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Scale high-trust datasets with a
Training Data Generation Partner

Build model-ready training and evaluation data across text, vision, audio, and 3D with scholar-grade QA, secure pipelines, and production velocity your roadmap can rely on.

Talk to an Expert

When training data is inconsistent, your team pays twice: once in wasted labeling hours and again in weeks of debugging model behavior that traces back to unclear guidelines, noisy samples, or missing edge cases. Small error rates compound—5% label noise can trigger repeated retrains, longer hyperparameter searches, and fragile evaluation results that don’t reproduce. The cost of inaction is tangible: delayed launches by 2–6 weeks, higher compute spend, and product risk when models fail under distribution shift, multilingual inputs, or safety-sensitive prompts.

Abaka AI is your training data generation partner for frontier-quality datasets—built to be accurate, scalable, and provable. You bring the target tasks and acceptance criteria; we operationalize collection, cleaning, labeling, RLHF, and evaluation with multi-layer QA and full IP provenance. With Abaka Forge, you get one workflow from spec to delivery across modalities, plus governance that matches enterprise expectations: strict NDAs, segregated pipelines, and compliance programs aligned to SOC 2, ISO 27001, GDPR, and CCPA.

The Training Data Generation Partner Bottleneck

01

Quality Decay

As volume grows, quality often drops—especially when guidelines evolve midstream. A single ambiguous definition (for example, what counts as “unsafe advice”) can create 10–20% disagreement between annotators, forcing rework and retraining. Abaka prevents quality decay with calibrated rubrics, gold sets, reviewer escalation, and scholar-network specialists for domains like medicine, law, mathematics, and coding. We track drift at batch level so you can lock accuracy targets (up to 99% on suitable tasks) before you scale.

02

Volume Walls

Internal teams hit throughput ceilings fast. Even strong annotators top out at around 500 files/day per person, and hiring ramps can take 4–8 weeks—too slow for iterative model development. Abaka breaks the volume wall with a global, vertically specialized workforce across 50+ countries plus platform automation in Abaka Forge. You can run pilots in 2–3 weeks, then expand to sustained production without retooling your pipeline or compromising review depth.

03

Compliance Friction

Training data programs stall when security reviews, privacy requirements, and IP provenance aren’t engineered in from Day 0. Without clear controls, data transfers and access requests can add 2–4 weeks of process overhead and limit what your team can label or share. Abaka reduces compliance friction using strict NDAs, segregated secure pipelines, and standards-aligned practices (SOC 2, ISO 27001, GDPR, CCPA). You retain ownership, and your data is never repurposed, resold, or shared.

01

Task scoping, rubrics, and acceptance criteria

Start with a dataset spec your engineers can trust: label taxonomy, edge-case definitions, reviewer thresholds, and sampling plans. We convert product requirements into executable guidelines for text, image, video, audio, and 3D tasks. Abaka supports frontier-model needs like instruction following, reasoning quality, dense captioning, and safety categories. Your team gets a clean handoff: what we label, how we label it, and how “done” is measured—before the first batch ships.

02

Custom data collection with IP provenance

When off-the-shelf data won’t cover your edge cases, Abaka can source and capture custom text, image, video, audio, and sensor streams—pre-filtered, curated, timestamped, and tagged. Our collection workflows are designed for 0% copyright risk on collected data, making governance easier for enterprise deployment. For teams building agents and real-world systems, we support targeted scenario capture and structured metadata so your training set aligns with the operating environment.

03

High-accuracy labeling across multimodal datasets

Abaka delivers production labeling with multi-layer QA and domain-specialist reviewers. We cover tasks ranging from classification and extraction in text to segmentation, keypoints, and tracking in vision workflows, as well as 3D point cloud cuboids and trajectories. For complex work, we route samples to scholar-network expertise in medicine, law, coding, math (including Lean4), and science. Throughput scales without sacrificing consistency, with clear batch reports and error analysis.

