AI Training Datasets
for foundation-model quality and scale

Source, curate, and label text, image, video, audio, and 3D datasets with strict IP provenance, SOC 2/ISO 27001 controls, and delivery formats your stack already supports.

Talk to an Expert

If your model quality is stalling, the bottleneck is rarely “more GPUs”—it’s that your AI training datasets are inconsistent, under-specified, or drifting from your target distribution. Teams often spend 6–10 weeks just wrangling sources, writing labeling rules, and redoing failed batches. That delay compounds: every retrain cycle slips, and roadmap features get cut because evaluation and feedback loops cannot keep up.

The cost of inaction shows up as measurable waste: a single relabel can consume 20–40% of a sprint, and data rework can force you to throw away entire training runs. When dataset provenance is unclear, legal review becomes a gating function—turning days into weeks. Abaka helps you move from ad-hoc data pulling to reliable, production-grade datasets delivered on schedule and ready for training.

The AI Training Datasets Bottleneck in AI Development

Quality Decay

Dataset quality decays faster than most teams expect. As product requirements evolve (new intents, new edge cases, new sensors), yesterday’s “good enough” labels become today’s failure mode. A common pattern is 10–25% of samples silently drifting out of spec after guideline changes, tool updates, or new annotator cohorts. Without tight versioning, gold sets, and multi-layer QA, your training set turns into a mixture of definitions—making loss curves look fine while real-world behavior regresses.

Volume Walls

Even when your taxonomy is correct, volume becomes the constraint. Internal teams hit a ceiling on throughput, especially when you need multi-step workflows (collection → cleaning → labeling → review → export). At scale, every “small” decision—like an extra attribute on a bounding box—multiplies hours across tens of thousands of items. Abaka supports large-scale delivery with 1M+ vertically specialized annotators across 50+ countries and a throughput ceiling of 500 files/day per annotator to keep quality sustainable under load.

Compliance Friction

Compliance and IP provenance can add weeks if you’re relying on unclear data sources or fragmented vendors. Security reviews often require evidence: access controls, audit trails, segregated pipelines, and NDAs. Meanwhile, unclear ownership can block training entirely. Abaka operates with SOC 2 and ISO 27001 controls plus GDPR and CCPA alignment, and provides full IP provenance with 0% copyright risk on collected data—so your team can move from “maybe usable” to “approved to train” without last-minute legal surprises.

Custom dataset sourcing with IP provenance

Build AI training datasets from the ground up—without guesswork on ownership. Abaka supports custom sourcing and curated datasets spanning text, image, video, audio, and 3D. For custom capture, we can organize on-demand collection that is timestamped, tagged, and curated so you can match your target distribution (geography, lighting, device type, language, domain). You get clear provenance and controlled access, which helps security and legal review move faster and reduces the risk of training on questionable web-scraped material.

Cleaning, normalization, and dedup for training readiness

Raw data is rarely train-ready. We run practical curation steps your engineers expect: format normalization (UTF-8 text, consistent frame rates, audio sample rates), deduplication, spam and toxicity filtering, and label-set harmonization across batches. Your team can define acceptance thresholds (e.g., maximum blur, minimum signal-to-noise, language ID confidence). This reduces avoidable training instability and makes evaluation results interpretable because your dataset distribution is controlled rather than accidental.

Expert annotation across text, vision, audio, and 3D

Abaka delivers labeling and annotation for the modalities that matter in modern AI training datasets: dense captioning, classification, entity and relation extraction, segmentation, keypoints, tracking, and 3D/4D point cloud labeling. For LLM-focused programs, we support instruction tuning data, reasoning-heavy QA, coding and math tasks (including Lean4), and high-level evaluation questions. Scholar-network reviewers cover domains like medicine, law, languages, and mathematics so you can push beyond generic labels and into expert-grade supervision.

RLHF and preference data pipelines

When your model needs better instruction-following, safety, or style alignment, RLHF data becomes central. We support pairwise ranking, rubric-based scoring, multi-turn conversation grading, and structured feedback for tool/function calling. Deliverables can include ranked responses, reason tags, and failure-mode taxonomies that plug into your reward modeling and DPO-style training flows. You can also combine RLHF with targeted collection (e.g., domain prompts, policy edge cases) to keep alignment data close to your product surface.

