Abaka pairs vertically specialized annotators with Abaka Forge to help your team label text, images, video, and 3D data faster—without trading off accuracy, security, or control.
When your datasets lag, models stall. A single 2-week slip in labeling can push back training runs, delay product milestones, and waste GPU spend while teams wait on ground truth. Quality issues compound: a 3–5% label error rate can cascade into missed edge cases, brittle evaluation, and weeks of rework across prompt sets, taxonomies, and guidelines. Meanwhile, unmanaged vendors create security risk—files copied into uncontrolled tools, unclear reviewer provenance, and inconsistent QA that turns every iteration into a reset.
Abaka helps you evaluate AI data annotation companies through outcomes, not claims: measurable accuracy targets, transparent throughput, and compliance you can audit. With Abaka Forge, you get an end-to-end pipeline—secure intake, guideline design, multi-layer QA, and continuous calibration—across text, RLHF, image, video, and 3D/4D. Your team keeps ownership and direction; Abaka supplies the people, process, and platform to deliver dependable labels on schedule, even as specs evolve and volumes spike.
Most providers look strong on a small pilot, then quality drifts when you scale. As throughput ramps, reviewers interpret guidelines differently, edge cases go unlogged, and acceptance criteria become subjective. Even a 1-in-20 disagreement rate can create thousands of conflicting labels per week on high-volume jobs. Abaka mitigates this with multi-layer QA, calibrated gold sets, and ongoing adjudication, so you maintain consistent 99% accuracy targets across batches, teams, and time.
Internal teams and boutique vendors hit hard ceilings: limited staffing, uneven coverage across time zones, and slow onboarding for specialized tasks. If one annotator can safely process up to 500 files/day, a project needing 50,000 files/week quickly becomes a staffing and management problem—not a labeling problem. Abaka provides elastic capacity through a 1M+ annotator network across 50+ countries, with fast ramp plans that preserve QA as volume increases.
Security and compliance are where many annotation engagements break: unclear access controls, weak NDAs, and datasets moving through personal drives or unapproved tools. That can trigger procurement delays of 4–8 weeks and force rework when audits fail. Abaka is built for enterprise expectations—SOC 2, ISO 27001, GDPR, and CCPA—with segregated secure pipelines and full IP provenance (0% copyright risk on collected data), so you can move from kickoff to production without compliance deadlocks.
Turn ambiguous requirements into a measurable labeling spec. We define label taxonomies, edge-case rules, and pass/fail thresholds, then translate them into reviewer playbooks inside Abaka Forge. Your team gets clear coverage targets (including long-tail scenarios), structured issue logs, and change control—so you can compare AI data annotation companies by the only metric that matters: stable quality in production.
We deliver instruction datasets, classification, extraction, and high-precision QA sets for LLM training and product features. Abaka supports complex domains via scholar-network expertise (coding, mathematics, medicine, law, and more) and multilingual coverage across 50+ countries. Outputs are delivered in clean JSONL/CSV with consistent schemas, versioning, and audit trails through Abaka Forge.
Build reliable preference data: pairwise rankings, rubric scoring, safety checks, and instruction-following evaluations. We combine human evaluation with large-model automation in Abaka Forge to accelerate review, reduce variance, and keep alignment goals explicit. Ideal for frontier labs and enterprise GenAI teams improving helpfulness, factuality, and policy adherence without leaking sensitive prompts or outputs.
From bounding boxes and polygons to attribute tagging and dense captioning, Abaka supports computer vision pipelines for retail, healthcare imaging workflows, and autonomous systems. We handle image editing tasks when data needs cleanup before labeling, and we run multi-pass QA to control drift. Deliverables include COCO-style JSON, YOLO TXT, and task-specific schemas aligned to your training stack.
Support high-value video tasks like object tracking, event segmentation, and video spatial reasoning data for multimodal models. We design frame sampling strategies, temporal label rules, and adjudication steps for rare events. Outputs can include per-frame JSON, clip-level CSV, and structured narration sets. Teams use Abaka Forge to review disagreements, measure inter-annotator alignment, and iterate quickly.
Abaka labels 3D/4D point clouds for perception stacks—3D boxes, segmentation, motion tags, and scene understanding. We support robotics and automotive programs where accuracy is non-negotiable and edge cases are the rule, not the exception. Abaka Forge manages large files, secure access, and QA sampling so your team can scale point cloud work without drowning in manual review.
For autonomy teams, we deliver lane geometry, boundaries, and drivable area labels with consistent guidelines and fast iteration loops. Pricing can be aligned to lane work measured per kilometer (e.g., road lane tasks priced per km), and deliverables are structured for downstream model training and evaluation. This is built for Tier-1 workflows where coverage, traceability, and reviewer calibration matter.
