Abaka combines scholar-grade reviewers, multi-layer QA, and Abaka Forge automation to deliver consistent datasets across text, vision, RLHF, and 3D—without losing provenance or speed.
When annotation becomes the bottleneck, your roadmap slips even if modeling is strong. Teams lose weeks reworking inconsistent labels, reviewers burn out chasing edge cases, and model quality stalls because evaluation data is noisy. A 2-week sprint can turn into 6+ weeks once disagreement rates spike and re-annotation loops begin. Meanwhile, compliance reviews and unclear IP provenance can block launches outright—especially when datasets mix vendors, unmanaged contractors, and unclear access controls.
Abaka is built for teams that need repeatable, auditable labeling at scale. We pair vertically specialized annotators across 50+ countries with a clear spec-to-acceptance workflow, multi-layer QA, and platform-assisted throughput via Abaka Forge. You get clean delivery in the formats your pipelines expect, plus strict NDAs, segregated secure pipelines, and full IP provenance—so your training, tuning, and evaluation datasets remain stable as your requirements evolve.
Label quality often drops as volume rises: new annotators interpret guidelines differently, edge cases pile up, and small inconsistencies compound into measurable performance regressions. Even a 1–2% labeling drift can derail evaluation deltas and trigger wasted retraining cycles. Abaka counters this with spec-driven onboarding, calibrated gold sets, and multi-layer QA so you can sustain up to 99% accuracy targets across long-running programs without restarting your pipeline every sprint.
Most teams discover a hard ceiling: a few trusted annotators can’t keep up, and scaling quickly creates churn and rework. With practical throughput caps like 500 files/day per annotator, large queues can add weeks to delivery if staffing isn’t elastic. Abaka provides access to 1M+ specialized annotators and uses Abaka Forge automation to keep velocity high while preserving reviewer consistency—so volume increases don’t translate into compounding lead time.
Security, privacy, and IP provenance failures can stop a dataset from ever reaching training. One missing access log, unclear subcontractor chain, or uncertain copyright origin can trigger re-collection and multi-week legal review. Abaka operates with SOC 2, ISO 27001, GDPR, and CCPA alignment, strict NDAs, segregated secure pipelines, and full IP provenance—enabling 0% copyright risk on collected data and faster internal approvals for enterprise teams.
Turn ambiguous goals into an annotation spec your team can trust: label taxonomy, edge-case rules, reviewer rubrics, and measurable acceptance criteria. Abaka supports enterprise workflows for model training and evaluation across verticals like automotive, medicine, and finance. We align outputs to common formats (JSONL/CSV/COCO-style schemas) and define sampling plans, gold sets, and escalation paths so quality remains stable as volume scales.
Generate and label text datasets for classification, extraction, summarization, and instruction following. Abaka supports multilingual labeling and specialist domains (law, medicine, mathematics, coding) through a scholar-network approach. Deliverables can include JSONL with span offsets, entity schemas, and structured rubrics for evaluation sets. Abaka Forge streamlines review workflows while keeping a clear audit trail from guideline to final label.
Build RLHF pipelines with pairwise preferences, rubric-based grading, and reasoning-focused tasks for alignment, factuality, and instruction following. We support human evaluation and model-as-judge calibration workflows and can staff reviewers with math/coding strengths for harder distributions. Outputs can include ranked pairs, scalar scores, and rater metadata in JSONL—ready for reward modeling and alignment training without ad-hoc spreadsheets.
Annotate images for bounding boxes, polygons, keypoints, attributes, and dense captioning in production-ready formats. Abaka supports quality gates for class balance, ambiguity resolution, and inter-annotator agreement. Use cases span retail shelf analytics, medical imaging assistance, and security monitoring. Abaka Forge accelerates labeling with large-model automation while keeping human reviewers in control for edge cases and high-stakes categories.
Label video for activity recognition, multi-object tracking, temporal events, and video spatial reasoning. We manage frame sampling strategies, track consistency rules, and occlusion handling so labels don’t jitter across time. Deliverables include per-frame annotations and event timelines in JSON/CSV, plus QA reports. Common applications include autonomous driving perception QA, retail loss prevention, and industrial safety monitoring.