Multimodal dataset assembly for foundation model training

Modern training often depends on paired or interleaved modalities: image+text pairs, video+caption, audio+transcript, and 3D scenes with semantic layers. Abaka assembles multimodal training sets with consistent IDs, cross-file linking, and schema validation so your training code can load without bespoke glue. We support interleaved image tasks and video spatial reasoning workflows, which are especially useful when you need models to ground language in visual context or predict actions from temporal cues.

Abaka Forge for workflow control and faster cycles

Abaka Forge is an all-in-one platform for collection, cleaning, annotation, training workflows, and production delivery across image, text, video, 3D/4D point cloud, and RLHF. It is designed to accelerate pipelines with large-model automation (up to 50x faster on appropriate steps), while keeping auditability and review gates intact. Outputs are standardized for downstream ingestion, and the platform helps you manage guideline versions, reviewer queues, and batch-level QA signals.

Evaluation-ready datasets and red-teaming sets

Training data alone is insufficient without eval sets you trust. We build evaluation datasets aligned to your target behaviors and risk profile, including safety and bias audits, robustness sets, and tool/function calling scenarios. Abaka’s evaluation framework spans six dimensions—Accuracy & Precision, Robustness & Reliability, Efficiency & Scalability, Safety & Bias Audits, Tool & Function Calling, and User Interaction & Usability—so your team can measure improvements that matter, not just leaderboard metrics.

Export formats and integration into your stack

AI training datasets must land cleanly in your storage and training pipelines. We deliver in practical formats like JSONL, CSV, Parquet, COCO-style JSON, PNG/JPEG with sidecar labels, KITTI-style text where applicable by spec, and structured schemas for RLHF (prompt, candidates, preference, rationale tags). You can request deterministic file naming, manifest files, checksums, and split definitions (train/val/test) so your ML engineers can reproduce runs and track dataset lineage across experiments.

Why Outsource AI Training Datasets

Faster Delivery

Outsourcing lets you compress timeline risk without sacrificing review rigor. Instead of spending weeks hiring, training, and building internal tooling, you can start with a scoped pilot and scale quickly once the schema stabilizes. Abaka can stand up the workflow, guidelines, QA gates, and delivery format from day one—so your team focuses on modeling and evaluation. The result is shorter iteration cycles and fewer “blocked by data” milestones.

Direct Savings

Dataset work has hidden costs: manager overhead, annotator churn, and the opportunity cost of pulling engineers into labeling tasks. With Abaka, you can choose cost-effective labor bands and task types (from generalist labeling to scholar-grade math/coding). For example, LLM math/coding support is priced at $18/hr, STEM generalists at $12/hr, and dense captioning at $6/hr—so you can match spend to task difficulty instead of paying senior internal time for operational throughput.

Risk Reduction

A single provenance issue can force you to discard a dataset or pause deployment. Abaka is built for trust: strict NDAs, segregated secure pipelines, and compliance controls (SOC 2, ISO 27001, GDPR, CCPA). For collected data, Abaka provides full IP provenance with 0% copyright risk—reducing legal uncertainty and keeping your training runs defensible. You also reduce operational risk through multi-layer QA and controlled throughput limits.

Elastic Scalability

Internal teams struggle to scale up and down as model priorities shift. Outsourcing provides elasticity: ramp a project when you’re approaching a training deadline, then taper as you move into evaluation and refinement. Abaka supports large programs with 1M+ annotators across 50+ countries, while enforcing sustainable throughput guidance (500 files/day per annotator) to avoid quality collapse. This helps you scale volume without losing consistency.

Domain Expertise

General labeling isn’t enough for frontier models and regulated domains. Abaka’s scholar-network domains include automobile, coding, languages, mathematics, medicine, science, business, and law. That matters when your dataset needs expert judgments: medical reasoning QA, legal clause labeling, or competition-grade math solutions. You get a dataset that encodes the right expertise, not just surface-level tags—improving training signal and reducing the number of “we need to relabel this” loops.

Innovation Velocity

Outsourcing becomes a lever for experimentation. You can try multiple dataset strategies—new instruction formats, different rubrics, or alternative negative sampling—without rebuilding operations each time. With Abaka Forge and established workflows for multimodal and RLHF tasks, your team can run controlled dataset A/B tests and iterate on what improves evaluation metrics. This boosts innovation velocity by making data iteration as routine as model iteration.

Industries We Serve

Automotive

Automotive AI depends on training datasets that reflect road reality: lanes, signage, rare maneuvers, weather, and long-tail safety events. Abaka supports road lane annotation priced at $3/km, plus video and sensor workflows that can include tracking, segmentation, and scene understanding. For ADAS and autonomy teams, we emphasize consistency across routes and cities, clear guideline versions, and evaluation splits that reflect operational design domains rather than random sampling.