Abaka is a trustworthy data partner for frontier AI: SOC 2 and ISO 27001 controls, strict NDAs, segregated secure pipelines, and GDPR/CCPA alignment. We provide full IP provenance and 0% copyright risk on collected data. Importantly, we never build models that compete with you—your data remains exclusively yours, never repurposed, resold, or shared.
Ramp quickly without compromising QA. Abaka combines a large global workforce with Abaka Forge automation to shorten cycles from spec to first delivery—often in 2–3 weeks—then keeps weekly releases predictable as volumes grow.
Avoid the hidden costs of hiring, training, and rework. With clear per-hour or per-unit pricing and built-in QA, you reduce wasted engineering time spent relabeling, rebuilding guidelines, and chasing inconsistencies across vendors.
Minimize security, IP, and vendor lock-in risk. Abaka runs SOC 2 and ISO 27001-aligned workflows, strict NDAs, and secure pipelines—plus a clear posture that your data is never repurposed or shared.
Scale capacity up or down as product priorities shift. With 1M+ annotators across 50+ countries, you can expand multilingual coverage, add specialized reviewers, or surge throughput without rebuilding your pipeline.
Match tasks to qualified reviewers. Abaka supports scholar-grade domains (coding, mathematics, medicine, law, and more) and complex multimodal work like video spatial reasoning and 3D perception—where generic labelers fail.
Move beyond “labeling” into a learning loop. We help your team design better rubrics, build eval sets, and instrument QA metrics so each iteration improves the next—especially for RLHF, safety, and model evaluation workloads.
Support ADAS and autonomy pipelines with lane, drivable area, and perception labels across image, video, and point clouds. Abaka helps you standardize edge-case handling, reduce relabel cycles, and maintain traceability for safety-focused development.
Build instruction data, RLHF preference sets, safety evaluations, and domain-specific QA (coding, math, law, medicine). We help foundation model teams reduce variance, improve rubric consistency, and ship weekly dataset iterations with strong governance.
Label 3D/4D scenes, actions, and interaction states for robot learning. Abaka also supports agent and RL environment needs through structured data and evaluation loops, helping robotics teams align perception labels with real task success metrics.
Create high-precision text and imaging datasets for clinical workflows, medical assistants, and decision-support tools. Abaka emphasizes careful guideline design, multi-layer QA, and secure access patterns to support sensitive enterprise environments.
Power product search, catalog enrichment, and visual understanding with attribute tagging, image classification, and dense captioning. Abaka helps maintain consistent taxonomies across brands and geographies while improving recall for long-tail SKUs.
Annotate documents, transactions, and conversational data for extraction, classification, and risk workflows. Abaka’s secure pipelines and controlled access make it practical to label regulated data while keeping reviewer actions auditable and consistent.
Label satellite and aerial imagery, map features, and change detection datasets. Abaka supports polygon-heavy work and large-file pipelines, delivering consistent schemas for downstream training and evaluation in analytics and monitoring systems.
Build robust datasets for detection, triage, and multimodal analysis with strict operational security. Abaka supports controlled workforce access, segregated pipelines, and auditing—so your team can scale labeling without compromising governance.
Train vision systems for inspection, yield estimation, and equipment monitoring using image and video labeling plus specialized taxonomies. Abaka helps you standardize labels across seasons, sites, and devices to reduce drift and improve reliability.
We align on your objective (training vs evaluation), define classes and edge cases, choose sampling strategy, and set acceptance criteria. Abaka establishes secure access, NDA coverage, and workflow controls, then configures the project in Abaka Forge.
We run a pilot to validate guidelines, measure disagreement, and refine rubrics. You review examples, we adjudicate edge cases, and we lock the first stable version of the spec. This is where many AI data annotation companies fail—Abaka makes it repeatable.
We ramp throughput while maintaining consistency: primary annotation, secondary review, and targeted adjudication. Abaka Forge supports automation-assisted checks and structured issue tracking so guideline updates propagate without breaking earlier batches.
As your model and product evolve, we handle taxonomy expansions, new edge cases, and format changes. Abaka keeps versioned outputs and clear change control so you can compare runs and avoid mixing incompatible label definitions.
You receive weekly deliveries with quality reporting, audit trails, and prioritized error themes. We continuously calibrate reviewers using gold sets and feedback loops, ensuring outputs remain stable as volume, language coverage, and modalities expand.
