tail coverage, and peak workloads without quality collapse.

05

Domain Expertise

Generic labelers miss domain nuance. Abaka brings specialized annotators and scholar-grade reviewers across medicine, law, mathematics, and coding—so your datasets reflect real-world criteria, not surface-level heuristics.

06

Innovation Velocity

Abaka Forge adds automation and repeatable workflows that accelerate iteration loops. When eval findings reveal gaps, we turn them into new data tasks quickly—closing the train–test loop without derailing your roadmap.

Industries We Serve

Automotive

Support perception and planning programs with lane, curb, signage, and scene understanding datasets. Abaka can deliver road-lane labeling priced per km and maintain consistent geometry rules across cities, seasons, and sensors—useful for ADAS validation and autonomy research.

GenAI / Foundation Models

Build instruction tuning, reasoning, and safety datasets with calibrated graders and reviewer arbitration. Abaka supports RLHF, domain QAs, and multilingual expansion—helping your team improve factuality, helpfulness, and tool-use behavior without sacrificing auditability.

Embodied AI / Robotics

Create 3D/4D point cloud and video datasets for navigation, manipulation, and spatial reasoning. Abaka can annotate trajectories, object states, and task outcomes, and can also support custom RL environment design for real-world agent capability.

Healthcare

Generate medical text and imaging datasets with strict access controls and domain-aware reviewers. Use Abaka for triage assistants, clinical summarization, coding support, and safety evaluations—where instruction clarity and provenance matter as much as accuracy.

Retail

Improve search, recommendation, and catalog intelligence with product attribute labeling, image moderation, and review understanding datasets. Abaka can build consistent taxonomies and deliver clean train/val/test splits to reduce noisy metrics and churn in A/B tests.

Finance

Train and evaluate models for document understanding, risk workflows, and customer support with compliance-ready handling. Abaka can label entities, transactions, and policy interpretations, and run targeted evals for hallucination risk and refusal behavior.

Geospatial

Develop mapping and change-detection datasets using imagery, video, and 3D sources. Abaka supports consistent annotation schemas, timestamped delivery, and quality gates that help teams measure progress across regions and sensing conditions.

Security / Defense

Support mission-oriented perception and language systems with secure pipelines and strict NDAs. Abaka can build datasets for detection, activity recognition, and red-teaming evaluations, with clear provenance and controlled access for sensitive projects.

Agriculture / Industrial

Generate datasets for inspection, yield estimation, defect detection, and automation using image, video, and sensor-driven modalities. Abaka helps you expand long-tail coverage—rare failures, lighting shifts, seasonal variation—without losing labeling consistency.

How It Works

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

We align on what “good” means: target tasks, label definitions, edge cases, required formats, and acceptance thresholds. Your team shares samples and failure modes; we propose guidelines, QA gates, and a delivery plan. Security, NDA, and access controls are finalized so work can start cleanly.

2) Week 1–2 — Pilot batch + calibration loop

Abaka delivers a pilot batch to validate instructions and reveal ambiguity. We run calibration sessions, refine rubrics, and establish a golden set. You review a small slice, we incorporate feedback quickly, and we lock a stable spec so scaling doesn’t introduce drift.

3) Week 2–3 — Scale production with QA and versioning

We ramp volume while maintaining multi-layer QA and reviewer escalation. Outputs are versioned and delivered in your required formats, with metadata for traceability. If you need multilingual expansion or new edge-case coverage, we add it as tracked change requests instead of ad-hoc edits.

4) Ongoing — Evaluate, patch gaps, and expand coverage

As models improve, your data needs shift. We turn eval findings into targeted new data tasks—hard negatives, safety probes, rare scenes, and domain depth. This keeps training data aligned to real errors, not generic benchmarks, and reduces re-train cycles caused by unseen distribution shifts.