GenAI / Foundation Models

Foundation-model teams need diverse, high-signal data: instruction tuning, reasoning, creative writing, tool use, and safety coverage. Abaka builds AI training datasets across text, image, video, audio, and multimodal pairings, with scholar-network reviewers for math, coding, and domain expertise. You can commission competition-grade reasoning sets, domain-specific corpora, and RLHF preference datasets that align to your product’s style, refusal rules, and tool/function calling requirements.

Embodied AI / Robotics

Robotics and embodied AI require data that connects perception to action: 3D scenes, navigation cues, temporal video context, and policy-learning feedback. Abaka supports 3D/4D point cloud annotation and can design custom RL environments for real-world agent capability. Training datasets can be structured around tasks like pick-and-place, warehouse navigation, or human-robot interaction, with consistent schemas that make imitation learning and reinforcement learning pipelines easier to maintain.

Healthcare

Healthcare AI benefits from careful domain labeling and rigorous review, especially for medical language understanding, triage assistants, or imaging support tools. Abaka provides domain expertise through scholar-network reviewers in medicine and science, and can build datasets for medical reasoning QA, clinical entity extraction, and imaging annotation workflows (where applicable to your program). Security controls and NDAs support sensitive workflows, while QA gates keep labeling definitions consistent across batches.

Retail

Retail use-cases span search relevance, product categorization, visual matching, and customer support automation. Abaka can produce product text datasets, image classification/segmentation sets, and multimodal product-image-to-description pairs to improve retrieval and recommendation. For conversational assistants, we build instruction data, policy-compliant response sets, and evaluation suites that test accuracy, refusal behavior, and tone adherence—so your assistant performs reliably across peak seasons and catalog changes.

Finance

Financial AI needs high precision and explainability signals: entity extraction from filings, transaction categorization, risk summarization, and compliance-aware assistant behavior. Abaka supports scholar-network expertise in business and law, enabling datasets that capture correct terminology and edge-case reasoning. You can also build evaluation datasets focused on factuality, bias, and refusal rules, ensuring your model’s outputs remain aligned to internal policy and external regulatory expectations.

Geospatial

Geospatial ML relies on imagery and sensor-aligned datasets: land-use classification, change detection, infrastructure mapping, and disaster assessment. Abaka supports image and video annotation workflows and can structure datasets for temporal comparison (before/after) with clear metadata standards. Deliverables can include segmentation masks, object footprints, and attribute schemas, exported in formats your GIS and ML pipelines can ingest without manual transformation.

Security / Defense

Security and defense programs often require strict access control, auditability, and compartmentalization. Abaka supports segregated secure pipelines, strict NDAs, and compliance controls (SOC 2, ISO 27001). Training datasets can cover computer vision detection, multilingual text understanding, or robustness evaluation sets designed to test failure modes under stress. We prioritize provenance, controlled reviewer access, and clear documentation to support internal governance processes.

Agriculture / Industrial

Agriculture and industrial AI teams need datasets that operate in messy, real-world environments: dust, glare, occlusion, seasonal change, and equipment variability. Abaka can assemble image/video datasets for crop health, defect detection, or equipment monitoring, and can expand into 3D where spatial understanding is required. We emphasize edge-case capture, balanced sampling across conditions, and practical output formats so your models generalize beyond a single farm, factory line, or region.

How It Works

1) Day 0–3 — Scope, schema, and success criteria

We start by translating your model goal into dataset requirements: modalities, target distributions, taxonomies, acceptance thresholds, and delivery formats. Your team shares sample data (or requirements for sourcing/capture), plus “what good looks like” via existing evals, failure cases, and product requirements. We then draft labeling guidelines, define QA gates (including gold sets if you have them), and confirm exports (e.g., JSONL for instruction tuning, COCO-style JSON for vision, Parquet for large-scale text).

2) Week 1–2 — Pilot batch and calibration

We run a pilot to validate that definitions are unambiguous and outputs integrate cleanly into your training code. This phase includes annotator calibration, reviewer alignment, and targeted revisions to guidelines based on disagreement analysis. You receive sample exports early to test ingestion and metrics. If you’re building AI training datasets for frontier tasks (math, coding, domain reasoning), we assign appropriate skill bands and add rubric checks so the dataset encodes correct reasoning—not just superficially plausible answers.