04

RLHF and preference data for LLM alignment

Generate RLHF datasets that improve instruction following, helpfulness, and safety behavior—without losing factuality. We support pairwise preference labeling, rubric-based grading, multi-turn conversation evaluation, and targeted adversarial prompts. Annotators are trained to apply policy-consistent judgments and to flag uncertainty for reviewer escalation. Outputs integrate cleanly into your pipeline (JSONL/Parquet), and your team can iterate on rubrics weekly as model behavior changes.

05

Math, coding, and expert reasoning datasets

For frontier training, you need more than generic Q&A. Abaka provides datasets for math capabilities, coding correctness, step-wise reasoning, and competition-grade problems—plus validation by subject-matter reviewers. We support domains like mathematics, science, business, and law, and can format outputs for supervised fine-tuning or evaluation harnesses. When required, we separate “final answers” from reasoning traces to match your safety and policy constraints.

06

Dense vision annotation for real products

Build vision datasets with consistent spatial semantics: bounding boxes, polygons, instance/semantic segmentation, keypoints, OCR regions, and dense captioning. For video, we support temporal tracking and event labeling to improve stability over time. Abaka Forge enables model-assisted labeling (where appropriate) to accelerate production while preserving human QA. Deliveries include structured metadata and audit logs so your team can trace revisions and reviewer decisions.

07

3D/4D point cloud labeling and trajectories

For robotics and autonomy, Abaka supports 3D/4D point cloud annotation including cuboids, lanes, drivable space, and motion trajectories. We handle synchronized sequences and frame-to-frame consistency checks, plus scenario tagging for long-tail events. Outputs can be delivered in common formats such as JSON, CSV, and PCD-derived metadata packages, with clear coordinate-frame conventions. Reviewers validate geometric consistency and track-level integrity before release.

08

Model evaluation, red-teaming, and benchmarks

Measure what matters using Abaka’s evaluation workflows: objective benchmarks, model-as-judge where appropriate, and human evaluation with calibrated rubrics. We can audit safety and bias behaviors, tool/function calling, robustness, and user interaction quality. For code and security-sensitive use cases, we run defensive coding and red-teaming evaluations with controlled prompts and reproducible scoring. Results are delivered with slice analysis so you can diagnose failure modes and prioritize fixes.

Why Outsource Training Data Generation Partner Work

01

Faster Delivery

Move from spec to a working pilot in 2–3 weeks, then ramp to production without pausing to hire, train, and re-train contractors. Your team stays focused on model iteration while Abaka runs the data factory.

02

Direct Savings

Reduce rework and internal ops overhead with standardized rubrics, multi-layer QA, and platform workflows. With clear unit economics (per hour, per km, or per eval), you can forecast spend and avoid surprise costs.

03

Risk Reduction

Lower operational and IP risk using strict NDAs, segregated pipelines, and full provenance. Abaka aligns to SOC 2, ISO 27001, GDPR, and CCPA practices and keeps your data exclusive to your team.

04

Elastic Scalability

Scale up for launches and scale down after milestones without reorganizing your team. Abaka’s workforce coverage across 50+ countries helps maintain throughput as tasks shift across languages and domains.

05

Domain Expertise

Access specialist reviewers in medicine, law, math, and coding—without building that bench internally. This is critical for high-stakes labels where a 1–2% error can distort training signals.

06

Innovation Velocity

Iterate faster with weekly rubric updates, error analyses, and structured feedback loops. As your models evolve, we evolve the dataset design—so your training data stays aligned with the product you’re shipping.

Industries We Serve

Automotive

Support ADAS and autonomy programs with lane and drivable-space labeling, scenario tagging, and multi-sensor workflows. Abaka can deliver road-lane annotation priced per km ($3/km) with QA gates that prioritize geometry consistency and long-tail events. For in-cabin and exterior perception, we handle video tracking, keypoints, and safety-critical edge cases. Your team gets reproducible training slices for night, rain, construction, and rare interactions.

