Talk to an Expert
Talk to an Expert

Scale high-trust datasets with a
Training Data Generation Vendor

Abaka delivers multimodal data collection, annotation, and RLHF with multi-layer QA, secure pipelines, and predictable throughput—so your team trains, evaluates, and ships faster.

Talk to an Expert

When training data generation is ad hoc, quality drifts quietly—then shows up as failed evals, brittle agents, and expensive retraining cycles. Teams lose weeks to rework: mislabeled edge cases, inconsistent taxonomies, and duplicated annotation across vendors. At scale, even a 2% error rate can cascade into thousands of bad examples, and “quick fixes” (spot checks or one-off contractors) often miss systematic issues like class imbalance, prompt leakage, or policy violations. The result is delayed launches, higher inference risk, and wasted spend on data you can’t confidently trace.

Abaka makes training data generation a repeatable production system. You get a single partner for collection, cleaning, annotation, and evaluation-ready packaging—backed by SOC 2, ISO 27001, GDPR, and CCPA controls, strict NDAs, and full IP provenance (0% copyright risk on collected data). With Abaka Forge, your team standardizes guidelines, automates parts of the workflow with large-model assistance, and applies multi-layer QA so every batch is measurable, auditable, and aligned to the exact failure modes your model must improve.

The Training Data Generation Vendor Bottleneck

01

Quality Decay

Early batches look good, then quality erodes as scope expands—new annotators interpret guidelines differently, edge cases multiply, and the label ontology drifts. A 99% target accuracy requires more than random spot checks; it needs calibrated rubrics, double-pass review, and disagreement analysis. Abaka enforces throughput discipline (up to 500 files/day per annotator) so speed doesn’t silently trade off against correctness, and your team gets consistent QA signals per batch—before bad labels contaminate fine-tunes and force multi-week relabeling.

02

Volume Walls

Most teams hit a scaling cliff: pilots succeed, then production collapses under volume—more modalities, more locales, more edge cases. Recruiting, onboarding, and auditing a distributed workforce can take 2–3 weeks alone, and each new toolchain adds friction. Abaka provides 1M+ vertically specialized annotators across 50+ countries, plus an operations layer that can scale up/down without breaking taxonomies, guidelines, or delivery cadence—so you can increase coverage without creating a new bottleneck every sprint.

03

Compliance Friction

Security and provenance can stall data programs longer than labeling itself. Legal review, access controls, and audit trails become complex when multiple vendors and spreadsheets are involved—especially with sensitive prompts, internal documents, or customer conversations. Abaka runs segregated secure pipelines with strict NDAs and modern compliance (SOC 2, ISO 27001, GDPR, CCPA), and maintains full IP provenance so your datasets are defensible. That reduces approval cycles and avoids costly re-collection when data lineage can’t be proven.

01

Data spec, taxonomy, and QA design

Turn your model goals into an executable data plan: label taxonomy, edge-case catalog, acceptance criteria, and sampling strategy. Abaka helps define gold sets, reviewer rubrics, and multi-layer QA so outputs are measurable (not subjective). We support LLM instruction datasets, domain QA (math, coding, law, medicine), and multimodal tasks like interleaved images + text. Your team gets a living spec that stays stable as requirements change, reducing relabel churn and keeping evaluation alignment tight.

02

Custom data collection with provenance controls

Source or capture training inputs that match your deployment reality—text, image, video, LiDAR, and IoT sensor streams—curated, timestamped, tagged, and pre-filtered. Abaka supports on-demand custom capture pods for real-world scenarios and delivers 0% copyright risk on collected data via full IP provenance. When teams currently lose time cleaning raw feeds, Abaka’s collection-to-curation workflow can cut preprocessing time by up to 70% so you train sooner with less internal wrangling.

03

Instruction, reasoning, and domain QA generation

Generate high-signal text datasets: instruction-following, chain-of-thought-style reasoning tasks (when appropriate for your policy), and expert QAs across domains like mathematics, coding, business, and science. Abaka’s scholar-network reviewers validate difficulty, prevent leakage, and enforce formatting constraints for training and eval. Deliverables include JSONL/CSV with rich metadata (difficulty, topic, failure-mode tags), making it easier to run targeted fine-tunes and analyze regressions.