5) Weekly — Reporting, audits, and spec governance

You receive weekly reporting on throughput, QA outcomes, and unresolved edge cases. We maintain clear change logs for label specs, dataset versions, and acceptance tests, so your team can reproduce experiments and satisfy internal audit requirements without slowing delivery.

Modality & Format Coverage

Your training data generation firm should cover more than one format. Abaka supports multimodal pipelines end-to-end, with consistent schemas, QA gates, and production delivery through Abaka Forge.

ModalityAnnotation TypesToolsOutput Formats
TextInstruction tuning (SFT), classification, entity extraction, summarization, long-form reasoning QAsAbaka ForgeJSONL, CSV, Parquet, UTF-8 TXT, dataset cards (PDF/MD)
LLM RLHFPreference ranking, rubric grading, safety/refusal evaluation, tool/function-calling checksAbaka ForgeJSONL (prompt/response pairs), pairwise preference JSON, scored rubric tables (CSV), evaluation reports
ImageClassification, bounding boxes, polygon segmentation, keypoints, dense captioningAbaka ForgeCOCO-like JSON, CSV annotations, PNG/JPEG assets, QA manifests, label maps
VideoTemporal event tagging, multi-object tracking, frame-level boxes/masks, action labelsAbaka ForgeJSON/JSONL, CSV timelines, MP4/H.264 references, frame index maps, clip manifests
3D/4D Point Cloud3D bounding primitives, instance segmentation, tracking across frames, scene semanticsAbaka ForgeJSON annotations, PCD/PLY/LAS references, frame transforms, class taxonomies, QA logs
LiDAR + Camera fusionCross-sensor alignment checks, fused 2D–3D boxes, lane/road structure labeling, occlusion taggingAbaka ForgeSensor-synced manifests, JSON labels, calibration files, timestamped sequences, QA reports
AudioTranscription, speaker labeling, keyword spotting labels, intent tagging, TTS dataset preparationAbaka ForgeWAV/FLAC assets, JSON/CSV transcripts, speaker diarization RTTM, lexicons, dataset cards

Success Story

A leading frontier model lab
Challenge

The customer needed a reliable training data generation firm to scale instruction tuning and safety coverage without introducing label drift. Their internal team could design prompts and run evals, but they were bottlenecked on consistent grading, edge-case adjudication, and version control across weekly releases. Prior vendors delivered uneven quality—high variance between annotators, unclear rationales, and slow turnaround on spec changes—leading to noisy reward-model training and unstable regression results. They needed auditable datasets that would hold up under frequent iteration.

Approach

Abaka set up an RLHF pipeline with calibrated rubrics, reviewer arbitration, and a golden-set program managed inside Abaka Forge. We staffed domain-specialized graders (coding, math, and general instruction following) and implemented weekly calibration to keep interpretations consistent. We also mapped eval failures into targeted new tasks—hard prompts, refusal boundaries, and tool-use scenarios—so data generation tracked real weaknesses. Deliverables were versioned with clear change logs and acceptance checks to support fast experimentation and reproducibility.

Results

Within the first production cycle, the customer stabilized grading consistency and accelerated dataset delivery without sacrificing auditability. Multi-layer QA drove higher agreement on edge cases, and weekly governance reduced “silent spec drift” that previously forced relabeling. The team shipped new RLHF batches on a predictable cadence, improved coverage for safety and tool-use behaviors, and reduced internal review load while maintaining 99% accuracy targets. Outcomes included faster iteration loops, fewer regressions in evaluation suites, and measurable improvements within 2–3 weeks of kickoff.

99%
Target labeling accuracy with multi-layer QA
50+
Countries supported for multilingual and regional coverage
2–3 weeks
Typical timeline to first production-ready batch

By the Numbers

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

What Customers Say

We came in with messy guidelines and a moving target. Abaka translated our goals into a stable rubric, ran calibration weekly, and delivered versions we could actually reproduce in training. The biggest win was fewer surprise regressions because the dataset stayed consistent as we scaled.