Support 3D/4D point cloud annotation for object cuboids, semantic segmentation, and motion-aware labeling for embodied AI and robotics. We handle sensor-specific edge cases like sparse returns, reflective surfaces, and long-range noise. Outputs can be delivered as structured JSON with 3D boxes and per-point labels aligned to your coordinate conventions. Abaka Forge provides tooling for efficient review and consistent annotator workflows.
Unify labels across LiDAR and camera streams with consistent object IDs, synchronized timestamps, and cross-view verification. This reduces downstream headaches in perception stacks where mismatched IDs and timing offsets create false negatives. We deliver fused annotations with clear schema definitions and QA checkpoints. Typical programs include lane and object labeling, scene attributes, and long-tail scenario coverage for automotive and robotics teams.
Prevent re-annotation loops by building QA into the workflow: sampling plans, second-pass reviews, adjudication for hard cases, and dataset audit reports. Abaka targets up to 99% accuracy with structured escalation and reviewer calibration. You get traceability from guideline versions to label decisions, plus quantitative QA signals to manage drift. This is especially valuable for evaluation sets, safety audits, and long-running RLHF programs.
Compress timelines by mobilizing trained capacity quickly and keeping work unblocked with parallel QA. Many teams reach pilot-ready delivery in 2–3 weeks when specs are clear, instead of dragging for 6+ weeks through hiring and tool setup.
Avoid the hidden cost of rebuilding internal labeling operations: recruiting, training, rework, and QA overhead. Abaka combines specialized annotators with Abaka Forge automation to reduce manual burden and keep budgets predictable as volume grows.
Reduce security and IP risk with strict NDAs, segregated pipelines, and full provenance. Abaka aligns to SOC 2, ISO 27001, GDPR, and CCPA expectations so your legal and security reviews don’t become the critical path.
Scale from a small gold-set pilot to sustained production without compromising consistency. With access to 1M+ specialized annotators and throughput governance (e.g., 500 files/day per annotator), you can expand safely without quality collapse.
Hard data needs experts: coding, math, medicine, and law require reviewer depth, not generic crowd work. Abaka’s scholar-network domains help you label long-tail edge cases and build evaluation-grade datasets that hold up under scrutiny.
Iterate faster on new tasks—RLHF rubrics, tool-calling evals, multimodal reasoning, or robotics perception—without rebuilding your entire workflow. Abaka Forge standardizes operations so your team can focus on modeling and product outcomes.
Support ADAS and autonomous driving programs with lane and scene labeling, video temporal events, and LiDAR + camera fusion workflows. Abaka manages long-tail edge cases and consistent object identities across frames. Deliverables align to your internal schemas for training and evaluation, with multi-layer QA to keep perception metrics stable as new geos, weather, and lighting conditions expand the dataset.
Build instruction-following data, reasoning sets, and RLHF preference datasets across multilingual and specialist domains. Abaka supports human evaluation, rubric-based grading, and calibration workflows for alignment and factuality. Abaka Forge accelerates production while maintaining provenance and auditability—so your team can iterate quickly on new prompt styles, tool-calling behaviors, and evaluation criteria.
Label 3D/4D point clouds, robot-view video, and multimodal sequences for manipulation, navigation, and safety. Abaka can pair perception labeling with custom RL environment support for agent capability development. We focus on temporal consistency, coordinate correctness, and edge-case adjudication so robotics models generalize outside lab conditions and across new sensors.
Create high-quality datasets for clinical NLP, document extraction, and medical imagery support tasks where terminology and ambiguity matter. Abaka can staff domain-aware reviewers and enforce strict access controls, NDAs, and segregated secure pipelines. You receive auditable labeling guidelines, QA artifacts, and format-consistent outputs that support training and evaluation while respecting privacy and governance needs.
Power product search, recommendations, shelf analytics, and customer-support automation with image and text labeling. Abaka provides entity extraction, taxonomy mapping, image attribute tags, and dense captioning for long-tail SKU coverage. Abaka Forge improves throughput while QA keeps labels consistent across seasonal catalog changes and multilingual product listings.
