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Build reliable datasets with a
Data Curation Partner you can trust

Abaka curates, cleans, and QA-validates multimodal training data in secure pipelines—so your team ships evaluations, fine-tunes, and production models faster with fewer regressions.

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When curation is treated as a last-mile task, model progress slows down everywhere: noisy labels leak into training, edge cases get underrepresented, and evaluation sets drift. The result is costly rework—weeks of debugging that looks like “model issues” but is actually dataset debt. Teams also lose velocity when they can’t trust provenance: a single unclear source can trigger legal review cycles and stall releases. Even a 5–10% drop in dataset precision can cascade into failed benchmarks, unstable agents, and brittle multimodal behavior across new geos and devices.

Abaka operates as your data curation partner—combining expert human intelligence with Abaka Forge workflows for collection, cleaning, annotation, and multi-layer QA. You get curated datasets built to your taxonomy, acceptance criteria, and evaluation plan—then maintained as living assets with versioning and change control. With vertically specialized reviewers across 50+ countries and secure, segregated pipelines, your team can scale volume without sacrificing auditability. We focus on what actually improves outcomes: coverage of edge cases, consistent guidelines, and measurable quality gates before data ever reaches training.

The Data Curation Partner Bottleneck

01

Quality Decay

Curation quality erodes when guidelines live in docs instead of in the workflow. Small interpretation gaps compound into inconsistent labels, duplicated samples, and silent distribution shifts. Abaka sets explicit quality gates—golden sets, inter-annotator agreement checks, and targeted audits—so your curated corpus stays stable release to release. With 99% accuracy targets and controlled throughput (up to 500 files/day per annotator), you avoid “rush labeling” that inflates volume but degrades usefulness. The outcome is fewer retrains and fewer late-stage benchmark surprises.

02

Volume Walls

Teams hit volume ceilings when curation requires constant manual wrangling—format conversions, de-duplication, and ad-hoc sampling. Abaka Forge accelerates pipeline steps with large-model automation while keeping humans in the loop for edge cases and domain judgments. That means you can scale from thousands to millions of items without losing consistency. For collection-heavy programs, our custom capture pods and pre-filtered, timestamped tagging can cut preprocessing effort by 70%, so your team spends time on model iteration instead of data triage.

03

Compliance Friction

Even high-quality datasets become unusable if provenance, access control, or legal review is unclear. Abaka runs SOC 2 and ISO 27001-aligned operations, supports GDPR/CCPA requirements, and uses strict NDAs with segregated secure pipelines. For curated corpora, we track IP provenance end-to-end and maintain 0% copyright risk on collected data—so your team can ship without last-minute policy escalations. When security and documentation are built into the curation process, approvals move in days instead of weeks.

01

Curation plans aligned to model and eval goals

We translate your training and evaluation objectives into a concrete curation spec: taxonomy, inclusion/exclusion rules, edge-case quotas, and acceptance thresholds. Your team gets a measurable definition of “done” for each dataset version, including sampling strategy and QA checks. This is especially effective for foundation-model data mixtures, autonomous driving edge cases, and safety evaluation sets where coverage matters as much as volume. We operationalize the spec in Abaka Forge so guidelines are enforced inside the workflow—not just documented.

02

Deduplication, normalization, and dataset hygiene at scale

Abaka delivers practical dataset hygiene: duplicate and near-duplicate removal, schema normalization, and format validation across text, images, video, and 3D. We standardize metadata, enforce naming conventions, and remove corrupted or low-signal files before they enter training. Common outputs include normalized JSONL for text, COCO-style structures for vision, and consistent timestamped manifests for multimodal sets. The goal is fewer pipeline breaks and fewer silent bugs when you train across multiple frameworks and data sources.

03

Multi-layer quality assurance with scholar-grade reviewers

You get multi-layer QA designed for high-stakes datasets: reviewer spot checks, adjudication on disagreements, and targeted audits on known failure modes. Abaka’s scholar-network domains span coding, mathematics, medicine, law, and languages—useful when “curation” requires real judgment, not just formatting. We can also implement golden tasks and continuous calibration to keep teams aligned as your taxonomy evolves. This approach is proven for reasoning corpora, coding datasets, and domain-specific instruction following.

04

Coverage-driven sampling for edge cases and long tail

Random sampling is not enough when your model fails on rare events. We curate for coverage—building stratified and adversarial slices, ensuring representation across geos, devices, and conditions. For automotive and robotics, that can mean deliberate long-tail selection (weather, occlusion, unusual actors). For GenAI, it means balancing domains, difficulty, and safety categories. The output is a dataset that improves generalization rather than just increasing training tokens.