Director of Applied MLFrontier Model Lab

Quality improved when Abaka introduced reviewer escalation and a real golden set. We stopped arguing about edge cases internally because decisions were logged and consistent across batches. Our internal review time dropped noticeably while acceptance rates went up.

Head of Data OperationsEnterprise AI Platform Company

Security and provenance were non-negotiable for us. Abaka’s segregated pipeline and clear audit trail made procurement straightforward, and we could prove where every asset came from. Delivery stayed on schedule even as we expanded to multiple languages.

Security Program ManagerRegulated Software Company

The combination of Abaka Forge and domain-specialized annotators let us iterate quickly. When evals surfaced new failure modes, Abaka turned them into targeted data tasks instead of generic labeling. That kept our training spend focused on improving the model, not just growing volume.

ML Engineering LeadRobotics & Automation Company

Why Choose Abaka

01

A training data generation firm that ships production datasets—securely and repeatedly.

Abaka is built for teams that need more than one-off labeling. You get a trustworthy partner for frontier AI with SOC 2 and ISO 27001 alignment, strict NDAs, segregated pipelines, and full IP provenance. We never build models that compete with you—your data is exclusively yours and never repurposed, resold, or shared. With Abaka Forge as the system of record, your team gets versioned delivery, QA traceability, and a scalable operating model for training and evaluation.

02

99% accuracy target

Multi-layer QA, calibrated rubrics, and reviewer arbitration help keep labeling consistent as volumes grow. You get predictable acceptance criteria instead of “best effort” batches that require relabeling.

03

50+ country coverage

Expand into languages, dialects, and regional contexts without rebuilding your workflow. Abaka supports multilingual datasets with consistent governance and delivery formats.

04

Abaka Forge workflow control

Run collection, cleaning, annotation, and production delivery in one platform. Automation accelerates repetitive steps, while versioning and access control keep your team’s experiments reproducible and auditable.

05

Specialists for hard domains

Access domain-ready annotators and scholar-network reviewers across coding, mathematics, medicine, law, and more. This reduces semantic errors that generic vendors miss and improves dataset signal for training.

06

Self-funded, profitable, and aligned with your incentives

Abaka is self-funded and profitable, with offices in Singapore, Paris, and Silicon Valley. There’s no acquisition pressure and no incentive to repurpose customer data. You get a stable partner focused on delivery quality, compliance, and long-term reliability—so your data operation doesn’t reset every time vendor priorities change.