04

RLHF: preferences, ranking, and safety review

Build RLHF datasets that improve helpfulness without compromising safety. Abaka supports pairwise preference labeling, ranking, rubric-based scoring, and human evaluation for instruction following, refusal behavior, and policy adherence. We align annotation guidelines to your system prompt and policy language, and use multi-layer QA to reduce inconsistency across raters. Outputs are packaged for common RLHF pipelines and can be paired with targeted red-team sets to validate robustness before release.

05

Image annotation and dense captioning at scale

Create vision datasets for detection, segmentation, and captioning with consistent class definitions and audit-ready QA. Abaka supports dense captioning, attribute tagging, and specialized workflows such as image editing tasks for model training. Deliverables include COCO-style JSON, Pascal VOC XML, and masks (PNG), plus metadata for sampling and error analysis. This is used across retail catalog intelligence, medical imaging support (where permitted by your governance), and robotics perception pipelines.

06

Video temporal labeling and spatial reasoning tasks

Produce training and evaluation data for video understanding: temporal segments, object tracks, event labeling, and video spatial reasoning tasks. Abaka structures guidelines to handle occlusion, scene cuts, and ambiguous motion, then applies reviewer passes to stabilize labels across long sequences. Outputs can include frame-level annotations, clip-level labels, and captions in JSON/CSV formats, enabling reliable fine-tunes for agent perception, safety monitoring, and industrial inspection use cases.

07

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

Support 3D/4D perception with cuboids, segmentation, and object attributes in point clouds—plus consistent ontology management for long-running programs. Abaka handles indoor scenes for embodied robotics and outdoor scenes for autonomy, with QA protocols designed for sparse points, range artifacts, and class confusion. Deliverables include common point-cloud annotation exports and calibration metadata. When paired with LiDAR + camera fusion labeling, you can train models that remain stable across sensor domain shifts.

08

Human evaluation and red-teaming for release readiness

Move from “looks good” to measured readiness using Abaka’s model evaluation methods: objective benchmarks, model-as-judge where appropriate, and human evaluation. We cover a 6-dimension framework—Accuracy & Precision, Robustness & Reliability, Efficiency & Scalability, Safety & Bias Audits, Tool & Function Calling, and User Interaction & Usability. Your team receives scored outputs, failure-mode clustering, and prioritized fix lists so the next data batch directly targets the issues that matter.

Why Outsource Training Data Generation Vendor Work

01

Faster Delivery

Skip the hiring and tooling ramp. Abaka can stand up a scoped pilot in days, then expand to production with stable guidelines and QA. Teams commonly compress multi-sprint data work into a 2–3 week delivery window by standardizing workflows in Abaka Forge and using trained reviewers to keep decisions consistent as volume grows.

02

Direct Savings

Reduce rework and internal coordination cost. Instead of managing multiple vendors, spreadsheets, and audits, you get a single accountable pipeline with clear acceptance criteria. Pricing can align to your task type—hourly expert labeling, per-eval evaluation, or per-km lane annotation—so spend maps cleanly to output.

03

Risk Reduction

Lower security, privacy, and provenance risk with SOC 2 and ISO 27001 controls, strict NDAs, and segregated secure pipelines. Abaka maintains full IP provenance and does not repurpose or resell your data—so your datasets remain defensible as you scale to sensitive domains and larger teams.

04

Elastic Scalability

Scale up for launches and scale down after—without breaking quality. With 1M+ specialized annotators across 50+ countries, Abaka can add capacity while enforcing throughput limits (500 files/day per annotator) and QA gates, keeping outputs consistent when scope expands to new locales and modalities.

05

Domain Expertise

Match tasks to reviewers who understand the content. Abaka supports scholar-network domains including mathematics, coding, medicine, science, business, and law, enabling higher-quality instruction data, reasoning datasets, and human evaluations—especially where subtle correctness matters more than label volume.