Improve document understanding, risk workflows, and customer communications with labeled datasets for extraction, classification, and evaluation. Abaka supports specialist review for complex business and legal language and provides traceability for audit needs. Security-first operations and provenance controls help you move faster through vendor reviews while keeping sensitive data protected.
Annotate satellite and aerial imagery for land-use classification, object detection, and change detection. Abaka supports polygon segmentation, multi-class taxonomies, and QA workflows designed for class imbalance. Outputs are delivered in structured formats compatible with geospatial pipelines, enabling reliable evaluation sets and production training data for mapping and monitoring applications.
Enable perception and analysis datasets for surveillance imagery/video, sensor fusion, and event detection while prioritizing access controls and audit trails. Abaka’s segregated secure pipelines and strict NDAs support sensitive workflows. Multi-layer QA and escalation paths help manage ambiguity and reduce false positives/negatives in downstream systems where operational risk is high.
Label vision and sensor data for crop monitoring, defect detection, predictive maintenance, and safety systems. Abaka can annotate images/video for object and anomaly detection and support text workflows for maintenance logs and incident reports. Abaka Forge standardizes review and QA so labels remain consistent as sites, equipment, and environmental conditions vary.
We confirm your use case (training, tuning, evaluation), data access constraints, and target outputs. Abaka drafts a labeling spec with edge-case handling, acceptance criteria, and QA checkpoints. You share a small representative sample, and we run a fast feasibility pass to validate label taxonomy, ambiguity hotspots, and the delivery schema before scaling.
We execute a pilot with trained annotators and multi-layer QA, then calibrate disagreement resolution and reviewer rubrics. For RLHF, we align raters on preference criteria and grading consistency; for vision, we validate class definitions and occlusion rules. You receive pilot outputs plus QA reports so you can approve the spec with confidence.
After pilot sign-off, we scale staffing and throughput while keeping quality stable via gold sets, sampling plans, and adjudication. Abaka Forge supports faster task routing, review, and issue triage. We deliver in your preferred formats (e.g., JSONL/CSV, COCO-style structures) with consistent versioning and clear change logs.
As your model evolves, the dataset must stay consistent. We manage guideline revisions, maintain label provenance, and monitor drift using periodic audits and targeted recalibration. When distributions shift (new geos, new languages, new product categories), we update edge-case rules and keep the labeling operation stable rather than restarting from scratch.
You get weekly rollups on throughput, QA signals, and issue categories, plus a concrete plan for the next iteration. Change requests are handled through a controlled spec update—sample-first when needed—so downstream training isn’t surprised. We optimize where it matters: fewer re-annotation loops, faster reviewer decisions, and cleaner evaluation sets.
Your team shouldn’t juggle different vendors for each data type. Abaka covers the full pipeline—from labeling to QA—across modalities, with consistent specs, provenance, and delivery formats supported by Abaka Forge.
| Modality | Annotation Types | Tools | Output Formats |
|---|---|---|---|
| Text | NER & span labeling, classification, instruction data, structured extraction, multilingual normalization | Abaka Forge | JSONL, CSV, TSV, Parquet, schema-mapped JSON |
| LLM RLHF | pairwise preference ranking, rubric-based grading, safety & bias audits, tool/function calling evals, human evaluation | Abaka Forge | JSONL (pairs), JSONL (scores), CSV exports, rater-metadata tables, evaluation reports |
| Image | bounding boxes, polygons/segmentation, keypoints, attributes, dense captioning | Abaka Forge | COCO-style JSON, JSON, CSV, mask PNGs, dataset manifests |
| Video | temporal events, multi-object tracking, frame-by-frame boxes/polygons, action labeling, video spatial reasoning tags | Abaka Forge | JSON (per-frame), CSV timelines, dataset manifests, track-ID exports, QA summaries |
| 3D/4D Point Cloud | 3D cuboids, semantic segmentation, instance IDs, motion-aware labeling, scene attributes | Abaka Forge | JSON (3D boxes), per-point label arrays, frame manifests, coordinate metadata, QA reports |
| LiDAR + Camera fusion | cross-sensor object IDs, synchronized timestamps, fused validation, lane/scene labels, occlusion adjudication | Abaka Forge | JSON (fusion schema), per-sensor exports, timestamped manifests, calibration metadata, QA audit logs |
| Audio | transcription, speaker diarization, intent labeling, emotion/tonality tags, multilingual normalization | Abaka Forge | JSONL, CSV, TXT transcripts, time-coded segments, dataset manifests |
The team’s internal labeling program couldn’t keep up with rapid iteration. New task variants were launched weekly, but rater consistency drifted and evaluation datasets became noisy. They were also consolidating multiple vendors, which created mismatched schemas and unclear provenance across datasets. The result was slow iteration cycles and low confidence in model deltas—especially for instruction following and factuality where small label inconsistencies can mask true progress.
