How much does it cost to hire image annotators through Abaka?
Pricing depends on task complexity (boxes vs. polygons vs. dense captions), QA depth, and volume. As a reference point, Abaka’s real-world rates include Image Editing at $8/hr and Dense Captioning at $6/hr, and some programs are priced per unit (e.g., Road Lane at $3/km for lane labeling). For image annotation projects, we typically scope a pilot first, then propose a blended rate and throughput plan that meets your accuracy and timeline targets. Talk to an Expert to get a quote based on your sample set and ontology.
How fast can you ramp an image annotation team?
Most teams can start with a scoped pilot in the first 1–2 weeks, depending on security setup, guideline readiness, and how quickly you can provide sample data and edge-case decisions. We use the pilot to calibrate reviewers, create gold sets, and validate acceptance criteria before scaling. Once the workflow is stable, we can expand staffing while controlling per-annotator throughput (up to 500 files/day) to maintain quality. Your ramp timeline is driven by required QA depth and how often the ontology changes.
What image annotation types and export formats do you support?
We support common computer-vision annotation tasks including bounding boxes, polygons for instance/semantic segmentation, keypoints/pose, attributes, and dense captioning. Exports can be delivered as COCO JSON, YOLO TXT, Pascal VOC XML, plus mask files (PNG/TIFF) and custom JSON schemas for attributes or hierarchies. If you have a house format, we can map outputs to your schema and provide consistent class maps, naming conventions, and split logic. Abaka Forge keeps the workflow, QA, and exports centralized.
How do you ensure annotation accuracy and consistency over time?
Accuracy comes from process, not promises. We implement clear written guidelines with visual examples, gold sets for calibration, and multi-layer QA with escalation for ambiguous cases. Abaka Forge captures reviewer feedback and decision logs so edge cases stay consistent across annotators and across weeks. We also control throughput (including caps like up to 500 files/day per annotator when needed) to avoid speed-driven errors. Programs can target up to 99% accuracy depending on the task and acceptance criteria you define.
How do you handle security requirements for sensitive images?
Abaka uses strict NDAs, segregated secure pipelines, and controlled access to keep your data contained. We align to SOC 2 and ISO 27001 and support GDPR and CCPA requirements, with audit-friendly governance practices. Work is executed in secure environments with role-based permissions and traceable exports. Importantly, Abaka does not repurpose, resell, or share your data—your datasets remain exclusively yours, and we never build models that compete with you. We can also support additional security controls based on your internal policies.
Can you hire multilingual annotators for global image datasets?
Yes. Abaka supports programs across 50+ countries, which is valuable when your images include localized packaging, signage, or region-specific objects. We can staff annotators who understand local context and language, and apply consistent guidelines using shared taxonomies and reviewer escalation. For workflows that include text-in-image (e.g., signage categories) we can coordinate with text annotation teams to ensure labels align across modalities. Multilingual support is also helpful for dataset metadata normalization and region-specific edge cases.
How is Abaka different from other image annotation vendors?
Three differences matter most: governance, quality systems, and incentives. Abaka is a trustworthy data partner for frontier AI—founded in 2019, self-funded and profitable—with a commitment to never build models that compete with you. Operationally, Abaka Forge consolidates workflow, QA, and exports, while multi-layer QA, gold sets, and escalation keep labels consistent. Finally, we emphasize IP provenance and secure pipelines so your project doesn’t stall in procurement or security review when stakes are high.
What if we need to change the ontology or annotation guidelines mid-project?
Change requests are normal, but unmanaged changes cause expensive relabeling. We use controlled ontology versioning, documented decision rules, and staged rollouts to keep production stable. When a class definition changes, we can run targeted relabeling on impacted slices instead of rewriting the entire dataset. Abaka Forge keeps revision history and reviewer notes, making it easier to align annotators quickly. Weekly reviews help you decide whether to freeze definitions, introduce new attributes, or split classes without disrupting throughput.
Can we start with a pilot before committing to a larger engagement?
Yes—most teams start with a pilot to validate quality, throughput, and format compatibility. In the pilot, we finalize guidelines, build gold sets, and calibrate QA so you can measure outcomes against your acceptance criteria. You’ll receive training-ready exports in your chosen format and a defect report that highlights ambiguous cases and systematic errors. After you approve the workflow, we scale production with the same QA gates and reporting cadence, so performance doesn’t degrade as volume increases.
Who owns the labeled data and derived outputs?
You do. Abaka’s operating principle is that your data is exclusively yours—never repurposed, resold, or shared. We do not train competing models on your datasets. Deliverables—including annotations, guidelines customized for your program, and exports—are produced for your project under strict NDAs. We also maintain full IP provenance and segregated secure pipelines to ensure the data remains controlled and attributable. If you need specific contract language for IP ownership and retention policies, we can support your legal review process.
What tools do your annotators use, and can we integrate with our stack?
Work is run through Abaka Forge—our all-in-one platform for collection, cleaning, annotation, and production workflows across image, video, 3D/4D point cloud, RLHF, and text. Abaka Forge supports model-assisted automation (up to 50x faster on suitable tasks) with human verification for acceptance. We export to common formats (COCO/YOLO/VOC/custom JSON) and can align naming conventions, class maps, and dataset splits to your training pipeline. If you have internal tooling, we can coordinate exports and validation checks accordingly.
What is the minimum project size to hire image annotators with Abaka?
There’s no one-size minimum; it depends on whether you need a small expert pod for high-precision labeling or a larger team for high-volume throughput. Many customers start with a pilot batch sized to validate guidelines and QA—often enough to train an initial model and measure error patterns—then scale once acceptance criteria are proven. If your dataset is small but high-stakes (e.g., rare defects), we can staff domain reviewers and run deeper QA. Talk to an Expert and we’ll recommend a right-sized pilot scope.