GenAI / Foundation Models

Create instruction data, reasoning datasets, multilingual corpora, and RLHF preference labels that improve alignment without collapsing diversity. Abaka supports scholar-network domains like coding, mathematics, medicine, and law—ideal for frontier model labs that need trustworthy supervision. We can also run red-teaming and safety evaluations to expose jailbreak paths and policy failures. Outputs arrive ready for SFT, DPO/RLHF, and evaluation harnesses.

Embodied AI / Robotics

Train agents that act in the real world with datasets built around tasks, failures, and recovery behaviors. Abaka supports 3D/4D point cloud labeling, trajectory annotation, and synchronized sensor packaging for navigation and manipulation. When needed, we help design custom RL environment data requirements and labeling protocols. Your team gets consistent definitions for success/failure states, contact events, and spatial affordances.

Healthcare

Build safer clinical and patient-facing systems with carefully reviewed text labeling, de-identification workflows, and expert validation for medical content. Abaka can staff medicine-domain reviewers to reduce hallucination-prone supervision and to score helpfulness versus risk. We support structured extraction, summarization evaluation, and safety/bias audits for healthcare chat experiences. Compliance-minded processes (GDPR/CCPA-aligned) help your team operate with clear access controls and audit trails.

Retail

Improve search, recommendations, and support automation with high-coverage product and customer-interaction datasets. Abaka labels attributes, intent, sentiment, and product taxonomy mappings, and can generate evaluation sets for customer-service LLMs. For vision use cases, we annotate shelf images, product detection, and OCR for packaging text. Your team receives training-ready outputs with consistent SKUs, variants, and multilingual metadata.

Finance

Support risk and compliance-sensitive AI with datasets designed for traceability. Abaka produces labeled text for classification, extraction, and summarization evaluation, with domain reviewers to reduce ambiguous supervision. We can run red-teaming and safety evaluations focused on fraud, social engineering, and policy violations. Deliveries include clear provenance and change logs so your team can demonstrate how training data evolved across releases.

Geospatial

Build mapping and earth-observation models with reliable annotation across imagery, video, and 3D representations. Abaka supports object detection/segmentation, change detection labeling, and structured metadata that makes temporal analysis easier. For infrastructure monitoring, we tag assets and conditions with consistent taxonomies. Your team gets outputs in common GIS-friendly structures (JSON/GeoJSON-like fielding where applicable) plus QA reports by region and sensor type.

Security / Defense

Operate with security-first data workflows for sensitive programs, including strict NDAs and segregated pipelines. Abaka supports evaluation and red-teaming for LLMs, plus vision and sensor annotation for detection and monitoring tasks. We design labeling protocols that emphasize false-negative reduction and controlled access. Your team keeps full ownership of deliverables, with auditability and clear reviewer escalation for sensitive content.

Agriculture / Industrial

Train perception and decision models for farms, factories, and field equipment using labeled imagery, video, and sensor data. Abaka supports defect detection, segmentation, and event labeling for operational monitoring. For outdoor and seasonal variation, we create balanced datasets across lighting, weather, crop cycles, and equipment types. Outputs are structured for deployment pipelines, with consistent taxonomies and QA checks on long-tail conditions.

How It Works

1) Day 0–3 — Scope, spec, and secure onboarding

We align on objectives, data sources, acceptance criteria, and delivery formats (e.g., JSONL, Parquet, COCO). Abaka sets up secure access, NDAs, and pipeline segregation as needed. You’ll review a draft rubric, edge-case library, and sampling plan. The outcome is a signed-off spec that prevents downstream rework and makes success measurable.

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

We run a pilot batch to validate guidelines, measure inter-annotator agreement, and tune reviewer thresholds. For expert tasks (math, coding, medicine, law), we assign scholar-network reviewers and escalation paths. You receive pilot outputs plus error analysis and recommended rubric updates. This phase locks quality before scaling volume.

3) Week 2–3 — Scale-up, automation, and steady-state flow

After pilot approval, we ramp throughput using Abaka Forge workflows and workforce scaling. Where appropriate, model-assisted tooling accelerates repetitive steps while humans remain the final authority for correctness. We implement batch-level QA reporting and sampling audits. Deliveries arrive on a predictable cadence with versioned guidelines and change logs.