Abaka established a spec-first workflow with clear acceptance criteria, calibrated gold sets, and adjudication rules for edge cases. We staffed reviewers matched to the task mix, including math/coding-capable raters for harder distributions, and ran iterative calibration to stabilize preferences and rubric grading. Using Abaka Forge, we standardized task routing, review, and audit trails, aligning outputs into a unified JSONL schema with rater metadata and versioned guidelines.
Within the pilot-to-scale window, the lab consolidated its RLHF and evaluation pipeline into one consistent delivery process. Weekly updates became predictable, rater disagreement dropped through calibration and adjudication, and the team stopped re-annotating large batches due to unclear edge cases. Abaka delivered up to 99% accuracy targets on agreed tasks, accelerated pilot readiness to 2–3 weeks, and enabled faster iteration by keeping evaluation sets stable across releases—cutting wasted rework and improving confidence in measured deltas.
We needed a partner who could move fast without turning our evaluation set into a moving target. Abaka’s spec discipline and adjudication loop reduced rework and made weekly deliveries predictable. The audit trail and provenance controls were also a major reason our security team approved the workflow quickly.
Our previous setup worked for small batches, but scaling broke consistency. Abaka helped us define edge cases, build gold sets, and keep the same interpretation across annotators. The result was cleaner training data and fewer surprises in downstream metrics when we retrained.
We run multimodal programs—text, images, and video—and didn’t want three separate vendors and three separate QA philosophies. Abaka delivered unified schemas and reporting, and the team was responsive when we changed requirements mid-sprint. That flexibility saved us weeks.
The difference wasn’t just throughput; it was governance. Abaka’s secure pipelines, NDAs, and clear access controls made our compliance review straightforward. We could scale labeling with confidence that the data would not be repurposed or shared, and that provenance was documented.
Abaka is built for teams that treat data as a competitive advantage. We never build models that compete with you—your data is exclusively yours and is never repurposed, resold, or shared. Combined with strict NDAs, segregated secure pipelines, and full IP provenance, you get a labeling operation that can scale across modalities while staying auditable. That’s how you maintain consistent datasets that keep training and evaluation trustworthy over time.
Full IP provenance and controlled access help your team avoid downstream legal and compliance surprises. You get clear versioning for specs, outputs, and QA artifacts—so labels remain explainable months later.
Abaka aligns to SOC 2, ISO 27001, GDPR, and CCPA expectations with strict NDAs and segregated pipelines. Security reviews become a workflow step—not a launch blocker.
When tasks demand math, coding, medical terminology, or legal nuance, generic crowd work fails. Abaka’s scholar-network domains and calibrated reviewers help you label edge cases that materially change model behavior.
Abaka Forge is an all-in-one platform for collection, cleaning, annotation, and production workflows. Large-model automation speeds up operations while preserving human control for difficult decisions and QA gates.
Many providers can deliver a one-time dataset; the challenge is keeping quality stable across weekly changes and shifting distributions. Abaka runs with controlled change management, recalibration, and drift monitoring so your training and eval sets remain consistent. You get reporting that ties issues to guideline updates and clear escalation paths for ambiguous samples. The outcome is fewer re-annotation loops, cleaner evaluation signals, and a labeling operation that scales with your roadmap.
Label the Present. Train the Future.
