Perimattic provides professional AI data annotation and labeling services — creating high-quality, consistent training datasets for computer vision, NLP, and machine learning models with rigorous quality assurance and domain expertise.
Annotation Platforms & Frameworks We Build On — Label Studio, CVAT, Labelbox, Prodigy, OpenCV, TensorFlow, PyTorch
AI data annotation is the process of labeling raw data — text, images, video, audio — with meaningful tags that machine learning models use to learn patterns. High-quality annotations are the foundation of supervised learning: the accuracy of your training data directly determines the accuracy of your AI system. Poor labels produce models that fail in production, regardless of architecture, compute, or model size.
The practical implication is significant. A computer vision model trained on inconsistent bounding boxes will misclassify objects at the boundary of its training distribution. An NLP model trained on ambiguously labeled sentiment data will underperform on the exact edge cases your business encounters most. Annotation quality is not a cost to minimise — it is the primary lever on model performance in the real world.
Perimattic combines domain-expert annotators, structured quality workflows, and active learning methodologies to produce training datasets that consistently achieve 95%+ annotation accuracy. We handle the full annotation lifecycle — schema design, annotator training, iterative labeling, QA, format conversion, and dataset delivery — so your ML team can focus on model development rather than data operations.
Annotation quality
One-size-fits-all approach with anonymous workers
Annotation quality
Domain-expert annotators custom-built for your industry
Scalability
Often hits scaling limits with variable consistency
Scalability
Built to scale from thousands to millions of items
Data security
Basic security measures with opaque data handling
Data security
SOC 2, encryption, NDA, and full audit logging
Customisation
Generic annotation schema with no industry adaptation
Customisation
Custom annotation guidelines built for your exact model requirements
Delivery speed
Queue-based, unpredictable timelines and throughput
Delivery speed
Dedicated team with fixed milestones and committed timelines
The distinction matters most in domains where annotation errors cascade into costly model failures: medical imaging, financial compliance, autonomous vehicles, and legal document processing. These are exactly where Perimattic's domain-expert approach delivers measurable accuracy gains over generic crowdsourcing.
Seven specialist annotation service lines, each built for a specific data type and model training requirement.
Bounding boxes, polygons, semantic segmentation, instance segmentation, keypoints, and 3D cuboids for computer vision model training. We handle still images, video frame sequences, and drone footage across any resolution.
Named entity recognition, sentiment labeling, intent classification, relation extraction, coreference resolution, and document classification for natural language processing models. We support 20+ languages.
Multi-reviewer workflows, inter-annotator agreement scoring, gold standard benchmarking, and automated consistency checks. Every dataset ships with a full quality report and IAA metrics.
Annotators with domain knowledge in healthcare, manufacturing, finance, legal, and automotive — not general-purpose workers. Domain expertise eliminates the interpretation errors that degrade model accuracy in specialized fields.
Transcription, speaker diarisation, sentiment labeling, emotion classification, and phoneme-level annotation for speech recognition, voice assistant, and audio intelligence model training.
Cuboid annotation, semantic segmentation, and object tracking in 3D point cloud data for autonomous vehicle, robotics, and industrial inspection systems. We annotate LiDAR, radar, and sensor fusion datasets.
We help your team use model confidence scores and uncertainty sampling to prioritise which unlabelled examples to annotate next — reducing total annotation cost by 30–60% while improving model accuracy on the examples that matter most.
A proven methodology refined across 30+ projects, ensuring predictable delivery and measurable dataset quality from scoping call to final delivery.
We review your data types, model goals, annotation requirements, and quality standards. This session is free and results in a clear project brief, annotation schema, and timeline estimate. You leave with a precise picture of what your dataset needs and why.
We prepare your data pipeline, clean and de-duplicate source files, define the annotation schema in full detail, and select a representative sample for annotator training and initial calibration rounds.
We train domain-expert annotators on your specific guidelines, run calibration sessions against gold standard examples, and establish inter-annotator agreement baselines before full-scale production begins.
We annotate in structured batches with built-in quality checkpoints. Each batch passes through multi-pass review, consensus labeling for edge cases, and IAA scoring before approval. You review samples at every milestone.
We deliver the completed dataset in your required format — COCO JSON, PASCAL VOC, YOLO, or custom — provide full quality reports, and integrate directly with your training pipeline, data store, or annotation platform.
Post-delivery, we analyse model performance gaps, identify annotation improvements, and support active learning workflows to reduce the cost of your next annotation round by 30–60% while improving dataset coverage.
We review your data types, model goals, annotation requirements, and quality standards. This session is free and results in a clear project brief, annotation schema, and timeline estimate. You leave with a precise picture of what your dataset needs and why.
We prepare your data pipeline, clean and de-duplicate source files, define the annotation schema in full detail, and select a representative sample for annotator training and initial calibration rounds.
We train domain-expert annotators on your specific guidelines, run calibration sessions against gold standard examples, and establish inter-annotator agreement baselines before full-scale production begins.
We annotate in structured batches with built-in quality checkpoints. Each batch passes through multi-pass review, consensus labeling for edge cases, and IAA scoring before approval. You review samples at every milestone.
We deliver the completed dataset in your required format — COCO JSON, PASCAL VOC, YOLO, or custom — provide full quality reports, and integrate directly with your training pipeline, data store, or annotation platform.
Post-delivery, we analyse model performance gaps, identify annotation improvements, and support active learning workflows to reduce the cost of your next annotation round by 30–60% while improving dataset coverage.
Select an industry to see how our domain-expert annotators create training data that meets the specific accuracy and compliance requirements of your sector.