4) Ongoing — Continuous improvement and dataset expansion

As your model ships and learns, the dataset must evolve. We add edge cases, expand languages, and rebalance slices based on your error dashboards. For RLHF and evaluation, we refresh prompts and adversarial sets to track regressions. All changes are documented so you can reproduce training runs and attribute gains to data updates.

5) Weekly — Stakeholder review and roadmap alignment

Every week, we run a structured review: quality metrics, throughput, cost, open questions, and upcoming specs. You can request guideline changes, new classes, or re-labels with clear impact estimates. This keeps your data program aligned with model milestones and prevents drift between product needs and labeling reality.

Modality & Format Coverage

Your training data rarely stays in one format. Abaka covers end-to-end generation and labeling across multimodal inputs, with consistent QA and delivery formats that plug into modern ML stacks and evaluation harnesses.

ModalityAnnotation TypesToolsOutput Formats
TextClassification, NER/entity linking, extraction, summarization scoring, multilingual QA pairsAbaka ForgeJSONL, Parquet, CSV, TSV, plain text
LLM RLHFPairwise preferences, rubric grading, multi-turn chat eval, safety policy tagging, adversarial prompt setsAbaka ForgeJSONL, Parquet, CSV, evaluation-ready score tables
ImageBounding boxes, polygons, instance/semantic segmentation, keypoints, OCR regionsAbaka ForgeCOCO JSON, VOC XML, JSON, CSV
VideoObject tracking, temporal events, action labels, frame-level segmentation, dense captionsAbaka ForgeJSON, CSV, COCO-style video annotations, frame-indexed manifests
3D/4D Point Cloud3D cuboids, trajectories, lane geometry, drivable space, scenario tagsAbaka ForgeJSON, CSV, sequence manifests, PCD-linked metadata
LiDAR + Camera fusionSensor synchronization, 2D–3D association, multi-view cuboids, track consistency checks, calibration-aware QAAbaka ForgeJSON, CSV, synchronized sequence manifests, per-frame metadata packages
AudioTranscription, speaker diarization, intent labels, sentiment, multilingual TTS QAAbaka ForgeJSON, CSV, TextGrid, WAV+annotation manifests

Success Story

A frontier model lab shipping multimodal assistants
Challenge

The team needed a training data generation partner to expand instruction data and RLHF evaluation sets across multiple languages while keeping policy consistency. Their internal labeling couldn’t keep up with weekly model releases, and quality drift appeared as guidelines changed. They also needed provable provenance and secure operations because datasets touched sensitive product behaviors. The goal was to stand up a repeatable pipeline that could deliver new batches on schedule and withstand rigorous internal review.

Approach

Abaka built a rubric-driven workflow in Abaka Forge spanning instruction following, safety categories, and multi-turn conversation evaluation. We staffed a blended workforce: generalist annotators for scale and scholar-network specialists for high-stakes judgments (coding, math, and policy-sensitive cases). Multi-layer QA included gold sets, reviewer escalation, and batch drift monitoring, with weekly guideline versioning. Deliverables were shipped in JSONL/Parquet formats with clear metadata, audit logs, and ownership guarantees—never repurposed beyond the customer’s program.

Results

Within 3 weeks, the lab approved the pilot and transitioned into steady-state weekly deliveries. The dataset pipeline increased evaluation coverage across languages, reduced rework from inconsistent judgments, and improved regression detection through standardized scoring. Over the first month, the program hit a sustained cadence for new RLHF and eval batches while maintaining target quality gates. Outcomes included 2–3 week turnaround for new task specs, up to 99% accuracy on suitable rubric-checked tasks, and a measurable reduction in review backlogs by 40%.