3) Week 2–3 — Scale production with QA instrumentation

After pilot sign-off, we scale volume while keeping quality stable through layered QA: spot checks, reviewer queues, and drift monitoring against the approved guideline version. We maintain batch traceability so every item can be audited (who labeled, who reviewed, which guideline version, what exceptions were applied). Production outputs are delivered in agreed formats with manifests, checksums, and split definitions. This keeps your training runs reproducible and reduces relabel risk.

4) Ongoing — Iteration, expansion, and model-driven feedback

As your model improves, your dataset needs evolve. We support iterative refreshes: new edge-case collection, taxonomy extensions, and targeted hard-negative mining informed by your evaluation failures. For RLHF or preference pipelines, we can adjust rubrics as your policy changes and add new scenario packs (tool calling, refusal compliance, multi-turn). With Abaka Forge, you can manage dataset versions and workflows while keeping governance and audit trails intact.

5) Weekly — Reporting, governance, and release cadence

Each week, you receive an operational and quality report: throughput, rejection reasons, reviewer disagreement patterns, and guideline change log. We align on next-week priorities—new domains, new languages, or new modalities—and lock the release cadence that fits your training schedule. This weekly rhythm prevents surprise regressions and keeps dataset changes intentional, making it easier to interpret evaluation deltas and to defend dataset lineage during internal reviews.

Modality & Format Coverage

AI training datasets rarely stay single-modality. Abaka supports end-to-end workflows across text, RLHF, vision, video, audio, and 3D/4D—while delivering standardized exports that integrate with training, evaluation, and analytics stacks. Below is a practical view of the coverage your team can request, including common annotation types, tooling, and output formats that work well for modern ML pipelines.

Modality	Annotation Types	Tools	Output Formats
Text	Instruction tuning (SFT), classification, entity/relation extraction, summarization QA, reasoning QA, multilingual normalization	Abaka Forge	JSONL (prompt/response), CSV, Parquet, UTF-8 TXT with manifests
LLM RLHF	Pairwise preference ranking, rubric scoring, multi-turn conversation grading, safety policy checks, tool/function calling evaluation tags	Abaka Forge	JSONL (prompt, candidates, preference), CSV score tables, audit logs
Image	Classification, bounding boxes, polygons, segmentation masks, keypoints, dense captioning, image+text pairing	Abaka Forge	COCO-style JSON, PNG/JPEG + JSON sidecars, CSV labels, masks (PNG)
Video	Object tracking, temporal segmentation, action labels, dense video captions, video spatial reasoning QA	Abaka Forge	Frame manifests, JSON tracks, COCO-style video schemas, MP4 + sidecar JSON
3D/4D Point Cloud	3D bounding boxes, semantic segmentation, instance segmentation, scene labeling, trajectory attributes	Abaka Forge	JSON annotations, PCD/PLY with manifests, per-frame label packages
LiDAR + Camera fusion	Cross-sensor alignment labeling, fused 3D boxes, projection consistency checks, occlusion and visibility attributes	Abaka Forge	Synchronized sensor manifests, JSON annotations, calibration metadata packaging
Audio	Transcription, speaker labeling, intent classification, multilingual TTS dataset prep, quality flags (noise, overlap)	Abaka Forge	WAV/FLAC + JSON/CSV transcripts, TextGrid-style timing exports, dataset manifests

Success Story

A frontier model lab improving multimodal instruction-following

Challenge

A frontier model lab needed AI training datasets that combined text instructions with images and short video clips to improve grounded instruction-following and reduce hallucinations in visual QA. Their internal pipeline struggled with three issues: inconsistent annotation rubrics across teams, unclear provenance across legacy data sources, and exports that required manual transformations before training. Each iteration introduced drift—so evaluation improvements were hard to attribute to data vs. model changes. They also needed a path to expand into RLHF-style preference data for multimodal responses without rebuilding operations from scratch.

Approach

Abaka partnered with the lab to standardize dataset definitions and deliver a reproducible pipeline. We began with a calibration pilot, aligning rubrics for multimodal question-answering, dense captioning, and error tagging (grounding failures, missing objects, temporal misunderstanding). We used Abaka Forge to manage guideline versions, review queues, and batch-level audit logs. The dataset was assembled with consistent IDs linking prompts to image/video assets, and exports were delivered in training-ready JSONL with manifests and checksums. Once the SFT dataset stabilized, we extended the workflow to collect preference rankings for multiple candidate responses, enabling the lab to train reward models and run preference-optimized fine-tunes while keeping governance and documentation intact.