Medical imaging and clinical text annotation demand specialist knowledge. Our annotators include medical imaging experts and clinical documentation specialists who understand the terminology and edge cases that general-purpose annotators miss.
Medical imaging and clinical text annotation demand specialist knowledge. Our annotators include medical imaging experts and clinical documentation specialists who understand the terminology and edge cases that general-purpose annotators miss.
Financial document annotation for fraud detection, compliance, and automated processing requires domain accuracy that general annotators cannot provide — our annotators understand financial terminology, clause structure, and regulatory language.
Visual inspection and defect detection models require large, precisely annotated image datasets captured in real production conditions. Our manufacturing annotators understand component taxonomy, defect severity, and pass/fail criteria.
Autonomous driving and ADAS systems demand the highest annotation precision, with zero tolerance for bounding box drift, missed objects, or label inconsistency. Our AV annotation team follows strict quality protocols across sensor modalities.
Product discovery, visual search, and recommendation engines depend on rich, accurate product and catalogue annotation at scale. Our retail annotation team handles high-volume SKU annotation with attribute-level precision.
Legal NLP models require annotators who understand legal terminology, clause structure, and jurisdiction-specific language — not general-purpose workers. Our legal annotators have backgrounds in law and compliance documentation.
Perimattic's work resulted in stable production systems. The team was helpful, easily accessible, and communicative through email. Their professionalism was impressive.
Quality
4.5
Schedule
5.0
Cost
5.0
Willing to Refer
4.5
DevOps Managed Services · Oct 2023 – Aug 2024
24/7 monitoring and support for production environments plus Linux server administration for a leasing automation company.
The new architecture is scalable and highly efficient, saving a lot of money in fees. Perimattic provides high-quality IT consulting and cloud development work promptly and at great value. The team remains involved from the planning stage to providing support, showing diligence and proactiveness.
Quality
5.0
Schedule
5.0
Cost
4.5
Willing to Refer
5.0
AWS Migration (Legacy → Microservices) · Nov 2018 – Ongoing
Transitioned a travel systems company's legacy server system to an AWS-based microservices architecture with ongoing maintenance.
Four structural advantages that separate production-quality annotation from cheap, inaccurate crowdsourced labels.
We have delivered 30+ annotation projects across healthcare, legal, manufacturing, finance, and automotive. Our annotators understand the terminology, edge cases, and quality standards of your specific field — not just how to draw bounding boxes.
Every annotation engagement includes SOC 2 compliant data handling, encryption in transit and at rest, NDA and data processing agreements before any file transfer, full audit trails, and access-controlled annotation environments.
Fixed-scope projects with clear milestones, per-batch quality reports, and no surprise invoices. You review annotated samples at every checkpoint and always know exactly where the dataset stands against your accuracy targets.
The same team that designs your annotation schema, trains your annotators, and implements your quality workflows also delivers your dataset. You get continuity of context from scoping call to final delivery, with no hand-off loss.
“The accuracy of your training data directly determines the accuracy of your AI system. Perimattic's domain-expert annotation approach delivers the label quality that production models actually need.”
Costs vary by annotation type and complexity. Simple image classification runs $0.02–$0.05 per image, complex polygon and semantic segmentation annotation runs $0.50–$2.00 per image, and text NER annotation runs $0.05–$0.20 per document. Costs also depend on domain expertise requirements, quality tier, and total volume. We provide a detailed cost breakdown after a free scoping call.
We use a multi-layer quality system: multi-pass review by senior annotators, consensus labeling for ambiguous cases, gold standard benchmarks for ongoing calibration, and inter-annotator agreement (IAA) scoring to measure consistency across annotators. Our target is 95%+ accuracy across all project types, and we provide full quality reports with every dataset delivery.
We annotate images (bounding boxes, polygons, semantic segmentation, keypoints, 3D cuboids), text (named entity recognition, sentiment, intent classification, relation extraction), video (frame-by-frame annotation, activity recognition, object tracking), audio (transcription, speaker diarisation, sentiment labeling), and 3D point clouds (LiDAR cuboid annotation for autonomous vehicles and robotics).
Timelines depend on dataset size, annotation complexity, and quality requirements. A focused image classification project with 10,000 images typically takes two to four weeks. A large-scale semantic segmentation or medical imaging project with 100,000+ images may take eight to sixteen weeks. We provide a detailed timeline estimate after reviewing a sample of your dataset.
Crowdsourcing platforms use anonymous workers with variable expertise and no domain knowledge of your industry. Perimattic uses trained annotators with domain expertise in your specific field — healthcare, legal, manufacturing, or finance. We apply structured quality workflows including gold standard testing, consensus review, and IAA scoring that crowdsourcing platforms do not apply consistently.
Yes. We operate under SOC 2 Type II compliance standards, use encryption in transit and at rest, and maintain strict access controls. For healthcare projects we can work under HIPAA-aligned data handling agreements. We sign NDAs and data processing agreements before any data transfer and provide full audit trails for compliance reporting.
We deliver in all standard formats: COCO JSON, PASCAL VOC XML, YOLO TXT, VGG Image Annotator, CSV, and custom formats. Our annotation stack includes Label Studio, CVAT, Prodigy, Labelbox, and Amazon SageMaker Ground Truth. We can annotate directly in your platform if you have an existing toolchain.
Yes. After an initial annotation round, we help your team use model confidence scores to prioritise which unlabelled examples need annotation next — focusing human effort on the examples that will most improve model performance. This typically reduces total annotation cost by 30–60% for large datasets while maintaining or improving model accuracy.
Tell us about your dataset and model goals and we will show you exactly how our domain-expert annotation approach can improve your training data quality, accelerate your labeling timeline, and reduce total annotation cost.