06

Innovation Velocity

Use Abaka Forge to accelerate iteration. Large-model automation can speed parts of collection, cleaning, and annotation by up to 50x, while humans validate edge cases and hard negatives. That shortens the loop between model failures, targeted data creation, and measurable improvements.

Industries We Serve

Automotive

Build dependable perception datasets for ADAS and autonomy: lane and drivable-area programs, long-tail scenario coverage, and sensor metadata packaging. Abaka supports LiDAR, camera, and fused labeling with consistent ontologies and QA. Your team gets deliverables structured for training and regression testing, plus the ability to scale labeling volume without sacrificing review rigor.

GenAI / Foundation Models

Generate instruction, reasoning, and preference data for fine-tuning and alignment—plus human evaluation for release gating. Abaka’s scholar-network coverage supports math and coding tasks as well as multilingual instruction. Outputs come with metadata for difficulty and failure modes, enabling targeted training runs instead of broad, expensive retrains.

Embodied AI / Robotics

Support robot perception and agent learning with 3D indoor scene labeling, video spatial reasoning, and task-driven datasets that reflect real-world interactions. Abaka can also design custom RL environments for agent capability development. The result is training data that maps to the behaviors you need—navigation, manipulation, and safety—without brittle, one-off pipelines.

Healthcare

Create high-quality datasets for medical AI support where permitted by your governance: de-identification workflows, structured labeling guidelines, and expert QA for clinical text and imaging tasks. Abaka’s secure pipelines and audit trails help teams operate under strict compliance requirements while keeping the data lifecycle controlled, documented, and reproducible.

Retail

Improve catalog intelligence and customer experiences using image tagging, attribute extraction, product matching, and moderation datasets. Abaka delivers dense captioning and segmentation outputs suitable for search, recommendations, and visual QA. With consistent taxonomies and reviewer calibration, you reduce noisy labels that cause misclassification and poor relevance.

Finance

Support document understanding, customer support automation, and risk operations with structured text datasets and human evaluation. Abaka can generate domain-specific QAs and instruction sets, then validate factuality and policy adherence via rubric-based review. Secure workflows reduce exposure risk when handling sensitive content and internal knowledge bases.

Geospatial

Produce training data for mapping and earth-observation pipelines: imagery annotation, change detection support, and temporal labeling across large areas. Abaka’s workflows emphasize clear ontology definitions and QA that accounts for seasonal variation, cloud cover, and sensor artifacts. Outputs are delivered in formats that integrate cleanly with GIS and ML stacks.

Security / Defense

Generate controlled, auditable datasets for perception, monitoring, and language tasks under strict access controls. Abaka provides segregated secure pipelines, NDAs, and compliance frameworks that support sensitive programs. Human evaluation and red-teaming can also be used to test model behavior under adversarial prompts and operational constraints.

Agriculture / Industrial

Improve inspection, automation, and forecasting with image/video labeling and sensor-aligned datasets. Abaka can label defects, growth stages, equipment states, and environmental conditions with consistent rubrics and QA. The programmatic approach reduces relabeling and helps your team scale from pilot to production without introducing drift in definitions.

How It Works

1) Day 0–3 — Scope, specs, and secure setup

We align on your target model behaviors, failure modes, and acceptance criteria, then translate them into dataset specs—ontology, rubrics, and sampling plan. Abaka sets up secure access, segregated pipelines, and NDA controls. You approve a small gold set and annotation playbook in Abaka Forge so production starts with measurable standards, not subjective interpretations.

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

We run a pilot that stress-tests edge cases and guideline clarity. Reviewers measure agreement, surface ambiguity, and propose rubric updates. Your team receives pilot outputs with QA reports and examples of common failure patterns. We iterate quickly—tightening definitions, adding counterexamples, and adjusting sampling—so the next batch improves both label consistency and model usefulness.

3) Week 2–3 — Production ramp with multi-layer QA

Abaka scales throughput using specialized annotators while maintaining QA gates: spot checks, second-pass review, and targeted audits on high-risk classes. We enforce throughput constraints (up to 500 files/day per annotator) to protect quality. Deliverables are packaged in your required formats with metadata, versioning, and batch-level acceptance summaries.