Results

With a clear rubric, controlled workflow, and consistent exports, the lab reduced iteration friction and improved dataset reliability. Engineers spent less time fixing ingestion issues and more time running targeted experiments. The team also gained confidence that data lineage was documented and that future expansions (more domains, more languages, longer videos) could be added without breaking the schema. The net effect was faster, more interpretable progress: training and evaluation cycles aligned to a weekly cadence, and relabel events dropped because guidelines were stable and enforced through QA gates. - **3 weeks** to deliver a production-grade multimodal dataset pipeline - **40% fewer** ingestion-related training interruptions - **2× faster** dataset iteration cadence after pilot sign-off

3 weeks

to scale from pilot to production delivery

40%

reduction in training interruptions from ingestion issues

2×

faster dataset iteration cadence with weekly releases

By the Numbers

1M+

Vertically specialized annotators available on-demand

50+

Countries supporting multilingual and regional coverage

Target accuracy for annotation programs with QA gates

IP provenance for collected data (no repurposing/resale)

What Customers Say

We needed datasets that engineering could ingest immediately, without writing converters for every batch. Abaka delivered consistent schemas, clear manifests, and a predictable release cadence, which made our training runs reproducible and our evaluation deltas explainable instead of noisy.

Head of Data Operations Foundation Model Company

The biggest improvement wasn’t just volume—it was definition stability. Their review process surfaced guideline ambiguity early, and once the rubric was locked we stopped burning weeks on relabeling. That kept our roadmap intact and reduced internal coordination overhead.

ML Platform Lead Enterprise AI Team

Security and provenance were non-negotiable for us. Abaka’s compliance posture and segregated workflow gave our stakeholders confidence, and their documentation made vendor review straightforward. It was the first time data delivery didn’t become a last-minute blocker.

Director of Applied AI Regulated Industry Company

We expanded from a small pilot to a multi-modality program without changing vendors or rebuilding processes. The combination of operational rigor, domain expertise, and tooling meant we could iterate weekly and keep quality stable even as requirements changed.

Product ML Manager Multimodal AI Company

Why Choose Abaka

Trust-first data partner for frontier AI

Abaka is built to be a long-term data partner, not a short-term vendor. Founded in 2019, self-funded and profitable, with offices in Singapore, Paris, and Silicon Valley, Abaka supports 1,000+ enterprise and research customers. We never build models that compete with you—your datasets remain exclusively yours and are never repurposed, resold, or shared. That governance posture matters when AI training datasets become a core asset and a defensible moat.

Compliance you can pass through procurement

Your security review should not be the longest part of your timeline. Abaka supports SOC 2 and ISO 27001 controls, plus GDPR and CCPA alignment, with strict NDAs and segregated secure pipelines. This makes it easier to operate under enterprise procurement expectations and to keep sensitive data flows controlled. You get auditability and documentation that support internal governance and vendor risk processes.

Full IP provenance with collected data

Training data provenance is increasingly a gating requirement. For collected datasets, Abaka provides full IP provenance with 0% copyright risk, allowing your team to move faster with confidence and to defend dataset lineage when asked. This is especially important for foundation models and multimodal datasets, where unclear sources can force expensive rework or a full dataset replacement late in the program.

Scholar-grade expertise for hard supervision

When you need more than surface labeling—math solutions, coding tasks, medical reasoning, legal categorization—Abaka assigns appropriate expertise through its scholar-network domains (automobile, coding, languages, mathematics, medicine, science, business, law). This avoids the common failure mode where datasets look large but encode shallow or inconsistent reasoning. Expert supervision improves signal quality and reduces post-hoc cleanup.

Abaka Forge for operational control

Abaka Forge centralizes dataset workflows so your team can manage versions, guidelines, QA gates, and exports in one place across text, image, video, RLHF, and 3D/4D point cloud. The platform supports large-model automation for faster throughput (up to 50x faster on suitable steps) while maintaining review controls. This helps you keep a weekly release cadence and reduces the operational drag of coordinating multiple tools and vendors.

Scale without quality collapse

Scaling dataset volume usually introduces inconsistency unless the process is designed for it. Abaka combines global capacity (1M+ specialized annotators across 50+ countries) with practical constraints like a 500 files/day per annotator throughput ceiling to protect quality. With multi-layer QA and batch-level traceability, you can scale AI training datasets while keeping definitions stable, error patterns measurable, and exports consistent—so bigger datasets actually translate into better model behavior.

Frequently Asked Questions

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How much do AI training datasets cost with Abaka?