Frequently Asked Questions

How much does a training data generation firm cost?
Pricing depends on modality, complexity, and QA depth, but Abaka provides concrete unit economics. For expert LLM work, Math/Coding annotation is $18/hr and STEM generalist work is $12/hr. For vision tasks, dense captioning can be $6/hr and image editing $8/hr, while autonomous driving road-lane labeling is $3/km. For model evaluation, red teaming can be $8/eval and defensive coding $15/eval. We’ll scope your dataset and recommend the most cost-effective mix.
How fast can you deliver training data after kickoff?
Most teams see a usable pilot quickly, then a first production-ready batch in 2–3 weeks once the label spec and QA gates are stable. Timelines depend on modality (text vs video vs 3D), target accuracy, and whether you need new collection. We structure delivery with a Day 0–3 scoping sprint, a Week 1–2 calibration pilot, and Week 2–3 production ramp. Weekly reporting keeps progress visible and prevents surprise delays from ambiguous edge cases.
What data formats and schemas can you deliver?
We deliver to your required schema, and we can adapt output formats to match your training stack and data lake. Common formats include JSONL and CSV for text and RLHF; COCO-like JSON and label maps for image; JSON/CSV timelines for video; and JSON plus point-cloud references for 3D/4D. We also provide dataset cards and QA manifests so your team can track label definitions, known limitations, and version history. Abaka Forge maintains traceability across every batch.
What accuracy level can you achieve for labeling?
Abaka targets 99% accuracy using multi-layer QA, golden sets, calibration sessions, and reviewer arbitration for edge cases. The exact achieved rate depends on how deterministic the task is and how well the spec can be operationalized (for example, subjective safety judgments require rubric alignment). We de-risk accuracy by running a pilot batch, measuring agreement and error patterns, then tightening guidelines before scaling. You get acceptance tests and clear escalation paths so errors are caught early rather than at the end.
How do you handle security and sensitive data?
Abaka operates with SOC 2 and ISO 27001 alignment and supports GDPR and CCPA requirements. We use strict NDAs, segregated secure pipelines, role-based access control, and audit-friendly delivery. For sensitive programs, we can limit access to dedicated teams and controlled workspaces, and we maintain clear provenance for assets and annotations. If your team has additional requirements (VPC setup, restricted export policies, or custom retention), we scope them during Day 0–3 and reflect them in the delivery plan.
Can you generate multilingual training data?
Yes—Abaka supports multilingual training data across 50+ countries, including localized instruction tuning, translation, and region-specific safety evaluations. We design workflows that keep semantics consistent across languages: shared rubrics, bilingual reviewer checks, and back-translation validation when appropriate. Your team can request language-specific metadata (locale, dialect, register) and consistent train/val/test splits. This approach prevents the common failure mode where each language becomes a separate project with incompatible labeling standards.
How are you different from other data labeling or dataset vendors?
Abaka is structured for frontier AI delivery: secure, versioned, and governance-driven, not ad-hoc task marketplaces. You get Abaka Forge as a system of record, multi-layer QA targeting 99% accuracy, and access to domain specialists for math, coding, medicine, and law. We also emphasize IP provenance and segregated pipelines, reducing compliance friction. Importantly, Abaka never builds models that compete with you—your data is exclusively yours and is never repurposed, resold, or shared.
What if our label spec changes mid-project?
Change is normal—what matters is managing it without blowing up timelines. Abaka runs spec governance with documented change requests, versioned guidelines, and impact estimates before modifications roll out. We can apply changes forward-only, or selectively reprocess affected subsets using targeted relabeling, depending on your training needs. Weekly calibration ensures new interpretations are adopted consistently across the team. This prevents “silent drift,” where different annotators apply different versions of the spec in the same batch.
Can we start with a pilot before committing to a full dataset?
Yes—pilots are often the fastest way to validate instructions, formats, and QA gates. A pilot typically includes a scoped sample set, initial guidelines, calibration feedback, and a quality report that highlights ambiguous edge cases. From there, we agree on acceptance thresholds and scale plans. Pilots are designed to be production-relevant: the outputs are delivered in final formats and can be used for training or evaluation, not just internal demos. This reduces risk before scaling volume.
Who owns the data and annotations you produce?
You do. Abaka’s operating model is built around customer exclusivity: your data is never repurposed, resold, or shared. We maintain provenance records and deliver versioned datasets and logs so you can demonstrate ownership and traceability internally. Because Abaka does not build models that compete with customers, there is no incentive to retain or reuse your artifacts beyond what’s required to deliver the project under your contractual terms. We can also align to your retention and deletion policies.
What tools do you use to manage and deliver datasets?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, training handoff, and production delivery. Forge supports text, image, video, 3D/4D point cloud, and RLHF workflows, with role-based access, version control, QA tracking, and automation to accelerate repetitive steps. Teams use Forge to keep a single source of truth across dataset versions and to simplify audits and reproducibility. If you have existing tooling, we can deliver to your schema and integrate via exports.
What is the minimum project size you can support?
We support both small, high-signal pilots and large production programs. Minimum size depends on modality and complexity: for many teams, a 500–2,000 sample pilot is enough to validate rubrics, QA gates, and output formats. For RLHF or safety evaluation, you can start with a limited set of prompts and targeted failure modes. We’ll recommend a minimum that produces statistically useful signal without wasting budget—then scale once acceptance criteria and governance are stable.

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Label the Present. Train the Future.

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