4) Ongoing — Continuous refresh and hard-negative mining

As your model evolves, we refresh datasets to track new failure modes: long-tail edge cases, adversarial prompts, and domain drift. We help you design hard-negative and counterfactual sets, then feed them into the next data iteration. The workflow stays stable while content changes—so you don’t rebuild pipelines each time priorities shift.

5) Weekly — Reporting, governance, and change control

Each week you get delivery reports: volume, QA metrics, disagreement drivers, and sampling coverage. Change requests are handled through controlled versioning—updated rubrics, new classes, or new locales—without breaking historical continuity. This keeps your training data generation program auditable and predictable across sprints and stakeholders.

Modality & Format Coverage

Train across modalities without juggling vendors. Abaka Forge standardizes guidelines, QA, and exports so your team can combine text, RLHF, vision, and 3D datasets into one measurable pipeline.

ModalityAnnotation TypesToolsOutput Formats
TextInstruction following, domain QA, classification, entity extraction, safety policy checksAbaka ForgeJSONL, CSV, TSV, YAML
LLM RLHFPairwise preferences, ranking, rubric scoring, refusal/safety review, human evaluationAbaka ForgeJSONL, CSV, Parquet, eval scorecards
ImageBounding boxes, polygons, segmentation masks, dense captioning, attribute taggingAbaka ForgeCOCO JSON, Pascal VOC XML, PNG masks, CSV
VideoTemporal segments, event labeling, object tracking, frame-level tags, video captioningAbaka ForgeJSON, CSV, frame-indexed manifests, clip metadata
3D/4D Point Cloud3D cuboids, point segmentation, object attributes, trajectory labeling, scene taggingAbaka ForgeJSON, PCD-aligned exports, calibration metadata, CSV
LiDAR + Camera fusionSensor fusion labeling, cross-view consistency checks, occlusion handling, track alignmentAbaka ForgeJSON, synchronized frame manifests, calibration files, CSV
AudioTranscription, speaker labeling, intent classification, QA scoring, TTS dataset preparationAbaka ForgeWAV+JSON, TextGrid, CSV, JSONL

Success Story

A frontier model lab
Challenge

The team needed a training data generation vendor to expand instruction and evaluation coverage across math, coding, and safety behaviors—without creating provenance or security risk. Their internal pipeline produced inconsistent rubrics across reviewers, making it hard to compare model versions. They also faced a scaling problem: pilots were strong, but production batches drifted as new annotators joined, and the team spent weeks triaging disagreements rather than training. They required a partner that could operationalize guidelines, stabilize QA, and deliver predictable weekly batches aligned to known failure modes.

Approach

Abaka implemented a rubric-first workflow in Abaka Forge: taxonomy design, gold sets, calibrated reviewer training, and multi-layer QA. We sourced vertically specialized annotators for math and coding tasks, then added a separate safety review lane for refusal behavior and policy adherence. The program used a pilot-to-production ramp: small batches to uncover ambiguity, then versioned guideline updates with clear change control. Deliverables were exported as structured JSONL with metadata tags (topic, difficulty, failure-mode) so the lab could fine-tune targeted capabilities and run consistent human evaluations across releases.

Results

Within 3 weeks, the lab transitioned from ad hoc labeling to a repeatable data production system with stable rubrics, auditable QA, and weekly delivery cadence. The new datasets reduced disagreement-driven rework, improved coverage of hard negatives, and made evaluation results comparable across model versions. The team achieved 99% accuracy on audited samples for the defined rubric scope, expanded to multilingual instruction coverage, and cut preprocessing and packaging time by 70% through standardized exports and workflow automation.

3 weeks
Pilot-to-production ramp for the first program lane
99%
Audited accuracy on rubric-scoped tasks
70%
Reduction in preprocessing and packaging time

By the Numbers

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

What Customers Say

We came in with a messy mix of guidelines and outputs from different teams. Abaka helped us standardize the rubric, build a gold set, and deliver batches we could actually trust. The QA reporting made it obvious where disagreements were coming from, and we stopped wasting cycles on relabeling.