Pricing depends on modality, difficulty, and QA depth. Common rates include STEM generalist work at $12/hr and LLM math/coding at $18/hr, with dense captioning at $6/hr. For automotive lane labeling, pricing can be $3/km. We’ll scope a pilot first, then provide a clear per-batch estimate tied to your schema, volume, and delivery format.

How long does it take to deliver an AI training dataset?

Most teams start with a pilot in Week 1–2 to lock guidelines and exports, then scale in Week 2–3 once calibration is approved. Timing varies by modality and review rigor, but we optimize for predictable weekly releases so your training schedule stays stable. If you already have schemas and examples, pilots can move faster.

What modalities and output formats do you support for AI training datasets?

We support text, image, video, audio, 3D/4D point cloud, LiDAR+camera fusion, and RLHF preference datasets. Deliveries commonly include JSONL, CSV, Parquet, COCO-style JSON for vision, and media files (PNG/JPEG/MP4/WAV) with sidecar annotations plus manifests and checksums. We can match your storage conventions and deterministic naming requirements.

How do you ensure dataset accuracy and labeling consistency?

We use multi-layer QA: guideline calibration, reviewer queues, spot checks, and drift monitoring against the active guideline version. We can incorporate gold sets and disagreement analysis to identify ambiguous definitions early. Our programs target up to 99% accuracy where the task allows, while keeping traceability so issues can be audited and corrected without destabilizing the entire dataset.

Can you handle secure or sensitive data for training datasets?

Yes. Abaka operates with SOC 2 and ISO 27001 controls and supports GDPR and CCPA-aligned processes, plus strict NDAs and segregated secure pipelines. We scope access control, storage, and export rules at kickoff so your governance team can review upfront. If your data cannot leave your environment, we can discuss workflow constraints and secure delivery options.

Do you support multilingual AI training datasets?

Yes. We operate across 50+ countries and can deliver multilingual datasets for instruction tuning, evaluation, transcription, and domain QA. We can also normalize language metadata, enforce locale-specific style rules, and build balanced splits by language and region. Tell us your target languages and product markets, and we’ll propose a coverage plan and QA approach.

How is Abaka different from other data labeling vendors?

Abaka is designed as a trustworthy data partner for frontier AI: we never build models that compete with you, and your data is never repurposed, resold, or shared. We combine global capacity with scholar-network expertise in math, coding, medicine, and law, and use Abaka Forge to manage workflow, versioning, and exports across modalities—including RLHF and 3D/4D.

What if we need changes to guidelines or the schema mid-project?

Change is expected. We treat updates as versioned releases: we capture the new definition, run a small recalibration batch, and then scale with the updated guideline version. This prevents silent drift and keeps evaluation results interpretable. We’ll also advise when backfills are necessary vs. when forward-only changes are sufficient to meet your model goals.

Can we start with a pilot dataset before committing to a large engagement?

Yes—starting with a pilot is the standard approach. A pilot lets you validate rubrics, exports, and QA gates with a small batch before scaling volume. You’ll receive sample outputs early so engineering can test ingestion and training. After pilot approval, we ramp production with weekly releases and documented dataset lineage.

Who owns the AI training dataset and can Abaka reuse it?

You own the dataset. Abaka does not repurpose, resell, or share customer data—ever. We also do not build models that compete with you, which helps ensure your data remains a protected asset. For collected data, we provide provenance documentation so you can defend ownership and usage rights as part of your governance process.

What tools do you use to manage dataset workflows and QA?

We use Abaka Forge for end-to-end workflow control across modalities, including annotation, review queues, versioning, and exports. Abaka Forge supports large-model automation to accelerate suitable steps while keeping QA gates and audit logs. If you have existing tooling, we can align deliverables to your schemas and integrate via agreed export formats and manifests.

What is the minimum dataset size or budget to get started?

Minimums depend on modality and task complexity, but many teams begin with a focused pilot sized to validate the rubric and export format. The goal is to prove correctness before scaling. Share your target use-case, modality, and a rough volume estimate, and we’ll propose a pilot scope that balances cost, timeline, and statistical usefulness for evaluation.

Ready to Get Started?

If you’re blocked by sourcing, curation, RLHF, or multimodal labeling, Abaka will help your team ship AI training datasets that are training-ready, provenance-clear, and delivered on a predictable cadence. Talk to us about a pilot that matches your schema, quality bar, and timeline—email business@abaka.ai. Human Intelligence — Data for Frontier AI