3 weeks
Pilot-to-production timeline
40%
Reduction in review backlog
Weekly
New RLHF + eval batch cadence

By the Numbers

2019
Founded — trustworthy data partner for frontier AI
1,000+
Enterprise and research customers served
50+
Countries covered for multilingual data work
99%
Accuracy target on suitable QA-validated tasks

What Customers Say

Abaka helped us turn vague “we need better data” requests into a clear rubric, a pilot batch, and a weekly delivery rhythm. The QA reporting made it easy to see where disagreements came from and how guideline updates changed outcomes.

Director of Applied MLConsumer AI Product Company

We needed expert reviewers for coding and math tasks, not just raw throughput. The escalation path and gold-set calibration reduced rework dramatically, and the deliverables were consistently formatted for our training pipeline.

Head of Data OperationsFrontier Model Lab

Security and provenance were non-negotiable. Abaka’s segregated workflow and documentation gave our compliance team confidence, and we were able to move fast without compromising internal controls.

Security Program ManagerEnterprise Software Company

We’ve worked with multiple vendors, and the difference here was operational discipline—clear specs, predictable cadence, and thoughtful handling of edge cases. The weekly reviews kept the data aligned with our release schedule.

ML Platform LeadRobotics Company

Why Choose Abaka

01

A data partner that protects your competitive edge.

Abaka is built for teams training frontier systems: secure-by-default pipelines, scholar-grade reviewers, and delivery discipline that holds up under internal audit. We never build models that compete with you—your data is exclusively yours and is never repurposed, resold, or shared. Founded in 2019 and self-funded and profitable, Abaka operates without acquisition pressure, so your program stays stable across long horizons and evolving requirements.

02

Multi-layer QA you can measure

Gold sets, reviewer escalation, batch drift monitoring, and structured error analysis reduce label noise and prevent quality decay as you scale volume and languages.

03

Abaka Forge for end-to-end execution

Run collection, cleaning, annotation, RLHF, and evaluation in one workflow. Abaka Forge supports all major data types and accelerates production with automation where appropriate.

04

Compliance-aligned operations

Work with SOC 2 and ISO 27001-aligned practices plus GDPR/CCPA readiness, strict NDAs, and segregated secure pipelines designed to satisfy enterprise security reviews.

05

Specialists for hard domains

Tap expert reviewers across medicine, law, mathematics (including Lean4), coding, and science to label and evaluate tasks where small mistakes create large model failures.

06

Predictable cadence from pilot to production

Start with a 2–3 week pilot, then scale into weekly deliveries with versioned rubrics, transparent throughput, and clear change control. You get steady-state output without internal hiring churn.