Director of Applied MLFrontier AI Research Team

The biggest win was operational reliability. We needed a vendor that could scale volume without letting quality drift. Abaka’s reviewer layer and change control meant our taxonomy stayed stable, and each delivery came with clear acceptance signals rather than guesswork.

Head of Data OperationsEnterprise GenAI Platform

Security review was a blocker for us. Abaka’s secure pipelines, NDAs, and compliance posture shortened approvals and reduced internal overhead. We also valued the clear stance that our data remains exclusively ours and is never repurposed or shared.

Security & Compliance LeadRegulated Technology Company

We used Abaka Forge to consolidate collection, annotation, and evaluation workflows. The exports were consistent, the metadata was useful for slicing failures, and automation sped up the boring parts while humans focused on edge cases. Our iteration loop got noticeably tighter.

ML Engineering ManagerRobotics & Automation Company

Why Choose Abaka

01

A trustworthy data partner that never competes with you.

Abaka is built for teams that need high-trust training data generation at scale—without compromising ownership, provenance, or security. We never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. With SOC 2 and ISO 27001 controls, strict NDAs, and segregated secure pipelines, you can run sensitive programs confidently. Combine that with Abaka Forge’s end-to-end workflow and multi-layer QA, and you get datasets your team can ship and defend.

02

Compliance-first execution

SOC 2, ISO 27001, GDPR, and CCPA alignment—plus audit-ready workflows and controlled access—so security reviews don’t stall your roadmap. We maintain full IP provenance for collected data (0% copyright risk).

03

Specialists for hard domains

Use scholar-network domains (math, coding, medicine, science, business, law) for instruction data, reasoning tasks, and evaluation. That raises correctness on subtle problems where generalist labeling breaks down.

04

Abaka Forge — one workflow for multimodal data

Run collection, cleaning, annotation, and evaluation in one system. Abaka Forge supports image, video, text, RLHF, and 3D/4D point cloud workflows, with automation that can accelerate parts of the pipeline by up to 50x.

05

Quality you can measure, not hope for

Multi-layer QA, calibrated rubrics, and batch-level reporting help you hit targets like 99% accuracy on scoped tasks. You get clearer acceptance criteria, fewer disagreements, and less relabel churn across sprints.

06

Global scale with controlled throughput

Scale across 50+ countries with 1M+ specialized annotators while protecting consistency. We enforce throughput limits (500 files/day per annotator) to reduce fatigue-driven errors and keep labels stable as volume ramps.

