Missed edge cases and unchecked labels cost you AI accuracy. We deliver human-reviewed annotations, verified through multi-level QA, so your AI training data is right the first time.
CMMI
Level 3
HIPAA
Compliant
AWS
Partner Network
ISO Certified
For Data Quality & Security
SOC 2
Type II Compliant
Whether you are training a computer vision (CV) model, fine-tuning an LLM, or building a speech pipeline, our teams label to your ontology and edge-case rules, so the dataset reflects how your model will be used in the real world.
Bounding boxes, polygons, semantic and instance segmentation, keypoints and landmarks for object detection, classification and pose estimation.
Frame-by-frame and interpolated labeling, multi-object tracking, activity and event tagging for surveillance, sports and autonomy use cases.
Named entity recognition, intent and sentiment tagging, document classification, relation extraction and OCR verification.
Transcription, speaker diarization, timestamping, emotion and sound-event labeling for ASR and voice assistant models.
3D cuboids, point-level segmentation and sensor-fusion labeling across LiDAR, radar, and camera streams.
Prompt–response writing, preference ranking for RLHF, SFT dataset curation, red-team test sets, and model output evaluation.
No crowdsourcing; every annotator is trained, tested, and under NDA.
Medical, linguistic, legal, & retail annotators for data that needs subject knowledge.
Native-speaker teams for text and speech annotation in English, Hindi, Spanish, French, German, Arabic, & more.
Tell us about your data and AI model goals. We'll design a custom annotation
workflow and label a sample batch for free.
Bounding boxes
Polygons
Semantic segmentation
Instance segmentation
Keypoints & skeletons
Polylines & lanes
3D cuboids
Object tracking
Entity tagging
Sentiment & intent
Transcription
Preference ranking (RLHF)Every label passes annotator, peer reviewer, and dedicated QA checks.
Seeded test items track each annotator's precision over time.
IoU for boxes and masks, Cohen's kappa for classification, and word error rate for transcription.
Edge cases found in production are fed back into the guide and retraining.
Platforms and model-assisted pre-labeling speed up annotation, but they can't decide what a correct label is for your use case. Here is what a tool gives you on its own, and what our data annotation team adds on top.
| Feature | Annotation Tool / Auto-Labeling Alone | With our Data Annotation Experts |
|---|---|---|
| Labeling Speed | Fast pre-labels, but every error passes straight into your training set. | We use pre-labeling for speed, then a human checks and corrects every pre-label. |
| Edge Cases | Occluded objects, rare classes, and ambiguous text get a confident guess. | Annotators flag ambiguous items; a lead makes a ruling and adds it to the guide. |
| Guidelines & Taxonomy | Your team writes, maintains, and enforces them. | We co-write the class list, visual examples, and edge-case rules with you. |
| Quality Control | Basic validation rules; spot-checking falls on your ML engineers. | Three-layer review, hidden gold tests, and IoU / kappa reports on every batch. |
| Domain Judgment | Generic. | Annotators are trained on your domain before they touch production data. |
| Workforce & Scaling | You hire, train and manage labelers, and absorb turnover. | A trained team that ramps up or down with your data volume. |
| Accountability | You pay for a license; output accuracy is your problem. | Accuracy targets written into the SLA, with rework before delivery. |
We work inside CVAT, Label Studio, Labelbox, or your in-house tool, and
deliver in COCO, Pascal VOC, YOLO, JSON, CSV, or XML.
Solved an acute problem
A commercial lender's underwriters spent 20–30 hours per loan file on manual data entry. We built a custom-trained document processing and workflow platform on AWS.
35%
higher throughput
99.5%
extraction accuracy
Rebuilt a broken core system
A bath & lighting brand in 55+ countries had site search converting just 10–15% of searchers. We trained a transformer ranking model on their own catalog and query logs.
80%
more accurate search
72%
higher conversion
Found the bigger problem
An eCommerce retailer brought us in for nopCommerce upkeep. The real bottleneck was inventory and pricing scattered across five channels — so we built the fix.
60%
increase in sales
~90%
tasks automated
Provided a dedicated engineering pod
A core retail POS platform had to outgrow three technology eras without losing what made it work. The same embedded pod carried it from CGI to PHP to AWS over 15 years, so no knowledge was ever lost in handover.
15+ years
Zero re-tendering and handovers
Our data annotation services define quality benchmarks upfront, including inter-annotator agreement thresholds, gold standard validation, and multi-round QA workflows. We deliver each dataset with validation reports to ensure consistency across all labeled data.
Our image annotation services prioritize schema design, edge-case sampling, and validation metrics alongside typical labeling quality. We support bounding boxes, segmentation, keypoints, and classification with strict quality controls.
We use inter-annotator agreement metrics such as Cohen’s Kappa and Fleiss’ Kappa, along with gold dataset validation and per-class agreement analysis. We measure quality continuously, not just at the final stage.
Our data annotation services are tool and platform-agnostic. We can work within your existing annotation infrastructure or deploy our own tools. Our quality control and validation workflows remain consistent across platforms.
With TechnoScore, you can engage pre-vetted, domain-trained data annotation experts starting from $18/hr.
We design deliberate sampling strategies to identify and include rare but critical edge cases. This ensures models perform reliably not just on common scenarios but also in high-impact, real-world conditions.
Yes. We start with a pilot batch where we annotate a representative dataset, measure quality metrics, and share validation reports. This allows you to evaluate our data annotation services before scaling.
We follow strict schema versioning and change control processes. We track, document, and, if needed, apply updates retroactively to maintain dataset consistency.
We support industries such as healthcare, automotive, agritech, retail/eCommerce, finance, and customer support, where high-quality labeled data is essential for model performance.
Project initiation within 48 hours
Round-the-clock support
Strict NDAs for data security
Share your project requirements.
We use pre-labeling for speed, then a human checks and corrects every pre-label.