Frequently Asked Questions

How much does a training data generation partner cost?
Pricing depends on modality, complexity, and the level of expert review required, but Abaka offers transparent unit economics so you can forecast spend. Common rates include $18/hr for LLM math/coding work, $12/hr for STEM generalists, $6/hr for dense captioning, and $3/km for road lane annotation. For evaluation programs, examples include $8/eval for red teaming and $12/eval for math capabilities. We’ll propose a pilot budget and a steady-state plan based on your throughput and QA targets.
How fast can you deliver a pilot dataset?
Most teams can start with a pilot in 2–3 weeks after scoping. Day 0–3 is typically used to finalize the rubric, acceptance criteria, and secure onboarding. Weeks 1–2 focus on producing a representative batch and calibrating QA (gold sets, reviewer escalation, and disagreement analysis). By Week 2–3, we iterate on guidelines and confirm delivery formats so the pilot results are training-ready and reproducible in your pipeline.
What modalities and output formats do you support for training data generation?
Abaka supports text, LLM RLHF, images, video, audio, 3D/4D point cloud, and LiDAR + camera fusion workflows. Outputs are delivered in practical formats for ML stacks such as JSONL, Parquet, CSV/TSV for text and RLHF, and common vision structures like COCO JSON or VOC XML. For 3D and sensor data, we deliver structured JSON/CSV metadata with sequence manifests and coordinate-frame conventions. We’ll align on exact formats during scoping to match your training and evaluation tooling.
What accuracy levels can you achieve for labeling and RLHF work?
Accuracy depends on the task definition and ambiguity, but Abaka is designed to hit high, measurable quality targets through calibrated rubrics and multi-layer QA. On suitable tasks with clear acceptance criteria, programs commonly target up to 99% accuracy using gold sets, reviewer audits, and batch-level drift monitoring. For RLHF and evaluation, we prioritize consistency and reproducibility by training raters against policy-aligned rubrics and routing uncertain cases to senior reviewers. You’ll see quality metrics and error analysis in every delivery cycle.
How do you handle security, NDAs, and compliance requirements?
Abaka runs strict NDAs, segregated secure pipelines, and controlled access workflows designed for enterprise security reviews. Our compliance posture aligns with SOC 2 and ISO 27001 practices and supports GDPR and CCPA requirements. We can tailor data handling to your constraints, including limiting access by role, enforcing reviewer escalation paths, and maintaining audit logs for changes and approvals. Just as important: your data remains exclusively yours—never repurposed, resold, or shared.
Can you support multilingual training data generation?
Yes. Abaka supports multilingual datasets with coverage across 50+ countries, including labeling, RLHF, and evaluation workflows. We localize rubrics—not just translations—so definitions remain consistent across languages and cultural contexts. For multilingual programs, we typically include language-specific reviewer checks and cross-language calibration to reduce drift. Outputs can be delivered as language-partitioned JSONL/Parquet or combined datasets with language metadata, depending on how your training pipeline samples and balances languages.
How are you different from other data labeling vendors?
Abaka is built for frontier AI teams that need trust, not just throughput. We combine platform execution (Abaka Forge), multi-layer QA, and domain-specialist reviewers for tasks like coding, math, medicine, and law. We also emphasize governance: secure pipelines, provenance, and clear change control so your datasets remain reproducible across model releases. Finally, Abaka never builds models that compete with you—your data is exclusive to your program and is never reused for someone else’s benefit.
What happens if we change the labeling guidelines mid-project?
Change is expected—especially for RLHF and evolving product requirements. We use versioned guidelines and a controlled change-request process: you approve the updated rubric, we run a calibration batch, and we quantify how the new definitions shift distributions and scores. If relabeling is needed, we recommend the smallest rework set that preserves training integrity (for example, only affected classes or high-impact slices). You’ll receive updated documentation and a clear mapping between versions so your team can reproduce experiments.
Can we start with a small pilot before committing to production?
Yes—most engagements start with a pilot designed to validate task definitions, QA thresholds, and delivery formats. In the pilot, we produce a representative batch, measure disagreement points, and refine rubrics until the outputs meet acceptance criteria. The pilot also establishes operational cadence, reporting structure, and unit economics so you can plan scale-up confidently. If the pilot meets targets, we expand the same workflow into steady-state production without retooling.
Who owns the training data and annotations you produce?
You do. Abaka’s operating principle is that your data is exclusively yours—never repurposed, resold, or shared. Deliverables, guidelines, and output formats are produced for your program and remain under your control. We also prioritize IP provenance in collection workflows to reduce copyright risk, and we maintain audit-friendly documentation so you can track how datasets were built and updated over time.
What tooling do you use to manage data generation and QA?
We use Abaka Forge, an all-in-one platform that supports collection, cleaning, annotation, RLHF, evaluation, and production workflows across text, image, video, audio, and 3D/4D point cloud. The platform supports automation to speed up repetitive steps while keeping humans as the final authority for correctness. It also enables versioning, role-based operations, and QA reporting so your team can review progress, spot drift, and approve changes with confidence.
Is there a minimum dataset size or minimum engagement?
There isn’t a one-size minimum, but most teams see the best results when the first engagement is large enough to validate edge cases and QA behavior—typically a pilot batch followed by a ramp plan. For smaller needs, we can propose a scoped evaluation set or targeted labeling sprint. For larger programs, we set up steady-state weekly deliveries with throughput and quality SLAs aligned to your roadmap. Talk to an Expert and we’ll recommend a right-sized pilot that matches your constraints.

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Annotate the Present. Train the Future.

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