Frequently Asked Questions

How much does a training data generation vendor cost?
Cost depends on modality, complexity, and the level of expertise required. Abaka pricing can be structured around real unit economics: expert LLM Math/Coding work is $18/hr, STEM generalist labeling is $12/hr, dense captioning is $6/hr, image editing is $8/hr, and road lane annotation can be $3/km. For evaluation programs, examples include Red Teaming at $8/eval and Math Capabilities at $12/eval. We’ll scope your rubric, QA depth, and weekly volume to produce a transparent estimate tied to deliverables.
How fast can you start and deliver the first batch?
Most teams can start within days once scope and secure access are approved. A typical timeline is Day 0–3 for specs, guidelines, and environment setup, then Week 1–2 for a pilot batch and calibration, followed by Week 2–3 for production ramp. If you already have a stable taxonomy and sample data, we can accelerate by reusing your existing rubric and focusing the pilot on edge cases and QA calibration. Delivery cadence is then set weekly for predictable iteration.
What data modalities and export formats do you support?
Abaka supports text, RLHF, image, video, 3D/4D point cloud, LiDAR + camera fusion, and audio programs through Abaka Forge. We deliver common ML-friendly formats such as JSONL, CSV/TSV, COCO-style JSON, Pascal VOC XML, PNG masks, and synchronized manifests with metadata. If your pipeline requires specific schemas, we can map outputs to your required fields and add versioning so training and evaluation teams can reproduce experiments across releases.
What accuracy can you guarantee for generated training data?
Accuracy targets depend on the task definition, ambiguity level, and rubric maturity. Abaka is built to operate at high standards—99% accuracy is achievable for well-scoped tasks when guidelines are unambiguous and QA is properly layered. We implement gold sets, reviewer calibration, disagreement analysis, and multi-layer QA to prevent drift. Rather than relying on vague “quality checks,” we define acceptance criteria up front and report batch-level quality so you can decide whether to ship, revise, or expand the rubric.
How do you handle security, privacy, and compliance?
Abaka operates with SOC 2 and ISO 27001 controls and supports GDPR and CCPA requirements. We use strict NDAs, segregated secure pipelines, and controlled access so only authorized personnel see sensitive data. We also maintain full IP provenance and do not repurpose, resell, or share your datasets—your data remains exclusively yours. If your team needs additional governance steps (redaction, access logging, or role-based review lanes), we’ll design them into the workflow from Day 0–3.
Can you generate multilingual training data?
Yes. Abaka supports global programs across 50+ countries and can generate or annotate multilingual datasets for instruction following, classification, transcription, and evaluation. We align language-specific guidelines to your taxonomy and run reviewer calibration to ensure consistency across locales. Outputs include language and locale metadata to help you evaluate performance by region. For sensitive domains, we can restrict work to specific geographies and apply secure pipeline controls so multilingual expansion doesn’t increase compliance risk.
How are you different from other data labeling companies or marketplaces?
Abaka is designed as a trustworthy data partner for frontier AI, not a gig marketplace. You get a production system: rubric design, specialized annotators, multi-layer QA, and structured exports in Abaka Forge—plus compliance controls and full provenance. We also never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. This reduces IP and strategy risk while giving your team repeatable delivery and measurable quality.
How do change requests work if our taxonomy evolves mid-project?
Change is expected—new edge cases, new policy language, new labels. Abaka handles changes through versioned guidelines and controlled rollout: we update the rubric, retrain impacted annotators, and run a short recalibration batch to validate agreement. We can also backfill historical data if required, while keeping lineage clear so your training runs remain reproducible. Weekly reporting highlights where changes affect quality, volume, or timelines, helping you prioritize what to update versus what to defer.
Can we run a pilot before committing to a larger engagement?
Yes. We recommend a pilot to validate rubric clarity, QA depth, and operational fit. Pilots typically focus on the highest-value failure modes—hard negatives, safety behaviors, or domain correctness—so you can measure impact quickly. You’ll receive pilot outputs, QA reporting, and concrete recommendations for scaling. If the pilot meets acceptance criteria, we ramp capacity without changing the core workflow, ensuring the production phase doesn’t introduce drift or tooling surprises.
Who owns the data and can you reuse it for other customers?
You own your data and outputs. Abaka’s policy is explicit: we never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. We maintain provenance and lineage so you can audit what was collected or labeled, when it was processed, and under which guidelines. If you provide third-party inputs, we can help document permissions and ensure outputs are governed under your required contractual terms.
What tooling do you use for training data generation and QA?
Abaka uses Abaka Forge—an all-in-one platform for collection, cleaning, annotation, training workflows, and production handoff across image, video, text, RLHF, and 3D/4D point cloud. The platform supports guideline management, reviewer workflows, and structured exports, with large-model automation that can accelerate parts of the pipeline by up to 50x. For teams that already have internal tools, we can integrate via export schemas and delivery manifests so you can keep your existing training stack.
What is the minimum project size you can take on?
Minimum size depends on complexity and the amount of setup required for secure access, rubrics, and QA. Many teams start with a focused pilot (for example, a few thousand items or a narrow set of high-risk edge cases) to validate workflow and quality gates. If you only need a small dataset, we’ll still apply the same principles—clear acceptance criteria, calibrated reviewers, and versioned guidelines—so the outputs remain reliable and reusable if you later scale to weekly production.

Ready to Get Started?

Label the Present. Train the Future.

Talk to an Expert