05

Preference data curation for alignment and usefulness

When you need RLHF-ready data, curation includes prompt selection, response filtering, and rubric design—so preference signals are consistent and auditable. We curate instruction sets, multi-turn dialogs, and tool-use traces, then validate them via human evaluation and model-as-judge workflows (where appropriate) inside Abaka Forge. This is ideal for assistant quality, enterprise agent behavior, and domain-specific copilot use cases where policy and tone must be consistent across languages and user segments.

06

Multimodal curation across text, vision, video, and 3D

Abaka curates multimodal corpora with synchronized metadata: image-text pairs, video with temporal annotations, and 3D/4D point clouds with scene context. We ensure alignment across modalities (timestamps, coordinate frames, sensor metadata) and validate schema integrity so training pipelines stay stable. This matters for embodied AI, VLMs, and autonomy stacks where “almost aligned” data creates hard-to-debug failures. Your team receives consistent, documented outputs ready for training and evaluation.

07

End-to-end provenance and secure data handling controls

Curation includes traceability: where each item came from, what transforms were applied, who reviewed it, and which guideline version was used. We run under strict NDAs, support segregated secure pipelines, and maintain full IP provenance for curated corpora—so your organization can pass internal governance reviews and external audits. We never build models that compete with you; your data remains exclusively yours and is never repurposed, resold, or shared.

08

Versioned deliveries with change control and clear acceptance

We deliver curated datasets as versioned artifacts with manifests, QA reports, and defined acceptance criteria. When requirements change, we run structured change requests—impact analysis, updated guidelines, and controlled backfills—so your training and evaluation sets don’t drift unpredictably. This is useful for production ML teams maintaining “living datasets” and for research teams iterating quickly without losing reproducibility. You get clean handoffs: formats, checksums, and documentation that engineers actually use.

Why Outsource Data Curation Partner Workstreams

01

Faster Delivery

Move from curation spec to first production delivery in 2–3 weeks by reusing proven workflows, QA patterns, and specialist teams. You avoid months of hiring and process design, and your engineers stay focused on models—not wrangling data.

02

Direct Savings

Reduce internal overhead spent on cleaning, sampling, and rework. Abaka Forge automation and calibrated human review cut repeated relabeling cycles and lower the cost of “dataset debt” that otherwise consumes sprint after sprint.

03

Risk Reduction

Minimize governance and security surprises with SOC 2 and ISO 27001-aligned operations, strict NDAs, and segregated pipelines. Clear provenance and documented QA reduce the risk of unusable datasets late in the release cycle.

04

Elastic Scalability

Scale from a small pilot to high-volume curation without breaking consistency. With 1M+ specialized annotators across 50+ countries and controlled throughput limits, you can grow volume while keeping quality stable.

05

Domain Expertise

Use scholar-network expertise (coding, math, medicine, law, languages) when curation requires judgment. This improves rubric consistency for RLHF, reasoning, and domain QA where generic labeling vendors struggle.

06

Innovation Velocity

Iterate faster with versioned datasets, weekly reporting, and structured change control. Your team can test new taxonomies, evaluation slices, and edge-case programs without losing reproducibility or operational control.

Industries We Serve

Automotive

Curate perception and autonomy datasets with long-tail coverage: lane geometry, rare behaviors, adverse weather, and sensor anomalies. We deliver clean, versioned outputs with consistent taxonomies so your training and validation sets stay aligned across releases.

GenAI / Foundation Models

Curate text and multimodal corpora for pretraining, instruction tuning, and eval stability. We balance domains and difficulty, remove duplicates, and produce provenance-backed datasets suitable for internal governance and safety review workflows.

Embodied AI / Robotics

Curate robotics data with action context: synchronized video, 3D/4D point clouds, and structured task metadata. We build coverage-driven slices that reflect real environments so agents generalize beyond lab setups.

Healthcare

Curate medical text and imaging datasets with careful normalization, terminology consistency, and reviewer QA. We focus on auditability and clear guidelines so your team can build dependable evaluation sets and reduce costly false positives.

Retail

Curate product, catalog, and customer-interaction datasets for search, recommendations, and support assistants. We standardize schemas, clean noisy attributes, and create robust evaluation slices for seasonal drift and new inventory.

Finance

Curate datasets for fraud, risk, and GenAI copilots with strict provenance and controlled access. We normalize entities, filter low-signal records, and build evaluation subsets that capture edge cases and policy constraints.

Geospatial

Curate geospatial imagery and sensor data with consistent metadata, tiling, and QA. We help you build clean training sets for detection, change monitoring, and mapping workflows while maintaining clear lineage and versioning.