Abaka supports end-to-end annotation and evaluation across core modalities—from LLM data to multimodal perception—using Abaka Forge for secure workflows, calibration, and consistent output schemas your training stack can ingest.
| Modality | Annotation Types | Tools | Output Formats |
|---|---|---|---|
| Text | Classification, entity extraction, instruction tuning data, multilingual QA, reasoning & rubric-based grading | Abaka Forge | JSONL, CSV, TSV, Parquet |
| LLM RLHF | Pairwise preference ranking, rubric scoring, safety & bias audits, tool/function-calling evals, model-as-judge + human review | Abaka Forge | JSONL, CSV, conversation JSON, eval scorecards |
| Image | Bounding boxes, polygons, instance segmentation, keypoints, dense captioning | Abaka Forge | COCO JSON, YOLO TXT, Pascal VOC XML, PNG masks |
| Video | Object tracking, temporal segments, action labels, event tags, video spatial reasoning narration | Abaka Forge | Per-frame JSON, clip CSV, COCO-style video JSON, timecoded annotations |
| 3D/4D Point Cloud | 3D bounding boxes, point-level segmentation, scene graph tags, motion/trajectory labels, object attributes | Abaka Forge | JSON, PCD-linked labels, binary masks, scene annotation bundles |
| LiDAR + Camera fusion | Cross-sensor alignment checks, fused 2D/3D boxes, lane/drivable area labeling, occlusion attributes, calibration QA | Abaka Forge | JSON, synchronized frame bundles, sensor metadata manifests, training-ready exports |
| Audio | Transcription, speaker diarization, intent labeling, wake-word tagging, multilingual TTS validation | Abaka Forge | TextGrid, JSON, CSV, SRT/VTT |
The customer needed to benchmark instruction-following and safety behaviors across multiple model snapshots, but their existing vendor produced inconsistent ratings and weak auditability. Disagreements were hard to diagnose, and prompt formatting drift made comparisons unreliable. They also needed reviewers with deeper domain grounding for coding and math tasks, plus the ability to scale evaluation volume without compromising security controls or exposing sensitive internal prompts.
Abaka designed a rubric-based evaluation workflow with calibrated examples, adjudication rules, and structured issue logging inside Abaka Forge. We staffed domain-aligned reviewers from scholar networks (coding and mathematics) and ran multi-layer QA, using automation-assisted checks to flag format violations and missing rationale fields. The customer received versioned outputs, traceable reviewer actions, and weekly reporting that surfaced systematic failure modes instead of isolated disagreements.
Within 3 weeks, the team had a stable evaluation pipeline that supported fast iteration across model snapshots, with clearer pass/fail criteria and fewer ambiguous ratings. Weekly releases improved comparability and reduced time lost to reformatting and relabeling. The program scaled to higher volume while maintaining consistent calibration and audit trails, enabling the customer to make decisions faster and ship improved safety and instruction-following behaviors—at 99% accuracy targets and with 2–3 week turnaround on new eval packs.
We had tried two annotation vendors before and kept getting inconsistent edge-case handling once the project scaled. Abaka helped us lock a taxonomy, run calibration, and ship weekly batches we could actually trust. The audit trail and structured issue log made reviews fast and objective.
The biggest difference was operational discipline. Access controls, versioned outputs, and multi-layer QA were built in from day one. Our team stopped spending cycles relabeling and instead focused on training and evaluation, because the dataset stayed stable across iterations.
We needed domain reviewers for coding and math evaluation, plus the ability to scale volume quickly. Abaka delivered qualified reviewers and consistent rubrics, and the workflow made disagreements easy to adjudicate. The result was faster decisions and less noise in our benchmarks.
Our internal labeling team was capped, and every change request created a mess across spreadsheets and tools. Abaka Forge centralized the workflow and made updates controlled and trackable. We finally had predictable weekly releases without sacrificing quality.
Abaka is self-funded, profitable, and founded in 2019—so you get long-term reliability without acquisition pressure. We never build models that compete with you, and your data is exclusively yours: never repurposed, resold, or shared. With SOC 2, ISO 27001, GDPR, and CCPA-aligned workflows plus segregated secure pipelines, you can run sensitive annotation and evaluation work with confidence and auditable governance.
We combine calibrated guidelines, gold sets, adjudication, and reviewer coaching to keep label quality stable as volume scales. Your team gets fewer relabel cycles and cleaner training signals.
Access a 1M+ annotator network across 50+ countries for multilingual and multi-time-zone coverage. Abaka standardizes rubrics and reporting so results don’t vary by team or region.
Manage secure intake, annotation, QA, and exports in one platform. Abaka Forge supports large-model automation for faster checks and consistent formatting, reducing manual review load for your engineers.
From coding and mathematics to medicine and law, Abaka matches work to qualified reviewers. This is essential for RLHF, evaluations, and high-stakes datasets where shallow understanding breaks quality.
We deliver clean, versioned datasets in standard formats (JSONL/CSV/COCO-style schemas) with clear change logs. That means you can compare training runs, reproduce benchmarks, and switch tasks or modalities without rebuilding your pipeline from scratch.
Label the Present. Train the Future. Talk to an Expert to benchmark AI data annotation companies, scope a pilot, and get a delivery plan with secure workflows and measurable quality targets.