Security / Defense

Curate mission-relevant datasets in segregated secure pipelines with strict NDAs and documented QA. We build reliable evaluation sets for detection, classification, and multimodal analysis without compromising provenance or operational controls.

Agriculture / Industrial

Curate datasets for inspection, yield monitoring, and predictive maintenance. We clean and standardize field imagery, sensor streams, and annotations so models stay stable across seasons, sites, and hardware upgrades.

How It Works

1) Day 0–3 — Define the curation spec and success metrics

We align on your use case, target model behaviors, and evaluation plan. Then we produce a curation spec: taxonomy, inclusion rules, edge-case quotas, QA gates, and delivery formats. We also confirm security requirements (NDA, access controls, segregated pipelines) and set acceptance criteria for version 1.

2) Week 1–2 — Stand up the pipeline in Abaka Forge

We configure Abaka Forge for ingestion, cleaning, sampling, and review. Your team gets a transparent workflow with guideline enforcement, QA checkpoints, and clear audit trails. We run calibration rounds, resolve ambiguity in labels, and lock the rubric before scaling.

3) Week 2–3 — Deliver the first curated dataset version

You receive a versioned delivery with manifests, QA reporting, and agreed output formats. We validate schema integrity, remove duplicates, and ensure coverage targets are met (including long-tail slices). Your engineers can plug the dataset directly into training and evaluation pipelines.

4) Ongoing — Maintain a living dataset with change control

As your taxonomy, model, or product requirements evolve, we run structured change requests: impact analysis, guideline updates, and controlled backfills. This prevents silent drift between training and eval sets and keeps reproducibility intact across model releases.

5) Weekly — Report quality, coverage, and throughput

We provide weekly rollups: QA findings, disagreement drivers, edge-case coverage, and throughput. You get clear visibility into what changed and why, plus recommendations for new slices or data acquisition to address model failure modes.

Modality & Format Coverage

Curation is only useful if it ships in the formats your stack expects. Abaka supports multimodal pipelines end-to-end—curated, QA’d, and versioned—delivered in practical, engineer-friendly structures.

ModalityAnnotation TypesToolsOutput Formats
TextDedup + filtering; taxonomy-based labeling; entity normalization; multilingual review; dataset slicingAbaka ForgeJSONL; CSV; Parquet; UTF-8 TXT; dataset manifests
LLM RLHFPreference ranking; rubric-based scoring; multi-turn dialog curation; tool-use trace review; safety category taggingAbaka ForgeJSONL; conversation transcripts; preference pairs; eval score tables; audit logs
ImageDataset cleaning; class balance curation; bounding boxes; polygons; dense captionsAbaka ForgeCOCO JSON; YOLO TXT; Pascal VOC XML; image manifests; QA reports
VideoClip selection; temporal segmentation; event tagging; object tracking curation; video QA samplingAbaka ForgeMP4 manifests; JSON annotations; frame-level labels; timestamp tables; dataset cards
3D/4D Point CloudScene curation; cuboids; point-level segmentation; trajectory review; coordinate-frame validationAbaka ForgePCD; LAS/LAZ; JSON labels; frame manifests; calibration metadata
LiDAR + Camera fusionSensor sync validation; fused object curation; calibration checks; cross-modal QA; long-tail slice creationAbaka ForgeSynchronized manifests; JSON labels; calibration files; timestamp alignment tables; dataset reports
AudioAudio cleaning; segmentation; transcription review; speaker labeling; multilingual QAAbaka ForgeWAV/FLAC manifests; JSON transcripts; TextGrid; CSV timestamps; QA summaries

Success Story

A leading GenAI / foundation model AI team
Challenge

The team’s training mixture was growing fast, but data quality and provenance were uneven across sources. Duplicates inflated token counts, multilingual content had inconsistent normalization, and evaluation sets drifted from what the model actually saw in training. Each new release required time-consuming forensic work to explain regressions. Internal governance reviewers also needed clearer lineage and change logs before approving datasets for new domains. The team needed a data curation partner who could enforce consistent standards while still moving quickly.

Approach

Abaka defined a curation spec tied to target behaviors and evaluation goals, then implemented it inside Abaka Forge: automated cleaning and deduplication, coverage-driven sampling, and multi-layer human QA. We introduced rubric-based review for tricky edge cases and created versioned dataset manifests with audit trails. For multilingual slices, we used language-specialist reviewers and normalization rules to keep format and semantics consistent across sources. We also established a weekly reporting cadence so quality issues were caught early instead of during model debugging.

Results

Within 3 weeks, the team received a first curated release with measurable QA gates and repeatable versioning, enabling faster fine-tune and eval cycles. Preprocessing effort dropped by 70% through automated hygiene plus human-in-the-loop adjudication, and the team stabilized dataset drift by treating curation as a governed pipeline rather than ad-hoc cleanup. The updated corpus improved downstream evaluation reliability and reduced late-stage rework, helping the team ship releases on schedule with clearer provenance—delivering 99% accuracy on curated slices and cutting release debugging time by 30%.

3 weeks
First curated dataset version delivered
70%
Preprocessing time reduction
99%
Accuracy target on curated slices

By the Numbers

2019
Founded — trustworthy data partner for frontier AI
1,000+
Enterprise and research customers served
50+
Countries supported for global data programs
1M+
Vertically specialized annotators available on-demand

What Customers Say

We came in asking for “cleanup,” but Abaka pushed us to define acceptance criteria and QA gates. The result was a curated dataset we could actually reuse and version, not a one-time patch. Our training and eval finally matched.

Director of Applied MLEnterprise AI Platform Company

The biggest win was provenance and change control. We stopped debating which dataset was “the real one” and started shipping versioned releases with clear manifests and review trails. That eliminated a lot of internal friction.

Head of Data OperationsFoundation Model Lab

Their reviewers caught edge cases our internal team kept missing, especially in multilingual slices and ambiguous categories. The weekly reporting made it easy to see where guidelines needed clarification and what to fix next.

ML Engineering ManagerGlobal Consumer Technology Company

Abaka was flexible with formats and workflows—JSONL, COCO, manifests, and QA summaries—so engineering didn’t have to build glue code. We got clean deliveries that plugged into training the same day.

Staff Machine Learning EngineerRobotics Software Company

Why Choose Abaka

01

A curation partner built for governance, scale, and measurable quality

Abaka combines expert human intelligence with Abaka Forge workflows to turn curation into a controlled, auditable pipeline. You get secure operations (SOC 2 and ISO 27001-aligned, strict NDAs, segregated pipelines), multi-layer QA, and versioned deliveries that engineering can trust. Our teams span 50+ countries and specialized domains—so you can scale volume without losing consistency. And we never build models that compete with you: your data stays exclusively yours, never repurposed, resold, or shared.

02

Human Intelligence — Data for Frontier AI

We focus on the work that changes outcomes: coverage, consistency, and QA gates tied to your model goals. No vague “data services”—just operational curation that ships.

03

Secure, segregated delivery pipelines

From access controls to audit logs, we design curation for enterprise requirements. Your team gets clear provenance and controlled change management for every dataset version.

04

Abaka Forge accelerates repeatable workflows

Use Abaka Forge to standardize cleaning, QA, and delivery across modalities. Large-model automation speeds routine steps while humans handle edge cases and domain judgment.

05

Specialists when “clean” isn’t enough

When curation involves reasoning, coding, math, medicine, law, or multilingual nuance, we route work to calibrated reviewers—reducing inconsistent judgments that cause model regressions.

06

No competing models. No VC pressure. Your data remains yours.

Abaka is self-funded and profitable, with offices in Singapore, Paris, and Silicon Valley. We do not build models that compete with you, and we never reuse or resell your data. That means your curated datasets stay exclusive to your organization, with clear IP provenance and 0% copyright risk on collected data.

Frequently Asked Questions

How much does a data curation partner cost?
Pricing depends on modality, complexity, and whether you need collection, cleaning, annotation, or evaluation curation. For common building blocks, Abaka offers transparent baselines you can compose into a full curation program: STEM generalist work is typically $12/hr, LLM math/coding specialists are $18/hr, dense captioning is $6/hr, and image editing is $8/hr. For autonomy-specific components, road lane annotation is $3/km. Talk to an Expert with your scope and target formats to get a fixed quote and timeline.
How fast can you deliver the first curated dataset?
Most teams receive a first curated dataset version in 2–3 weeks once the scope, security requirements, and acceptance criteria are confirmed. Day 0–3 is typically used for curation spec definition and success metrics. Week 1–2 is pipeline setup in Abaka Forge and calibration. Week 2–3 focuses on scaled production plus multi-layer QA, followed by a versioned delivery with manifests and QA reporting. If you have data ready and a stable taxonomy, pilots can move even faster.
What modalities and file formats do you support for data curation?
We support text, LLM RLHF datasets, images, video, audio, 3D/4D point clouds, and LiDAR + camera fusion workflows. Deliveries are produced in practical formats your engineers can integrate quickly—commonly JSONL/CSV/Parquet for text and RLHF, COCO/YOLO/VOC for vision, timestamped manifests and JSON for video, and standard point cloud formats like PCD or LAS/LAZ with accompanying metadata. If you have an internal schema, we can map outputs to it and document the mapping for reproducibility.
What accuracy can you achieve for curated datasets?
For curated annotation and review workstreams, Abaka targets up to 99% accuracy using multi-layer QA rather than relying on a single pass. The exact measurable accuracy depends on your label taxonomy, ambiguity level, and the definition of correctness (e.g., strict vs. tolerant matching). We recommend defining acceptance criteria up front (golden sets, adjudication rules, and error budgets by label type) so “accuracy” is meaningful. Your team also receives QA reporting so you can see error categories and improvements over time.
How do you handle security, privacy, and compliance?
Abaka operates with enterprise controls: SOC 2 and ISO 27001-aligned practices, strict NDAs, and segregated secure pipelines for sensitive programs. We support GDPR and CCPA requirements and build access control, logging, and audit trails into the workflow so governance teams can review dataset lineage and changes. We also maintain full IP provenance, and for collected data we provide 0% copyright risk. If you need additional controls (VPC, restricted access tiers, or custom retention), we can scope them during onboarding.
Can you curate multilingual datasets across regions and dialects?
Yes. Abaka supports global data programs across 50+ countries and can curate multilingual datasets with language-specialist reviewers. We focus on normalization and consistency: language ID, encoding validation, locale-aware tokenization choices (where applicable), and rubric alignment so the same intent is labeled consistently across languages. For RLHF and instruction data, we also curate tone and policy adherence by locale. Deliveries include clear metadata so your team can slice by language, region, domain, and difficulty for training and evaluation stability.
How are you different from a typical data labeling vendor?
A data curation partner is accountable for dataset usefulness, not just label throughput. Abaka starts from your model and evaluation goals, then designs curation specs, sampling plans, and QA gates—so you get a versioned dataset asset that can be maintained. We combine specialist human reviewers (including scholar-network domains like math, coding, and medicine) with Abaka Forge workflows for repeatability. We also emphasize governance: secure pipelines, provenance, and change control. And we never build competing models—your data stays exclusively yours.
What if our taxonomy or requirements change mid-project?
Change requests are expected in real programs, so we run structured change control. We’ll assess the impact on existing data, update guidelines, run calibration to re-align reviewers, and execute controlled backfills where needed. To prevent drift, we version every delivery and document what changed (taxonomy definitions, inclusion rules, QA checks). That way your team can reproduce past experiments while moving forward with improved labels or coverage. Weekly reporting highlights ambiguity hotspots so you can decide where to refine categories versus merge them.
Can we start with a pilot before a larger engagement?
Yes—pilots are a recommended way to validate label definitions, QA gates, and delivery formats before scaling. A pilot typically covers a representative slice of data (including edge cases) and produces a versioned output plus a QA report that quantifies error types and guideline gaps. From there, we refine the spec and ramp volume. This approach reduces total cost because you fix ambiguity early rather than paying for large-scale relabeling later. Talk to an Expert to define a pilot size, success metrics, and timeline.
Who owns the curated dataset and can you reuse it?
You own your curated dataset. Abaka does not repurpose, resell, or share your data—ever. We also never build models that compete with you, so there is no incentive to extract value from your datasets beyond delivering your project. For collection engagements, we provide full IP provenance and maintain 0% copyright risk on collected data. If your governance team requires additional contractual language on exclusivity, retention, or deletion, we can incorporate it during onboarding under NDA.
What tools and workflows do you use for data curation?
We use Abaka Forge—our all-in-one platform for collection, cleaning, annotation, training support, and production workflows. For curation, that means structured ingestion, automation for repetitive cleaning steps, built-in QA checkpoints, adjudication flows, and versioned deliveries with audit trails. Abaka Forge supports all major modalities (text, RLHF, image, video, and 3D/4D point clouds) and can be configured to match your taxonomy and acceptance criteria. You receive consistent outputs plus documentation that engineers and reviewers can follow.
What is the minimum dataset size you can curate?
We can start small—often a few hundred to a few thousand items is enough to run a meaningful pilot—especially if the goal is to validate taxonomy, QA gates, and formats. For multimodal or edge-case-heavy programs, a minimum viable slice should include representative long-tail examples so calibration is realistic. If you have very large-scale needs, we can scale using specialized teams and controlled throughput limits (up to 500 files/day per annotator) to protect quality. Share your target outcomes and constraints, and we’ll recommend a right-sized starting scope.

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Annotate the Present. Train the Future.

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